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1.  On estimation of vaccine efficacy using validation samples with selection bias 
Biostatistics (Oxford, England)  2006;7(4):615-629.
SUMMARY
Using validation sets for outcomes can greatly improve the estimation of vaccine efficacy (VE) in the field (Halloran and Longini, 2001; Halloran and others, 2003). Most statistical methods for using validation sets rely on the assumption that outcomes on those with no cultures are missing at random (MAR). However, often the validation sets will not be chosen at random. For example, confirmational cultures are often done on people with influenza-like illness as part of routine influenza surveillance. VE estimates based on such non-MAR validation sets could be biased. Here we propose frequentist and Bayesian approaches for estimating VE in the presence of validation bias. Our work builds on the ideas of Rotnitzky and others (1998, 2001), Scharfstein and others (1999, 2003), and Robins and others (2000). Our methods require expert opinion about the nature of the validation selection bias. In a re-analysis of an influenza vaccine study, we found, using the beliefs of a flu expert, that within any plausible range of selection bias the VE estimate based on the validation sets is much higher than the point estimate using just the non-specific case definition. Our approach is generally applicable to studies with missing binary outcomes with categorical covariates.
doi:10.1093/biostatistics/kxj031
PMCID: PMC2766283  PMID: 16556610
Bayesian; Expert opinion; Identifiability; Influenza; Missing data; Selection model; Vaccine efficacy
2.  Statistical analysis and handling of missing data in cluster randomized trials: a systematic review 
Trials  2016;17:72.
Background
Cluster randomized trials (CRTs) randomize participants in groups, rather than as individuals and are key tools used to assess interventions in health research where treatment contamination is likely or if individual randomization is not feasible. Two potential major pitfalls exist regarding CRTs, namely handling missing data and not accounting for clustering in the primary analysis. The aim of this review was to evaluate approaches for handling missing data and statistical analysis with respect to the primary outcome in CRTs.
Methods
We systematically searched for CRTs published between August 2013 and July 2014 using PubMed, Web of Science, and PsycINFO. For each trial, two independent reviewers assessed the extent of the missing data and method(s) used for handling missing data in the primary and sensitivity analyses. We evaluated the primary analysis and determined whether it was at the cluster or individual level.
Results
Of the 86 included CRTs, 80 (93 %) trials reported some missing outcome data. Of those reporting missing data, the median percent of individuals with a missing outcome was 19 % (range 0.5 to 90 %). The most common way to handle missing data in the primary analysis was complete case analysis (44, 55 %), whereas 18 (22 %) used mixed models, six (8 %) used single imputation, four (5 %) used unweighted generalized estimating equations, and two (2 %) used multiple imputation. Fourteen (16 %) trials reported a sensitivity analysis for missing data, but most assumed the same missing data mechanism as in the primary analysis. Overall, 67 (78 %) trials accounted for clustering in the primary analysis.
Conclusions
High rates of missing outcome data are present in the majority of CRTs, yet handling missing data in practice remains suboptimal. Researchers and applied statisticians should carry out appropriate missing data methods, which are valid under plausible assumptions in order to increase statistical power in trials and reduce the possibility of bias. Sensitivity analysis should be performed, with weakened assumptions regarding the missing data mechanism to explore the robustness of results reported in the primary analysis.
Electronic supplementary material
The online version of this article (doi:10.1186/s13063-016-1201-z) contains supplementary material, which is available to authorized users.
doi:10.1186/s13063-016-1201-z
PMCID: PMC4748550  PMID: 26862034
Cluster randomized trials; Missing data; Dropout; Sensitivity analysis
3.  Addressing Dichotomous Data for Participants Excluded from Trial Analysis: A Guide for Systematic Reviewers 
PLoS ONE  2013;8(2):e57132.
Introduction
Systematic reviewer authors intending to include all randomized participants in their meta-analyses need to make assumptions about the outcomes of participants with missing data.
Objective
The objective of this paper is to provide systematic reviewer authors with a relatively simple guidance for addressing dichotomous data for participants excluded from analyses of randomized trials.
Methods
This guide is based on a review of the Cochrane handbook and published methodological research. The guide deals with participants excluded from the analysis who were considered ‘non-adherent to the protocol’ but for whom data are available, and participants with missing data.
Results
Systematic reviewer authors should include data from ‘non-adherent’ participants excluded from the primary study authors' analysis but for whom data are available. For missing, unavailable participant data, authors may conduct a complete case analysis (excluding those with missing data) as the primary analysis. Alternatively, they may conduct a primary analysis that makes plausible assumptions about the outcomes of participants with missing data. When the primary analysis suggests important benefit, sensitivity meta-analyses using relatively extreme assumptions that may vary in plausibility can inform the extent to which risk of bias impacts the confidence in the results of the primary analysis. The more plausible assumptions draw on the outcome event rates within the trial or in all trials included in the meta-analysis. The proposed guide does not take into account the uncertainty associated with assumed events.
Conclusions
This guide proposes methods for handling participants excluded from analyses of randomized trials. These methods can help in establishing the extent to which risk of bias impacts meta-analysis results.
doi:10.1371/journal.pone.0057132
PMCID: PMC3581575  PMID: 23451162
4.  Collaborative Automation Reliably Remediating Erroneous Conclusion Threats (CARRECT) 
Objective
The objective of the CARRECT software is to make cutting edge statistical methods for reducing bias in epidemiological studies easy to use and useful for both novice and expert users.
Introduction
Analyses produced by epidemiologists and public health practitioners are susceptible to bias from a number of sources including missing data, confounding variables, and statistical model selection. It often requires a great deal of expertise to understand and apply the multitude of tests, corrections, and selection rules, and these tasks can be time-consuming and burdensome. To address this challenge, Aptima began development of CARRECT, the Collaborative Automation Reliably Remediating Erroneous Conclusion Threats system. When complete, CARRECT will provide an expert system that can be embedded in an analyst’s workflow. CARRECT will support statistical bias reduction and improved analyses and decision making by engaging the user in a collaborative process in which the technology is transparent to the analyst.
Methods
Older approaches to imputing missing data, including mean imputation and single imputation regression methods, have steadily given way to a class of methods known as “multiple imputation” (hereafter “MI”; Rubin 1987). Rather than making the restrictive assumption that the data are missing completely at random (MCAR), MI typically assumes the data are missing at random (MAR).
There are two key innovations behind MI. First, the observed values can be useful in predicting the missing cells, and thus specifying a joint distribution of the data is the first step in implementing the models. Second, single imputation methods will likely fail not only because of the inherent uncertainty in the missing values but also because of the estimation uncertainty associated with generating the parameters in the imputation procedure itself. By contrast, drawing the missing values multiple times, thereby generating m complete datasets along with the estimated parameters of the model properly accounts for both types of uncertainty (Rubin 1987; King et al. 2001). As a result, MI will lead to valid standard errors and confidence intervals along with unbiased point estimates.
In order to compute the joint distribution, CARRECT uses a bootstrapping-based algorithm that gives essentially the same answers as the standard Bayesian Markov Chain Monte Carlo (MCMC) or Expectation Maximization (EM) approaches, is usually considerably faster than existing approaches and can handle many more variables.
Results
Tests were conducted on one of the proposed methods with an epidemiological dataset from the Integrated Health Interview Series (IHIS) producing verifiably unbiased results despite high missingness rates. In addition, mockups (Figure 1) were created of an intuitive data wizard that guides the user through the analysis processes by analyzing key features of a given dataset. The mockups also show prompts for the user to provide additional substantive knowledge to improve the handling of imperfect datasets, as well as the selection of the most appropriate algorithms and models.
Conclusions
Our approach and program were designed to make bias mitigation much more accessible to much more than only the statistical elite. We hope that it will have a wide impact on reducing bias in epidemiological studies and provide more accurate information to policymakers.
PMCID: PMC3692841
Bias reduction; Missing data; Statistical model selection
5.  A novel application of the Intent to Attend assessment to reduce bias due to missing data in a randomized controlled clinical trial 
Clinical trials (London, England)  2014;11(4):494-502.
Background
Missing data are unavoidable in most randomized controlled clinical trials, especially when measurements are taken repeatedly. If strong assumptions about the missing data are not accurate, crude statistical analyses are biased and can lead to false inferences. Furthermore, if we fail to measure all predictors of missing data, we may not be able to model the missing data process sufficiently. In longitudinal randomized trials, measuring a patient's intent to attend future study visits may help to address both of these problems. Leon et al. developed and included the Intent to Attend assessment in the Lithium Treatment—Moderate dose Use Study (LiTMUS), aiming to remove bias due to missing data from the primary study hypothesis [1].
Purpose
The purpose of this study is to assess the performance of the Intent to Attend assessment with regard to its use in a sensitivity analysis of missing data.
Methods
We fit marginal models to assess whether a patient's self-rated intent predicted actual study adherence. We applied inverse probability of attrition weighting (IPAW) coupled with patient intent to assess whether there existed treatment group differences in response over time. We compared the IPAW results to those obtained using other methods.
Results
Patient-rated intent predicted missed study visits, even when adjusting for other predictors of missing data. On average, the hazard of retention increased by 19% for every one-point increase in intent. We also found that more severe mania, male gender, and a previously missed visit predicted subsequent absence. Although we found no difference in response between the randomized treatment groups, IPAW increased the estimated group difference over time.
Limitations
LiTMUS was designed to limit missed study visits, which may have attenuated the effects of adjusting for missing data. Additionally, IPAW can be less efficient and less powerful than maximum likelihood or Bayesian estimators, given that the parametric model is well-specified.
Conclusions
In LiTMUS, the Intent to Attend assessment predicted missed study visits. This item was incorporated into our IPAW models and helped reduce bias due to informative missing data. This analysis should both encourage and facilitate future use of the Intent to Attend assessment along with IPAW to address missing data in a randomized trial.
doi:10.1177/1740774514531096
PMCID: PMC4247354  PMID: 24872362
intent to attend; inverse probability weighting; attrition; intermittent missing data; bipolar disorder; LiTMUS
6.  Accommodating Missingness When Assessing Surrogacy Via Principal Stratification 
Clinical trials (London, England)  2013;10(3):363-377.
Background
When an outcome of interest in a clinical trial is late-occurring or difficult to obtain, surrogate markers can extract information about the effect of the treatment on the outcome of interest. Understanding associations between the causal effect of treatment on the outcome and the causal effect of treatment on the surrogate is critical to understanding the value of a surrogate from a clinical perspective.
Purpose
Traditional regression approaches to determine the proportion of the treatment effect explained by surrogate markers suffer from several shortcomings: they can be unstable, and can lie outside of the 0–1 range. Further, they do not account for the fact that surrogate measures are obtained post-randomization, and thus the surrogate-outcome relationship may be subject to unmeasured confounding. Methods to avoid these problem are of key importance.
Methods
Frangakis C, Rubin DM. Principal stratification in causal inference. Biometrics 2002; 58:21–9 suggested assessing the causal effect of treatment within pre-randomization “principal strata” defined by the counterfactual joint distribution of the surrogate marker under the different treatment arms, with the proportion of the overall outcome causal effect attributable to subjects for whom the treatment affects the proposed surrogate as the key measure of interest. Li Y, Taylor JMG, Elliott MR. Bayesian approach to surrogacy assessment using principal stratification in clinical trials. Biometrics 2010; 66:523–31 developed this “principal surrogacy” approach for dichotomous markers and outcomes, utilizing Bayesian methods that accommodated non-identifiability in the model parameters. Because the surrogate marker is typically observed early, outcome data is often missing. Here we extend Li, Taylor, and Elliott to accommodate missing data in the observable final outcome under ignorable and non-ignorable settings. We also allow for the possibility that missingness has a counterfactual component, a feature that previous literature has not addressed.
Results
We apply the proposed methods to a trial of glaucoma control comparing surgery versus medication, where intraocular pressure (IOP) control at 12 months is a surrogate for IOP control at 96 months. We also conduct a series of simulations to consider the impacts of non-ignorability, as well as sensitivity to priors and the ability of the Decision Information Criterion to choose the correct model when parameters are not fully identified.
Limitations
Because model parameters cannot be fully identified from data, informative priors can introduce non-trivial bias in moderate sample size settings, while more non-informative priors can yield wide credible intervals.
Conclusions
Assessing the linkage between causal effects of treatment on a surrogate marker and causal effects of a treatment on an outcome is important to understanding the value of a marker. These causal effects are not fully identifiable: hence we explore the sensitivity and identifiability aspects of these models and show that relatively weak assumptions can still yield meaningful results.
doi:10.1177/1740774513479522
PMCID: PMC4096330  PMID: 23553326
Causal Inference; Surrogate Marker; Bayesian Analysis; dentifiability; Non-response; Counterfactual
7.  An exploration of the missing data mechanism in an Internet based smoking cessation trial 
Background
Missing outcome data are very common in smoking cessation trials. It is often assumed that all such missing data are from participants who have been unsuccessful in giving up smoking (“missing=smoking”). Here we use data from a recent Internet based smoking cessation trial in order to investigate which of a set of a priori chosen baseline variables are predictive of missingness, and the evidence for and against the “missing=smoking” assumption.
Methods
We use a selection model, which models the probability that the outcome is observed given the outcome and other variables. The selection model includes a parameter for which zero indicates that the data are Missing at Random (MAR) and large values indicate “missing=smoking”. We examine the evidence for the predictive power of baseline variables in the context of a sensitivity analysis. We use data on the number and type of attempts made to obtain outcome data in order to estimate the association between smoking status and the missing data indicator.
Results
We apply our methods to the iQuit smoking cessation trial data. From the sensitivity analysis, we obtain strong evidence that older participants are more likely to provide outcome data. The model for the number and type of attempts to obtain outcome data confirms that age is a good predictor of missing data. There is weak evidence from this model that participants who have successfully given up smoking are more likely to provide outcome data but this evidence does not support the “missing=smoking” assumption. The probability that participants with missing outcome data are not smoking at the end of the trial is estimated to be between 0.14 and 0.19.
Conclusions
Those conducting smoking cessation trials, and wishing to perform an analysis that assumes the data are MAR, should collect and incorporate baseline variables into their models that are thought to be good predictors of missing data in order to make this assumption more plausible. However they should also consider the possibility of Missing Not at Random (MNAR) models that make or allow for less extreme assumptions than “missing=smoking”.
doi:10.1186/1471-2288-12-157
PMCID: PMC3507670  PMID: 23067272
8.  How much can we learn about missing data?: an exploration of a clinical trial in psychiatry 
When a randomized controlled trial has missing outcome data, any analysis is based on untestable assumptions, e.g. that the data are missing at random, or less commonly on other assumptions about the missing data mechanism. Given such assumptions, there is an extensive literature on suitable methods of analysis. However, little is known about what assumptions are appropriate. We use two sources of ancillary data to explore the missing data mechanism in a trial of adherence therapy in patients with schizophrenia: carer-reported (proxy) outcomes and the number of contact attempts. This requires additional assumptions to be made whose plausibility we discuss. Proxy outcomes are found to be unhelpful in this trial because they are insufficiently associated with patient outcome and because the ancillary assumptions are implausible. The number of attempts required to achieve a follow-up interview is helpful and suggests that these data are unlikely to depart far from being missing at random. We also perform sensitivity analyses to departures from missingness at random, based on the investigators’ prior beliefs elicited at the start of the trial. Wider use of techniques such as these will help to inform the choice of suitable assumptions for the analysis of randomized controlled trials.
doi:10.1111/j.1467-985X.2009.00627.x
PMCID: PMC2916212  PMID: 20711246
Informatively missing; Missing data; Missingness not at random; Prior elicitation; Proxy data; Repeated attempts; Sensitivity analysis
9.  A Bayesian model for longitudinal count data with non-ignorable dropout 
Summary
Asthma is an important chronic disease of childhood. An intervention programme for managing asthma was designed on principles of self-regulation and was evaluated by a randomized longitudinal study.The study focused on several outcomes, and, typically, missing data remained a pervasive problem. We develop a pattern–mixture model to evaluate the outcome of intervention on the number of hospitalizations with non-ignorable dropouts. Pattern–mixture models are not generally identifiable as no data may be available to estimate a number of model parameters. Sensitivity analyses are performed by imposing structures on the unidentified parameters.We propose a parameterization which permits sensitivity analyses on clustered longitudinal count data that have missing values due to non-ignorable missing data mechanisms. This parameterization is expressed as ratios between event rates across missing data patterns and the observed data pattern and thus measures departures from an ignorable missing data mechanism. Sensitivity analyses are performed within a Bayesian framework by averaging over different prior distributions on the event ratios. This model has the advantage of providing an intuitive and flexible framework for incorporating the uncertainty of the missing data mechanism in the final analysis.
doi:10.1111/j.1467-9876.2008.00628.x
PMCID: PMC2975948  PMID: 21072316
Gibbs sampling; Longitudinal data; Non-linear mixed effects models; Poisson outcomes; Randomized trials; Transition Markov models
10.  Evidence for the Selective Reporting of Analyses and Discrepancies in Clinical Trials: A Systematic Review of Cohort Studies of Clinical Trials 
PLoS Medicine  2014;11(6):e1001666.
In a systematic review of cohort studies, Kerry Dwan and colleagues examine the evidence for selective reporting and discrepancies in analyses between journal publications and other documents for clinical trials.
Please see later in the article for the Editors' Summary
Background
Most publications about selective reporting in clinical trials have focussed on outcomes. However, selective reporting of analyses for a given outcome may also affect the validity of findings. If analyses are selected on the basis of the results, reporting bias may occur. The aims of this study were to review and summarise the evidence from empirical cohort studies that assessed discrepant or selective reporting of analyses in randomised controlled trials (RCTs).
Methods and Findings
A systematic review was conducted and included cohort studies that assessed any aspect of the reporting of analyses of RCTs by comparing different trial documents, e.g., protocol compared to trial report, or different sections within a trial publication. The Cochrane Methodology Register, Medline (Ovid), PsycInfo (Ovid), and PubMed were searched on 5 February 2014. Two authors independently selected studies, performed data extraction, and assessed the methodological quality of the eligible studies. Twenty-two studies (containing 3,140 RCTs) published between 2000 and 2013 were included. Twenty-two studies reported on discrepancies between information given in different sources. Discrepancies were found in statistical analyses (eight studies), composite outcomes (one study), the handling of missing data (three studies), unadjusted versus adjusted analyses (three studies), handling of continuous data (three studies), and subgroup analyses (12 studies). Discrepancy rates varied, ranging from 7% (3/42) to 88% (7/8) in statistical analyses, 46% (36/79) to 82% (23/28) in adjusted versus unadjusted analyses, and 61% (11/18) to 100% (25/25) in subgroup analyses. This review is limited in that none of the included studies investigated the evidence for bias resulting from selective reporting of analyses. It was not possible to combine studies to provide overall summary estimates, and so the results of studies are discussed narratively.
Conclusions
Discrepancies in analyses between publications and other study documentation were common, but reasons for these discrepancies were not discussed in the trial reports. To ensure transparency, protocols and statistical analysis plans need to be published, and investigators should adhere to these or explain discrepancies.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
In the past, clinicians relied on their own experience when choosing the best treatment for their patients. Nowadays, they turn to evidence-based medicine—the systematic review and appraisal of trials, studies that investigate the benefits and harms of medical treatments in patients. However, evidence-based medicine can guide clinicians only if all the results from clinical trials are published in an unbiased and timely manner. Unfortunately, the results of trials in which a new drug performs better than existing drugs are more likely to be published than those in which the new drug performs badly or has unwanted side effects (publication bias). Moreover, trial outcomes that support the use of a new treatment are more likely to be published than those that do not support its use (outcome reporting bias). Recent initiatives—such as making registration of clinical trials in a trial registry (for example, ClinicalTrials.gov) a prerequisite for publication in medical journals—aim to prevent these biases, which pose a threat to informed medical decision-making.
Why Was This Study Done?
Selective reporting of analyses of outcomes may also affect the validity of clinical trial findings. Sometimes, for example, a trial publication will include a per protocol analysis (which considers only the outcomes of patients who received their assigned treatment) rather than a pre-planned intention-to-treat analysis (which considers the outcomes of all the patients regardless of whether they received their assigned treatment). If the decision to publish the per protocol analysis is based on the results of this analysis being more favorable than those of the intention-to-treat analysis (which more closely resembles “real” life), then “analysis reporting bias” has occurred. In this systematic review, the researchers investigate the selective reporting of analyses and discrepancies in randomized controlled trials (RCTs) by reviewing published studies that assessed selective reporting of analyses in groups (cohorts) of RCTs and discrepancies in analyses of RCTs between different sources (for example, between the protocol in a trial registry and the journal publication) or different sections of a source. A systematic review uses predefined criteria to identify all the research on a given topic.
What Did the Researchers Do and Find?
The researchers identified 22 cohort studies (containing 3,140 RCTs) that were eligible for inclusion in their systematic review. All of these studies reported on discrepancies between the information provided by the RCTs in different places, but none investigated the evidence for analysis reporting bias. Several of the cohort studies reported, for example, that there were discrepancies in the statistical analyses included in the different documents associated with the RCTs included in their analysis. Other types of discrepancies reported by the cohort studies included discrepancies in the reporting of composite outcomes (an outcome in which multiple end points are combined) and in the reporting of subgroup analyses (investigations of outcomes in subgroups of patients that should be predefined in the trial protocol to avoid bias). Discrepancy rates varied among the RCTs according to the types of analyses and cohort studies considered. Thus, whereas in one cohort study discrepancies were present in the statistical test used for the analysis of the primary outcome in only 7% of the included studies, they were present in the subgroup analyses of all the included studies.
What Do These Findings Mean?
These findings indicate that discrepancies in analyses between publications and other study documents such as protocols in trial registries are common. The reasons for these discrepancies in analyses were not discussed in trial reports but may be the result of reporting bias, errors, or legitimate departures from a pre-specified protocol. For example, a statistical analysis that is not specified in the trial protocol may sometimes appear in a publication because the journal requested its inclusion as a condition of publication. The researchers suggest that it may be impossible for systematic reviewers to distinguish between these possibilities simply by looking at the source documentation. Instead, they suggest, it may be necessary for reviewers to contact the trial authors. However, to make selective reporting of analyses more easily detectable, they suggest that protocols and analysis plans should be published and that investigators should be required to stick to these plans or explain any discrepancies when they publish their trial results. Together with other initiatives, this approach should help improve the quality of evidence-based medicine and, as a result, the treatment of patients.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001666.
Wikipedia has pages on evidence-based medicine, on systematic reviews, and on publication bias (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
ClinicalTrials.gov provides information about the US National Institutes of Health clinical trial registry, including background information about clinical trials
The Cochrane Collaboration is a global independent network of health practitioners, researchers, patient advocates, and others that aims to promote evidence-informed health decision-making by producing high-quality, relevant, accessible systematic reviews and other synthesized research evidence; the Cochrane Handbook for Systematic Reviews of Interventions describes the preparation of systematic reviews in detail
PLOS Medicine recently launched a Reporting Guidelines Collection, an open-access collection of reporting guidelines, commentary, and related research on guidelines from across PLOS journals that aims to help advance the efficiency, effectiveness, and equitability of the dissemination of biomedical information
doi:10.1371/journal.pmed.1001666
PMCID: PMC4068996  PMID: 24959719
11.  The effect of tertiary surveys on missed injuries in trauma: a systematic review 
Background
Trauma tertiary surveys (TTS) are advocated to reduce the rate of missed injuries in hospitalized trauma patients. Moreover, the missed injury rate can be a quality indicator of trauma care performance. Current variation of the definition of missed injury restricts interpretation of the effect of the TTS and limits the use of missed injury for benchmarking. Only a few studies have specifically assessed the effect of the TTS on missed injury. We aimed to systematically appraise these studies using outcomes of two common definitions of missed injury rates and long-term health outcomes.
Methods
A systematic review was performed. An electronic search (without language or publication restrictions) of the Cochrane Library, Medline and Ovid was used to identify studies assessing TTS with short-term measures of missed injuries and long-term health outcomes. ‘Missed injury’ was defined as either: Type I) any injury missed at primary and secondary survey and detected by the TTS; or Type II) any injury missed at primary and secondary survey and missed by the TTS, detected during hospital stay. Two authors independently selected studies. Risk of bias for observational studies was assessed using the Newcastle-Ottawa scale.
Results
Ten observational studies met our inclusion criteria. None was randomized and none reported long-term health outcomes. Their risk of bias varied considerably. Nine studies assessed Type I missed injury and found an overall rate of 4.3%. A single study reported Type II missed injury with a rate of 1.5%. Three studies reported outcome data on missed injuries for both control and intervention cohorts, with two reporting an increase in Type I missed injuries (3% vs. 7%, P<0.01), and one a decrease in Type II missed injuries (2.4% vs. 1.5%, P=0.01).
Conclusions
Overall Type I and Type II missed injury rates were 4.3% and 1.5%. Routine TTS performance increased Type I and reduced Type II missed injuries. However, evidence is sub-optimal: few observational studies, non-uniform outcome definitions and moderate risk of bias. Future studies should address these issues to allow for the use of missed injury rate as a quality indicator for trauma care performance and benchmarking.
doi:10.1186/1757-7241-20-77
PMCID: PMC3546883  PMID: 23190504
Tertiary survey; Missed injury; Multiple trauma; Patient safety; Quality of care
12.  Bayesian Sensitivity Analysis of Statistical Models with Missing Data 
Statistica Sinica  2014;24(2):871-896.
Methods for handling missing data depend strongly on the mechanism that generated the missing values, such as missing completely at random (MCAR) or missing at random (MAR), as well as other distributional and modeling assumptions at various stages. It is well known that the resulting estimates and tests may be sensitive to these assumptions as well as to outlying observations. In this paper, we introduce various perturbations to modeling assumptions and individual observations, and then develop a formal sensitivity analysis to assess these perturbations in the Bayesian analysis of statistical models with missing data. We develop a geometric framework, called the Bayesian perturbation manifold, to characterize the intrinsic structure of these perturbations. We propose several intrinsic influence measures to perform sensitivity analysis and quantify the effect of various perturbations to statistical models. We use the proposed sensitivity analysis procedure to systematically investigate the tenability of the non-ignorable missing at random (NMAR) assumption. Simulation studies are conducted to evaluate our methods, and a dataset is analyzed to illustrate the use of our diagnostic measures.
doi:10.5705/ss.2012.126
PMCID: PMC3991016  PMID: 24753718
Influence measure; Missing data mechanism; Perturbation manifold; Sensitivity analysis
13.  Strategies to improve retention in randomised trials 
Background
Loss to follow-up from randomised trials can introduce bias and reduce study power, affecting the generalisability, validity and reliability of results. Many strategies are used to reduce loss to follow-up and improve retention but few have been formally evaluated.
Objectives
To quantify the effect of strategies to improve retention on the proportion of participants retained in randomised trials and to investigate if the effect varied by trial strategy and trial setting.
Search methods
We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, PreMEDLINE, EMBASE, PsycINFO, DARE, CINAHL, Campbell Collaboration's Social, Psychological, Educational and Criminological Trials Register, and ERIC. We handsearched conference proceedings and publication reference lists for eligible retention trials. We also surveyed all UK Clinical Trials Units to identify further studies.
Selection criteria
We included eligible retention trials of randomised or quasi-randomised evaluations of strategies to increase retention that were embedded in 'host' randomised trials from all disease areas and healthcare settings. We excluded studies aiming to increase treatment compliance.
Data collection and analysis
We contacted authors to supplement or confirm data that we had extracted. For retention trials, we recorded data on the method of randomisation, type of strategy evaluated, comparator, primary outcome, planned sample size, numbers randomised and numbers retained. We used risk ratios (RR) to evaluate the effectiveness of the addition of strategies to improve retention. We assessed heterogeneity between trials using the Chi2 and I2 statistics. For main trials that hosted retention trials, we extracted data on disease area, intervention, population, healthcare setting, sequence generation and allocation concealment.
Main results
We identified 38 eligible retention trials. Included trials evaluated six broad types of strategies to improve retention. These were incentives, communication strategies, new questionnaire format, participant case management, behavioural and methodological interventions. For 34 of the included trials, retention was response to postal and electronic questionnaires with or without medical test kits. For four trials, retention was the number of participants remaining in the trial. Included trials were conducted across a spectrum of disease areas, countries, healthcare and community settings. Strategies that improved trial retention were addition of monetary incentives compared with no incentive for return of trial-related postal questionnaires (RR 1.18; 95% CI 1.09 to 1.28, P value < 0.0001), addition of an offer of monetary incentive compared with no offer for return of electronic questionnaires (RR 1.25; 95% CI 1.14 to 1.38, P value < 0.00001) and an offer of a GBP20 voucher compared with GBP10 for return of postal questionnaires and biomedical test kits (RR 1.12; 95% CI 1.04 to 1.22, P value < 0.005). The evidence that shorter questionnaires are better than longer questionnaires was unclear (RR 1.04; 95% CI 1.00 to 1.08, P value = 0.07) and the evidence for questionnaires relevant to the disease/condition was also unclear (RR 1.07; 95% CI 1.01 to 1.14). Although each was based on the results of a single trial, recorded delivery of questionnaires seemed to be more effective than telephone reminders (RR 2.08; 95% CI 1.11 to 3.87, P value = 0.02) and a 'package' of postal communication strategies with reminder letters appeared to be better than standard procedures (RR 1.43; 95% CI 1.22 to 1.67, P value < 0.0001). An open trial design also appeared more effective than a blind trial design for return of questionnaires in one fracture prevention trial (RR 1.37; 95% CI 1.16 to 1.63, P value = 0.0003).
There was no good evidence that the addition of a non-monetary incentive, an offer of a non-monetary incentive, 'enhanced' letters, letters delivered by priority post, additional reminders, or questionnaire question order either increased or decreased trial questionnaire response/retention. There was also no evidence that a telephone survey was either more or less effective than a monetary incentive and a questionnaire. As our analyses are based on single trials, the effect on questionnaire response of using offers of charity donations, sending reminders to trial sites and when a questionnaire is sent, may need further evaluation. Case management and behavioural strategies used for trial retention may also warrant further evaluation.
Authors' conclusions
Most of the retention trials that we identified evaluated questionnaire response. There were few evaluations of ways to improve participants returning to trial sites for trial follow-up. Monetary incentives and offers of monetary incentives increased postal and electronic questionnaire response. Some other strategies evaluated in single trials looked promising but need further evaluation. Application of the findings of this review would depend on trial setting, population, disease area, data collection and follow-up procedures.
PLAIN LANGUAGE SUMMARY
Methods that might help to keep people in randomised trials
Background
Most trials follow people up to collect data through personal contact after they have been recruited. Some trials get data from other sources, such as routine collected data or disease registers. There are many ways to collect data from people in trials, and these include using letters, the internet, telephone calls, text messaging, face-to-face meetings or the return of medical test kits. Most trials have missing data, for example, because people are too busy to reply, are unable to attend a clinic, have moved or no longer want to participate. Sometimes data has not been recorded at study sites, or are not sent to the trial co-ordinating centre. Researchers call this 'loss to follow-up', 'drop out' or 'attrition' and it can affect the trial's results. For example, if the people with the most or least severe symptoms do not return questionnaires or attend a follow-up visit, this will bias the findings of the trial. Many methods are used by researchers to keep people in trials. These encourage people to send back data by questionnaire, return to a clinic or hospital for trial-related tests, or be seen by a health or community care worker.
Study characteristics
This review identified methods that encouraged people to stay in trials. We searched scientific databases for randomised studies (where people are allocated to one of two or more possible treatments in a random manner) or quasi-randomised studies (where allocation is not really random, e.g. based on date of birth, order in which they attended clinic) that compared methods of increasing retention in trials. We included trials of participants from any age, gender, ethnic, cultural, language and geographic groups.
Key results
The methods that appeared to work were offering or giving a small amount of money for return of a completed questionnaire and enclosing a small amount of money with a questionnaire with the promise of a further small amount of money for return of a filled in questionnaire. The effect of other ways to keep people in trials is still not clear and more research is needed to see if these really do work. Such methods are shorter questionnaires, sending questionnaires by recorded delivery, using a trial design where people know which treatment they will receive, sending specially designed letters with a reply self addressed stamped envelope followed by a number of reminders, offering a donation to charity or entry into a prize draw, sending a reminder to the study site about participants to follow-up, sending questionnaires close to the time the patient was last followed-up, managing peoples' follow-up, conducting follow-up by telephone and changing the order of questionnaire questions.
Quality of evidence
The methods that we identified were tested in trials run in many different disease areas and settings and, in some cases, were tested in only one trial. Therefore, more studies are needed to help decide whether our findings could be used in other research fields.
doi:10.1002/14651858.MR000032.pub2
PMCID: PMC4470347  PMID: 24297482
14.  A Bayesian Ensemble Approach for Epidemiological Projections 
PLoS Computational Biology  2015;11(4):e1004187.
Mathematical models are powerful tools for epidemiology and can be used to compare control actions. However, different models and model parameterizations may provide different prediction of outcomes. In other fields of research, ensemble modeling has been used to combine multiple projections. We explore the possibility of applying such methods to epidemiology by adapting Bayesian techniques developed for climate forecasting. We exemplify the implementation with single model ensembles based on different parameterizations of the Warwick model run for the 2001 United Kingdom foot and mouth disease outbreak and compare the efficacy of different control actions. This allows us to investigate the effect that discrepancy among projections based on different modeling assumptions has on the ensemble prediction. A sensitivity analysis showed that the choice of prior can have a pronounced effect on the posterior estimates of quantities of interest, in particular for ensembles with large discrepancy among projections. However, by using a hierarchical extension of the method we show that prior sensitivity can be circumvented. We further extend the method to include a priori beliefs about different modeling assumptions and demonstrate that the effect of this can have different consequences depending on the discrepancy among projections. We propose that the method is a promising analytical tool for ensemble modeling of disease outbreaks.
Author Summary
Policy decisions in response to emergent disease outbreaks use simulation models to inform the efficiency of different control actions. However, different projections may be made, depending on the choice of models and parameterizations. Ensemble modeling offers the ability to combine multiple projections and has been used successfully within other fields of research. A central issue in ensemble modeling is how to weight the projections when they are combined. For this purpose, we here adapt and extend a weighting method used in climate forecasting such that it can be used for epidemiological considerations. We investigate how the method performs by applying it to ensembles of projections for the UK foot and mouth disease outbreak in UK, 2001. We conclude that the method is a promising analytical tool for ensemble modeling of disease outbreaks.
doi:10.1371/journal.pcbi.1004187
PMCID: PMC4415763  PMID: 25927892
15.  How Recent History Affects Perception: The Normative Approach and Its Heuristic Approximation 
PLoS Computational Biology  2012;8(10):e1002731.
There is accumulating evidence that prior knowledge about expectations plays an important role in perception. The Bayesian framework is the standard computational approach to explain how prior knowledge about the distribution of expected stimuli is incorporated with noisy observations in order to improve performance. However, it is unclear what information about the prior distribution is acquired by the perceptual system over short periods of time and how this information is utilized in the process of perceptual decision making. Here we address this question using a simple two-tone discrimination task. We find that the “contraction bias”, in which small magnitudes are overestimated and large magnitudes are underestimated, dominates the pattern of responses of human participants. This contraction bias is consistent with the Bayesian hypothesis in which the true prior information is available to the decision-maker. However, a trial-by-trial analysis of the pattern of responses reveals that the contribution of most recent trials to performance is overweighted compared with the predictions of a standard Bayesian model. Moreover, we study participants' performance in a-typical distributions of stimuli and demonstrate substantial deviations from the ideal Bayesian detector, suggesting that the brain utilizes a heuristic approximation of the Bayesian inference. We propose a biologically plausible model, in which decision in the two-tone discrimination task is based on a comparison between the second tone and an exponentially-decaying average of the first tone and past tones. We show that this model accounts for both the contraction bias and the deviations from the ideal Bayesian detector hypothesis. These findings demonstrate the power of Bayesian-like heuristics in the brain, as well as their limitations in their failure to fully adapt to novel environments.
Author Summary
In this paper we study how history affects perception using an auditory delayed comparison task, in which human participants repeatedly compare the frequencies of two, temporally-separated pure tones. We demonstrate that the history of the experiment has a substantial effect on participants' performance: when both tones are high relative to past stimuli, people tend to report that the 2nd tone was higher, and when they are relatively low, they tend to report that the 1st tone was higher. Interestingly, only the most recent trials bias performance, which can be interpreted as if the participants assume that the statistics of stimuli in the experiment is highly volatile. Moreover, this bias persists even in settings, in which it is detrimental to performance. These results demonstrate the abilities, as well as limitations, of the cognitive system when incorporating expectations in perception.
doi:10.1371/journal.pcbi.1002731
PMCID: PMC3486920  PMID: 23133343
16.  Seasonal Influenza Vaccination for Children in Thailand: A Cost-Effectiveness Analysis 
PLoS Medicine  2015;12(5):e1001829.
Background
Seasonal influenza is a major cause of mortality worldwide. Routine immunization of children has the potential to reduce this mortality through both direct and indirect protection, but has not been adopted by any low- or middle-income countries. We developed a framework to evaluate the cost-effectiveness of influenza vaccination policies in developing countries and used it to consider annual vaccination of school- and preschool-aged children with either trivalent inactivated influenza vaccine (TIV) or trivalent live-attenuated influenza vaccine (LAIV) in Thailand. We also compared these approaches with a policy of expanding TIV coverage in the elderly.
Methods and Findings
We developed an age-structured model to evaluate the cost-effectiveness of eight vaccination policies parameterized using country-level data from Thailand. For policies using LAIV, we considered five different age groups of children to vaccinate. We adopted a Bayesian evidence-synthesis framework, expressing uncertainty in parameters through probability distributions derived by fitting the model to prospectively collected laboratory-confirmed influenza data from 2005-2009, by meta-analysis of clinical trial data, and by using prior probability distributions derived from literature review and elicitation of expert opinion. We performed sensitivity analyses using alternative assumptions about prior immunity, contact patterns between age groups, the proportion of infections that are symptomatic, cost per unit vaccine, and vaccine effectiveness. Vaccination of children with LAIV was found to be highly cost-effective, with incremental cost-effectiveness ratios between about 2,000 and 5,000 international dollars per disability-adjusted life year averted, and was consistently preferred to TIV-based policies. These findings were robust to extensive sensitivity analyses. The optimal age group to vaccinate with LAIV, however, was sensitive both to the willingness to pay for health benefits and to assumptions about contact patterns between age groups.
Conclusions
Vaccinating school-aged children with LAIV is likely to be cost-effective in Thailand in the short term, though the long-term consequences of such a policy cannot be reliably predicted given current knowledge of influenza epidemiology and immunology. Our work provides a coherent framework that can be used for similar analyses in other low- and middle-income countries.
Ben Cooper and colleagues use an age-structured model to estimate optimal cost-effectiveness of flu vaccination among Thai children aged 2 to 17.
Editors' Summary
Background
Every year, millions of people catch influenza, a viral disease of the airways. Most infected individuals recover quickly, but elderly people, the very young, and chronically ill individuals are at high risk of developing serious complications such as pneumonia; seasonal influenza kills about half a million people annually. Small but frequent changes in the influenza virus mean that an immune response produced one year by exposure to the virus provides only partial protection against influenza the next year. Annual immunization with a vaccine that contains killed or live-attenuated (weakened) influenza viruses of the major circulating strains can reduce a person’s chance of catching influenza. Consequently, many countries run seasonal influenza vaccination programs that target elderly people and other people at high risk of influenza complications, and people who care for these individuals.
Why Was This Study Done?
As well as reducing the vaccinated person’s risk of infection, influenza vaccination protects unvaccinated members of the population by reducing the chances of influenza spreading. Because children make a disproportionately large contribution to the transmission of influenza, vaccination of children might therefore provide greater benefits to the whole population than vaccination of elderly people, particularly when vaccination uptake among the elderly is low. Thus, many high-income countries now recommend annual influenza vaccination of children with a trivalent live-attenuated influenza vaccine (LAIV; a trivalent vaccine contains three viruses), which is sprayed into the nose. However, to date no low- or middle-income countries have evaluated this policy. Here, the researchers develop a mathematical model (framework) to evaluate the cost-effectiveness of annual vaccination of children with LAIV or trivalent inactivated influenza vaccine (TIV) in Thailand. A cost-effectiveness analysis evaluates whether a medical intervention is good value for money by comparing the health outcomes and costs associated with the introduction of the intervention with the health outcomes and costs of the existing standard of care. Thailand, a middle-income country, offers everyone over 65 years old free seasonal influenza vaccination with TIV, but vaccine coverage in this age group is low (10%).
What Did the Researchers Do and Find?
The researchers developed a modeling framework that contained six connected components including a transmission model that incorporated infectious contacts within and between different age groups, a health outcome model that calculated the disability-adjusted life years (DALYs, a measure of the overall disease burden) averted by specific vaccination policies, and a cost model that calculated the costs to the population of each policy. They used this framework and data from Thailand to calculate the cost-effectiveness of six childhood vaccination policies in Thailand (one with TIV and five with LAIV that targeted children of different ages) against a baseline policy of 10% TIV coverage in the elderly; they also investigated the cost-effectiveness of increasing vaccination in the elderly to 66%. All seven vaccination policies tested reduced influenza cases and deaths compared to the baseline policy, but the LAIV-based polices were consistently better than the TIV-based policies; the smallest reductions were seen when TIV coverage in elderly people was increased to 66%. All seven policies were highly cost-effective according to the World Health Organization’s threshold for cost-effectiveness. That is, the cost per DALY averted by each policy compared to the baseline policy (the incremental cost-effectiveness ratio) was less than Thailand’s gross domestic product per capita (the total economic output of a country divided by the number of people in the country).
What Do These Findings Mean?
These findings suggest that seasonal influenza vaccination of children with LAIV is likely to represent good value for money in Thailand and, potentially, in other middle- and low-income countries in the short term. The long-term consequences of annual influenza vaccination of children in Thailand cannot be reliably predicted, however, because of limitations in our current understanding of influenza immunity in populations. Moreover, the accuracy of these findings is limited by the assumptions built into the modeling framework, including the vaccine costs and efficacy that were used to run the model, which were estimated from limited data. Importantly, however, these findings support proposals for large-scale community-based controlled trials of policies to vaccinate children against influenza in low- and middle-income countries. Indeed, based on these findings, Thailand is planning to evaluate school-based seasonal influenza vaccination in a few provinces in 2016 before considering a nationwide program of seasonal influenza vaccination of children.
Additional Information
This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001829.
The UK National Health Service Choices website provides information for patients about seasonal influenza, about influenza vaccination, and about influenza vaccination in children
The World Health Organization provides information on seasonal influenza (in several languages) and on influenza vaccines
The US Centers for Disease Control and Prevention also provides information for patients and health professionals on all aspects of seasonal influenza, including information about vaccination, about children, influenza, and vaccination, and about herd immunity; its website contains a short video about personal experiences of influenza
Flu.gov, a US government website, provides access to information on seasonal influenza and vaccination
MedlinePlus has links to further information about influenza and about vaccination (in English and Spanish)
The Thai National Influenza Center monitors influenza activity throughout Thailand
doi:10.1371/journal.pmed.1001829
PMCID: PMC4444096  PMID: 26011712
17.  Meta-analysis of test accuracy studies: an exploratory method for investigating the impact of missing thresholds 
Systematic Reviews  2015;4:12.
Background
Primary studies examining the accuracy of a continuous test evaluate its sensitivity and specificity at one or more thresholds. Meta-analysts then usually perform a separate meta-analysis for each threshold. However, the number of studies available for each threshold is often very different, as primary studies are inconsistent in the thresholds reported. Furthermore, of concern is selective reporting bias, because primary studies may be less likely to report a threshold when it gives low sensitivity and/or specificity estimates. This may lead to biased meta-analysis results. We developed an exploratory method to examine the potential impact of missing thresholds on conclusions from a test accuracy meta-analysis.
Methods
Our method identifies studies that contain missing thresholds bounded between a pair of higher and lower thresholds for which results are available. The bounded missing threshold results (two-by-two tables) are then imputed, by assuming a linear relationship between threshold value and each of logit-sensitivity and logit-specificity. The imputed results are then added to the meta-analysis, to ascertain if original conclusions are robust. The method is evaluated through simulation, and application made to 13 studies evaluating protein:creatinine ratio (PCR) for detecting proteinuria in pregnancy with 23 different thresholds, ranging from one to seven per study.
Results
The simulation shows the imputation method leads to meta-analysis estimates with smaller mean-square error. In the PCR application, it provides 50 additional results for meta-analysis and their inclusion produces lower test accuracy results than originally identified. For example, at a PCR threshold of 0.16, the summary specificity is 0.80 when using the original data, but 0.66 when also including the imputed data. At a PCR threshold of 0.25, the summary sensitivity is reduced from 0.95 to 0.85 when additionally including the imputed data.
Conclusions
The imputation method is a practical tool for researchers (often non-statisticians) to explore the potential impact of missing threshold results on their meta-analysis conclusions. Software is available to implement the method. In the PCR example, it revealed threshold results are vulnerable to the missing data, and so stimulates the need for advanced statistical models or, preferably, individual patient data from primary studies.
Electronic supplementary material
The online version of this article (doi:10.1186/2046-4053-4-12) contains supplementary material, which is available to authorized users.
doi:10.1186/2046-4053-4-12
PMCID: PMC4417327  PMID: 25652323
Meta-analysis; Diagnostic test; Multiple thresholds; Imputation; Missing data; Sensitivity analysis
18.  A Flexible Bayesian Approach to Monotone Missing Data in Longitudinal Studies with Nonignorable Missingness with Application to an Acute Schizophrenia Clinical Trial 
We develop a Bayesian nonparametric model for a longitudinal response in the presence of nonignorable missing data. Our general approach is to first specify a working model that flexibly models the missingness and full outcome processes jointly. We specify a Dirichlet process mixture of missing at random (MAR) models as a prior on the joint distribution of the working model. This aspect of the model governs the fit of the observed data by modeling the observed data distribution as the marginalization over the missing data in the working model. We then separately specify the conditional distribution of the missing data given the observed data and dropout. This approach allows us to identify the distribution of the missing data using identifying restrictions as a starting point. We propose a framework for introducing sensitivity parameters, allowing us to vary the untestable assumptions about the missing data mechanism smoothly. Informative priors on the space of missing data assumptions can be specified to combine inferences under many different assumptions into a final inference and accurately characterize uncertainty. These methods are motivated by, and applied to, data from a clinical trial assessing the efficacy of a new treatment for acute Schizophrenia.
doi:10.1080/01621459.2014.969424
PMCID: PMC4517693  PMID: 26236060
Dirichlet process mixture; Identifiability; Identifying restrictions; Sensitivity analysis
19.  Imputation methods for missing outcome data in meta-analysis of clinical trials 
Background
Missing outcome data from randomized trials lead to greater uncertainty and possible bias in estimating the effect of an experimental treatment. An intention-to-treat analysis should take account of all randomized participants even if they have missing observations.
Purpose
To review and develop imputation methods for missing outcome data in meta-analysis of clinical trials with binary outcomes.
Methods
We review some common strategies, such as simple imputation of positive or negative outcomes, and develop a general approach involving ‘informative missingness odds ratios’ (IMORs). We describe several choices for weighting studies in the meta-analysis, and illustrate methods using a meta-analysis of trials of haloperidol for schizophrenia.
Results
IMORs describe the relationship between the unknown risk among missing participants and the known risk among observed participants. They are allowed to differ between treatment groups and across trials. Application of IMORs and other methods to the haloperidol trials reveals the overall conclusion to be robust to different assumptions about the missing data.
Limitations
The methods are based on summary data from each trial (number of observed positive outcomes, number of observed negative outcomes and number of missing outcomes) for each intervention group. This limits the options for analysis, and greater flexibility would be available with individual participant data.
Conclusions
We propose that available reasons for missingness be used to determine appropriate IMORs. We also recommend a strategy for undertaking sensitivity analyses, in which the IMORs are varied over plausible ranges.
doi:10.1177/1740774508091600
PMCID: PMC2602608  PMID: 18559412
20.  Factors Associated with Findings of Published Trials of Drug–Drug Comparisons: Why Some Statins Appear More Efficacious than Others 
PLoS Medicine  2007;4(6):e184.
Background
Published pharmaceutical industry–sponsored trials are more likely than non-industry-sponsored trials to report results and conclusions that favor drug over placebo. Little is known about potential biases in drug–drug comparisons. This study examined associations between research funding source, study design characteristics aimed at reducing bias, and other factors that potentially influence results and conclusions in randomized controlled trials (RCTs) of statin–drug comparisons.
Methods and Findings
This is a cross-sectional study of 192 published RCTs comparing a statin drug to another statin drug or non-statin drug. Data on concealment of allocation, selection bias, blinding, sample size, disclosed funding source, financial ties of authors, results for primary outcomes, and author conclusions were extracted by two coders (weighted kappa 0.80 to 0.97). Univariate and multivariate logistic regression identified associations between independent variables and favorable results and conclusions. Of the RCTs, 50% (95/192) were funded by industry, and 37% (70/192) did not disclose any funding source. Looking at the totality of available evidence, we found that almost all studies (98%, 189/192) used only surrogate outcome measures. Moreover, study design weaknesses common to published statin–drug comparisons included inadequate blinding, lack of concealment of allocation, poor follow-up, and lack of intention-to-treat analyses. In multivariate analysis of the full sample, trials with adequate blinding were less likely to report results favoring the test drug, and sample size was associated with favorable conclusions when controlling for other factors. In multivariate analysis of industry-funded RCTs, funding from the test drug company was associated with results (odds ratio = 20.16 [95% confidence interval 4.37–92.98], p < 0.001) and conclusions (odds ratio = 34.55 [95% confidence interval 7.09–168.4], p < 0.001) that favor the test drug when controlling for other factors. Studies with adequate blinding were less likely to report statistically significant results favoring the test drug.
Conclusions
RCTs of head-to-head comparisons of statins with other drugs are more likely to report results and conclusions favoring the sponsor's product compared to the comparator drug. This bias in drug–drug comparison trials should be considered when making decisions regarding drug choice.
Lisa Bero and colleagues found published trials comparing one statin with another were more likely to report results and conclusions favoring the sponsor's product than the comparison drug.
Editors' Summary
Background.
Randomized controlled trials are generally considered to be the most reliable type of experimental study for evaluating the effectiveness of different treatments. Randomization involves the assignment of participants in the trial to different treatment groups by the play of chance. Properly done, this procedure means that the different groups are comparable at outset, reducing the chance that outside factors could be responsible for treatment effects seen in the trial. When done properly, randomization also ensures that the clinicians recruiting participants into the trial cannot know the treatment group to which a patient will end up being assigned. However, despite these advantages, a large number of factors can still result in bias creeping in. Bias comes about when the findings of research appear to differ in some systematic way from the true result. Other research studies have suggested that funding is a source of bias; studies sponsored by drug companies seem to more often favor the sponsor's drug than trials not sponsored by drug companies
Why Was This Study Done?
The researchers wanted to more precisely understand the impact of different possible sources of bias in the findings of randomized controlled trials. In particular, they wanted to study the outcomes of “head-to-head” drug comparison studies for one particular class of drugs, the statins. Drugs in this class are commonly prescribed to reduce the levels of cholesterol in blood amongst people who are at risk of heart and other types of disease. This drug class is a good example for studying the role of bias in drug–drug comparison trials, because these trials are extensively used in decision making by health-policy makers.
What Did the Researchers Do and Find?
This research study was based on searching PubMed, a biomedical literature database, with the aim of finding all randomized controlled trials of statins carried out between January 1999 and May 2005 (reference lists also were searched). Only trials which compared one statin to another statin or one statin to another type of drug were included. The researchers extracted the following information from each article: the study's source of funding, aspects of study design, the overall results, and the authors' conclusions. The results were categorized to show whether the findings were favorable to the test drug (the newer statin), inconclusive, or not favorable to the test drug. Aspects of each study's design were also categorized in relation to various features, such as how well the randomization was done (in particular, the degree to which the processes used would have prevented physicians from knowing which treatment a patient was likely to receive on enrollment); whether all participants enrolled in the trial were eventually analyzed; and whether investigators or participants knew what treatment an individual was receiving.
One hundred and ninety-two trials were included in this study, and of these, 95 declared drug company funding; 23 declared government or other nonprofit funding while 74 did not declare funding or were not funded. Trials that were properly blinded (where participants and investigators did not know what treatment an individual received) were less likely to have conclusions favoring the test drug. However, large trials were more likely to favor the test drug than smaller trials. When looking specifically at the trials funded by drug companies, the researchers found various factors that predicted whether a result or conclusion favored the test drug. These included the impact of the journal publishing the results; the size of the trial; and whether funding came from the maker of the test drug. However, properly blinded trials were less likely to produce results favoring the test drug. Even once all other factors were accounted for, the funding source for the study was still linked with results and conclusions that favored the maker of the test drug.
What Do These Findings Mean?
This study shows that the type of sponsorship available for randomized controlled trials of statins was strongly linked to the results and conclusions of those studies, even when other factors were taken into account. However, it is not clear from this study why sponsorship has such a strong link to the overall findings. There are many possible reasons why this might be. Some people have suggested that drug companies may deliberately choose lower dosages for the comparison drug when they carry out “head-to-head” trials; this tactic is likely to result in the company's product doing better in the trial. Others have suggested that trials which produce unfavorable results are not published, or that unfavorable outcomes are suppressed. Whatever the reasons for these findings, the implications are important, and suggest that the evidence base relating to statins may be substantially biased.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040184.
The James Lind Library has been created to help people understand fair tests of treatments in health care by illustrating how fair tests have developed over the centuries
The International Committee of Medical Journal Editors has provided guidance regarding sponsorship, authorship, and accountability
The CONSORT statement is a research tool that provides an evidence-based approach for reporting the results of randomized controlled trials
Good Publication Practice guidelines provide standards for responsible publication of research sponsored by pharmaceutical companies
Information from Wikipedia on Statins. Wikipedia is an internet encyclopedia anyone can edit
doi:10.1371/journal.pmed.0040184
PMCID: PMC1885451  PMID: 17550302
21.  A Bayesian missing data framework for generalized multiple outcome mixed treatment comparisons 
Research synthesis methods  2015;7(1):6-22.
Summary
Bayesian statistical approaches to mixed treatment comparisons (MTCs) are becoming more popular due to their flexibility and interpretability. Many randomized clinical trials report multiple outcomes with possible inherent correlations. Moreover, MTC data are typically sparse (though richer than standard meta-analysis, comparing only two treatments) and researchers often choose study arms based upon which treatments emerge as superior in previous trials. In this paper, we summarize existing hierarchical Bayesian methods for MTCs with a single outcome, and introduce novel Bayesian approaches for multiple outcomes simultaneously, rather than in separate MTC analyses. We do this by incorporating partially observed data and its correlation structure between outcomes through contrast- and arm-based parameterizations that consider any unobserved treatment arms as missing data to be imputed. We also extend the model to apply to all types of generalized linear model outcomes, such as count or continuous responses. We offer a simulation study under various missingness mechanisms (e.g., MCAR, MAR, and MNAR) providing evidence that our models outperform existing models in terms of bias, MSE, and coverage probability, then illustrate our methods with a real MTC dataset. We close with a discussion of our results, several contentious issues in MTC analysis, and a few avenues for future methodological development.
doi:10.1002/jrsm.1153
PMCID: PMC4779385  PMID: 26536149
Bayesian hierarchical model; Markov chain Monte Carlo; missingness mechanism; network meta-analysis
22.  The anatomy of choice: active inference and agency 
This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behavior. In particular, we consider prior beliefs that action minimizes the Kullback–Leibler (KL) divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimizes a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimizing free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action—constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualizes optimal decision theory and economic (utilitarian) formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution—that minimizes free energy. This sensitivity corresponds to the precision of beliefs about behavior, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behavior entails a representation of confidence about outcomes that are under an agent's control.
doi:10.3389/fnhum.2013.00598
PMCID: PMC3782702  PMID: 24093015
active inference; agency; Bayesian; bounded rationality; embodied cognition; free energy; inference; utility theory
23.  Continuous Subcutaneous Insulin Infusion (CSII) Pumps for Type 1 and Type 2 Adult Diabetic Populations 
Executive Summary
In June 2008, the Medical Advisory Secretariat began work on the Diabetes Strategy Evidence Project, an evidence-based review of the literature surrounding strategies for successful management and treatment of diabetes. This project came about when the Health System Strategy Division at the Ministry of Health and Long-Term Care subsequently asked the secretariat to provide an evidentiary platform for the Ministry’s newly released Diabetes Strategy.
After an initial review of the strategy and consultation with experts, the secretariat identified five key areas in which evidence was needed. Evidence-based analyses have been prepared for each of these five areas: insulin pumps, behavioural interventions, bariatric surgery, home telemonitoring, and community based care. For each area, an economic analysis was completed where appropriate and is described in a separate report.
To review these titles within the Diabetes Strategy Evidence series, please visit the Medical Advisory Secretariat Web site, http://www.health.gov.on.ca/english/providers/program/mas/mas_about.html,
Diabetes Strategy Evidence Platform: Summary of Evidence-Based Analyses
Continuous Subcutaneous Insulin Infusion Pumps for Type 1 and Type 2 Adult Diabetics: An Evidence-Based Analysis
Behavioural Interventions for Type 2 Diabetes: An Evidence-Based Analysis
Bariatric Surgery for People with Diabetes and Morbid Obesity: An Evidence-Based Summary
Community-Based Care for the Management of Type 2 Diabetes: An Evidence-Based Analysis
Home Telemonitoring for Type 2 Diabetes: An Evidence-Based Analysis
Application of the Ontario Diabetes Economic Model (ODEM) to Determine the Cost-effectiveness and Budget Impact of Selected Type 2 Diabetes Interventions in Ontario
Objective
The objective of this analysis is to review the efficacy of continuous subcutaneous insulin infusion (CSII) pumps as compared to multiple daily injections (MDI) for the type 1 and type 2 adult diabetics.
Clinical Need and Target Population
Insulin therapy is an integral component of the treatment of many individuals with diabetes. Type 1, or juvenile-onset diabetes, is a life-long disorder that commonly manifests in children and adolescents, but onset can occur at any age. It represents about 10% of the total diabetes population and involves immune-mediated destruction of insulin producing cells in the pancreas. The loss of these cells results in a decrease in insulin production, which in turn necessitates exogenous insulin therapy.
Type 2, or ‘maturity-onset’ diabetes represents about 90% of the total diabetes population and is marked by a resistance to insulin or insufficient insulin secretion. The risk of developing type 2 diabetes increases with age, obesity, and lack of physical activity. The condition tends to develop gradually and may remain undiagnosed for many years. Approximately 30% of patients with type 2 diabetes eventually require insulin therapy.
CSII Pumps
In conventional therapy programs for diabetes, insulin is injected once or twice a day in some combination of short- and long-acting insulin preparations. Some patients require intensive therapy regimes known as multiple daily injection (MDI) programs, in which insulin is injected three or more times a day. It’s a time consuming process and usually requires an injection of slow acting basal insulin in the morning or evening and frequent doses of short-acting insulin prior to eating. The most common form of slower acting insulin used is neutral protamine gagedorn (NPH), which reaches peak activity 3 to 5 hours after injection. There are some concerns surrounding the use of NPH at night-time as, if injected immediately before bed, nocturnal hypoglycemia may occur. To combat nocturnal hypoglycemia and other issues related to absorption, alternative insulins have been developed, such as the slow-acting insulin glargine. Glargine has no peak action time and instead acts consistently over a twenty-four hour period, helping reduce the frequency of hypoglycemic episodes.
Alternatively, intensive therapy regimes can be administered by continuous insulin infusion (CSII) pumps. These devices attempt to closely mimic the behaviour of the pancreas, continuously providing a basal level insulin to the body with additional boluses at meal times. Modern CSII pumps are comprised of a small battery-driven pump that is designed to administer insulin subcutaneously through the abdominal wall via butterfly needle. The insulin dose is adjusted in response to measured capillary glucose values in a fashion similar to MDI and is thus often seen as a preferred method to multiple injection therapy. There are, however, still risks associated with the use of CSII pumps. Despite the increased use of CSII pumps, there is uncertainty around their effectiveness as compared to MDI for improving glycemic control.
Part A: Type 1 Diabetic Adults (≥19 years)
An evidence-based analysis on the efficacy of CSII pumps compared to MDI was carried out on both type 1 and type 2 adult diabetic populations.
Research Questions
Are CSII pumps more effective than MDI for improving glycemic control in adults (≥19 years) with type 1 diabetes?
Are CSII pumps more effective than MDI for improving additional outcomes related to diabetes such as quality of life (QoL)?
Literature Search
Inclusion Criteria
Randomized controlled trials, systematic reviews, meta-analysis and/or health technology assessments from MEDLINE, EMBASE, CINAHL
Adults (≥ 19 years)
Type 1 diabetes
Study evaluates CSII vs. MDI
Published between January 1, 2002 – March 24, 2009
Patient currently on intensive insulin therapy
Exclusion Criteria
Studies with <20 patients
Studies <5 weeks in duration
CSII applied only at night time and not 24 hours/day
Mixed group of diabetes patients (children, adults, type 1, type 2)
Pregnancy studies
Outcomes of Interest
The primary outcomes of interest were glycosylated hemoglobin (HbA1c) levels, mean daily blood glucose, glucose variability, and frequency of hypoglycaemic events. Other outcomes of interest were insulin requirements, adverse events, and quality of life.
Search Strategy
The literature search strategy employed keywords and subject headings to capture the concepts of:
1) insulin pumps, and
2) type 1 diabetes.
The search was run on July 6, 2008 in the following databases: Ovid MEDLINE (1996 to June Week 4 2008), OVID MEDLINE In-Process and Other Non-Indexed Citations, EMBASE (1980 to 2008 Week 26), OVID CINAHL (1982 to June Week 4 2008) the Cochrane Library, and the Centre for Reviews and Dissemination/International Agency for Health Technology Assessment. A search update was run on March 24, 2009 and studies published prior to 2002 were also examined for inclusion into the review. Parallel search strategies were developed for the remaining databases. Search results were limited to human and English-language published between January 2002 and March 24, 2009. Abstracts were reviewed, and studies meeting the inclusion criteria outlined above were obtained. Reference lists were also checked for relevant studies.
Summary of Findings
The database search identified 519 relevant citations published between 1996 and March 24, 2009. Of the 519 abstracts reviewed, four RCTs and one abstract met the inclusion criteria outlined above. While efficacy outcomes were reported in each of the trials, a meta-analysis was not possible due to missing data around standard deviations of change values as well as missing data for the first period of the crossover arm of the trial. Meta-analysis was not possible on other outcomes (quality of life, insulin requirements, frequency of hypoglycemia) due to differences in reporting.
HbA1c
In studies where no baseline data was reported, the final values were used. Two studies (Hanaire-Broutin et al. 2000, Hoogma et al. 2005) reported a slight reduction in HbA1c of 0.35% and 0.22% respectively for CSII pumps in comparison to MDI. A slightly larger reduction in HbA1c of 0.84% was reported by DeVries et al.; however, this study was the only study to include patients with poor glycemic control marked by higher baseline HbA1c levels. One study (Bruttomesso et al. 2008) showed no difference between CSII pumps and MDI on Hba1c levels and was the only study using insulin glargine (consistent with results of parallel RCT in abstract by Bolli 2004). While there is statistically significant reduction in HbA1c in three of four trials, there is no evidence to suggest these results are clinically significant.
Mean Blood Glucose
Three of four studies reported a statistically significant reduction in the mean daily blood glucose for patients using CSII pump, though these results were not clinically significant. One study (DeVries et al. 2002) did not report study data on mean blood glucose but noted that the differences were not statistically significant. There is difficulty with interpreting study findings as blood glucose was measured differently across studies. Three of four studies used a glucose diary, while one study used a memory meter. In addition, frequency of self monitoring of blood glucose (SMBG) varied from four to nine times per day. Measurements used to determine differences in mean daily blood glucose between the CSII pump group and MDI group at clinic visits were collected at varying time points. Two studies use measurements from the last day prior to the final visit (Hoogma et al. 2005, DeVries et al. 2002), while one study used measurements taken during the last 30 days and another study used measurements taken during the 14 days prior to the final visit of each treatment period.
Glucose Variability
All four studies showed a statistically significant reduction in glucose variability for patients using CSII pumps compared to those using MDI, though one, Bruttomesso et al. 2008, only showed a significant reduction at the morning time point. Brutomesso et al. also used alternate measures of glucose variability and found that both the Lability index and mean amplitude of glycemic excursions (MAGE) were in concordance with the findings using the standard deviation (SD) values of mean blood glucose, but the average daily risk range (ADRR) showed no difference between the CSII pump and MDI groups.
Hypoglycemic Events
There is conflicting evidence concerning the efficacy of CSII pumps in decreasing both mild and severe hypoglycemic events. For mild hypoglycemic events, DeVries et al. observed a higher number of events per patient week in the CSII pump group than the MDI group, while Hoogma et al. observed a higher number of events per patient year in the MDI group. The remaining two studies found no differences between the two groups in the frequency of mild hypoglycemic events. For severe hypoglycemic events, Hoogma et al. found an increase in events per patient year among MDI patients, however, all of the other RCTs showed no difference between the patient groups in this aspect.
Insulin Requirements and Adverse Events
In all four studies, insulin requirements were significantly lower in patients receiving CSII pump treatment in comparison to MDI. This difference was statistically significant in all studies. Adverse events were reported in three studies. Devries et al. found no difference in ketoacidotic episodes between CSII pump and MDI users. Bruttomesso et al. reported no adverse events during the study. Hanaire-Broutin et al. found that 30 patients experienced 58 serious adverse events (SAEs) during MDI and 23 patients had 33 SAEs during treatment out of a total of 256 patients. Most events were related to severe hypoglycemia and diabetic ketoacidosis.
Quality of Life and Patient Preference
QoL was measured in three studies and patient preference was measured in one. All three studies found an improvement in QoL for CSII users compared to those using MDI, although various instruments were used among the studies and possible reporting bias was evident as non-positive outcomes were not consistently reported. Moreover, there was also conflicting results in two of the studies using the Diabetes Treatment Satisfaction Questionnaire (DTSQ). DeVries et al. reported no difference in treatment satisfaction between CSII pump users and MDI users while Brutomesso et al. reported that treatment satisfaction improved among CSII pump users.
Patient preference for CSII pumps was demonstrated in just one study (Hanaire-Broutin et al. 2000) and there are considerable limitations with interpreting this data as it was gathered through interview and 72% of patients that preferred CSII pumps were previously on CSII pump therapy prior to the study. As all studies were industry sponsored, findings on QoL and patient preference must be interpreted with caution.
Quality of Evidence
Overall, the body of evidence was downgraded from high to low due to study quality and issues with directness as identified using the GRADE quality assessment tool (see Table 1) While blinding of patient to intervention/control was not feasible in these studies, blinding of study personnel during outcome assessment and allocation concealment were generally lacking. Trials reported consistent results for the outcomes HbA1c, mean blood glucose and glucose variability, but the directness or generalizability of studies, particularly with respect to the generalizability of the diabetic population, was questionable as most trials used highly motivated populations with fairly good glycemic control. In addition, the populations in each of the studies varied with respect to prior treatment regimens, which may not be generalizable to the population eligible for pumps in Ontario. For the outcome of hypoglycaemic events the evidence was further downgraded to very low since there was conflicting evidence between studies with respect to the frequency of mild and severe hypoglycaemic events in patients using CSII pumps as compared to CSII (see Table 2). The GRADE quality of evidence for the use of CSII in adults with type 1 diabetes is therefore low to very low and any estimate of effect is, therefore, uncertain.
GRADE Quality Assessment for CSII pumps vs. MDI on HbA1c, Mean Blood Glucose, and Glucose Variability for Adults with Type 1 Diabetes
Inadequate or unknown allocation concealment (3/4 studies); Unblinded assessment (all studies) however lack of blinding due to the nature of the study; No ITT analysis (2/4 studies); possible bias SMBG (all studies)
HbA1c: 3/4 studies show consistency however magnitude of effect varies greatly; Single study uses insulin glargine instead of NPH; Mean Blood Glucose: 3/4 studies show consistency however magnitude of effect varies between studies; Glucose Variability: All studies show consistency but 1 study only showed a significant effect in the morning
Generalizability in question due to varying populations: highly motivated populations, educational component of interventions/ run-in phases, insulin pen use in 2/4 studies and varying levels of baseline glycemic control and experience with intensified insulin therapy, pumps and MDI.
GRADE Quality Assessment for CSII pumps vs. MDI on Frequency of Hypoglycemic
Inadequate or unknown allocation concealment (3/4 studies); Unblinded assessment (all studies) however lack of blinding due to the nature of the study; No ITT analysis (2/4 studies); possible bias SMBG (all studies)
Conflicting evidence with respect to mild and severe hypoglycemic events reported in studies
Generalizability in question due to varying populations: highly motivated populations, educational component of interventions/ run-in phases, insulin pen use in 2/4 studies and varying levels of baseline glycemic control and experience with intensified insulin therapy, pumps and MDI.
Economic Analysis
One article was included in the analysis from the economic literature scan. Four other economic evaluations were identified but did not meet our inclusion criteria. Two of these articles did not compare CSII with MDI and the other two articles used summary estimates from a mixed population with Type 1 and 2 diabetes in their economic microsimulation to estimate costs and effects over time. Included were English articles that conducted comparisons between CSII and MDI with the outcome of Quality Adjusted Life Years (QALY) in an adult population with type 1 diabetes.
From one study, a subset of the population with type 1 diabetes was identified that may be suitable and benefit from using insulin pumps. There is, however, limited data in the literature addressing the cost-effectiveness of insulin pumps versus MDI in type 1 diabetes. Longer term models are required to estimate the long term costs and effects of pumps compared to MDI in this population.
Conclusions
CSII pumps for the treatment of adults with type 1 diabetes
Based on low-quality evidence, CSII pumps confer a statistically significant but not clinically significant reduction in HbA1c and mean daily blood glucose as compared to MDI in adults with type 1 diabetes (>19 years).
CSII pumps also confer a statistically significant reduction in glucose variability as compared to MDI in adults with type 1 diabetes (>19 years) however the clinical significance is unknown.
There is indirect evidence that the use of newer long-acting insulins (e.g. insulin glargine) in MDI regimens result in less of a difference between MDI and CSII compared to differences between MDI and CSII in which older insulins are used.
There is conflicting evidence regarding both mild and severe hypoglycemic events in this population when using CSII pumps as compared to MDI. These findings are based on very low-quality evidence.
There is an improved quality of life for patients using CSII pumps as compared to MDI however, limitations exist with this evidence.
Significant limitations of the literature exist specifically:
All studies sponsored by insulin pump manufacturers
All studies used crossover design
Prior treatment regimens varied
Types of insulins used in study varied (NPH vs. glargine)
Generalizability of studies in question as populations were highly motivated and half of studies used insulin pens as the mode of delivery for MDI
One short-term study concluded that pumps are cost-effective, although this was based on limited data and longer term models are required to estimate the long-term costs and effects of pumps compared to MDI in adults with type 1 diabetes.
Part B: Type 2 Diabetic Adults
Research Questions
Are CSII pumps more effective than MDI for improving glycemic control in adults (≥19 years) with type 2 diabetes?
Are CSII pumps more effective than MDI for improving other outcomes related to diabetes such as quality of life?
Literature Search
Inclusion Criteria
Randomized controlled trials, systematic reviews, meta-analysis and/or health technology assessments from MEDLINE, Excerpta Medica Database (EMBASE), Cumulative Index to Nursing & Allied Health Literature (CINAHL)
Any person with type 2 diabetes requiring insulin treatment intensive
Published between January 1, 2000 – August 2008
Exclusion Criteria
Studies with <10 patients
Studies <5 weeks in duration
CSII applied only at night time and not 24 hours/day
Mixed group of diabetes patients (children, adults, type 1, type 2)
Pregnancy studies
Outcomes of Interest
The primary outcome of interest was a reduction in glycosylated hemoglobin (HbA1c) levels. Other outcomes of interest were mean blood glucose level, glucose variability, insulin requirements, frequency of hypoglycemic events, adverse events, and quality of life.
Search Strategy
A comprehensive literature search was performed in OVID MEDLINE, MEDLINE In-Process and Other Non-Indexed Citations, EMBASE, CINAHL, The Cochrane Library, and the International Agency for Health Technology Assessment (INAHTA) for studies published between January 1, 2000 and August 15, 2008. Studies meeting the inclusion criteria were selected from the search results. Data on the study characteristics, patient characteristics, primary and secondary treatment outcomes, and adverse events were abstracted. Reference lists of selected articles were also checked for relevant studies. The quality of the evidence was assessed as high, moderate, low, or very low according to the GRADE methodology.
Summary of Findings
The database search identified 286 relevant citations published between 1996 and August 2008. Of the 286 abstracts reviewed, four RCTs met the inclusion criteria outlined above. Upon examination, two studies were subsequently excluded from the meta-analysis due to small sample size and missing data (Berthe et al.), as well as outlier status and high drop out rate (Wainstein et al) which is consistent with previously reported meta-analyses on this topic (Jeitler et al 2008, and Fatourechi M et al. 2009).
HbA1c
The primary outcome in this analysis was reduction in HbA1c. Both studies demonstrated that both CSII pumps and MDI reduce HbA1c, but neither treatment modality was found to be superior to the other. The results of a random effects model meta-analysis showed a mean difference in HbA1c of -0.14 (-0.40, 0.13) between the two groups, which was found not to be statistically or clinically significant. There was no statistical heterogeneity observed between the two studies (I2=0%).
Forrest plot of two parallel, RCTs comparing CSII to MDI in type 2 diabetes
Secondary Outcomes
Mean Blood Glucose and Glucose Variability
Mean blood glucose was only used as an efficacy outcome in one study (Raskin et al. 2003). The authors found that the only time point in which there were consistently lower blood glucose values for the CSII group compared to the MDI group was 90 minutes after breakfast. Glucose variability was not examined in either study and the authors reported no difference in weight gain between the CSII pump group and MDI groups at the end of study. Conflicting results were reported regarding injection site reactions between the two studies. Herman et al. reported no difference in the number of subjects experiencing site problems between the two groups, while Raskin et al. reported that there were no injection site reactions in the MDI group but 15 such episodes among 8 participants in the CSII pump group.
Frequency of Hypoglycemic Events and Insulin Requirements
All studies reported that there were no differences in the number of mild hypoglycemic events in patients on CSII pumps versus MDI. Herman et al. also reported no differences in the number of severe hypoglycemic events in patients using CSII pumps compared to those on MDI. Raskin et al. reported that there were no severe hypoglycemic events in either group throughout the study duration. Insulin requirements were only examined in Herman et al., who found that daily insulin requirements were equal between the CSII pump and MDI treatment groups.
Quality of Life
QoL was measured by Herman et al. using the Diabetes Quality of Life Clinical Trial Questionnaire (DQOLCTQ). There were no differences reported between CSII users and MDI users for treatment satisfaction, diabetes impact, and worry-related scores. Patient satisfaction was measured in Raskin et al. using a patient satisfaction questionnaire, whose results indicated that patients in the CSII pump group had significantly greater improvement in overall treatment satisfaction at the end of the study compared to the MDI group. Although patient preference was also reported, it was only examined in the CSII pump group, thus results indicating a greater preference for CSII pumps in this groups (as compared to prior injectable insulin regimens) are biased and must be interpreted with caution.
Quality of Evidence
Overall, the body of evidence was downgraded from high to low according to study quality and issues with directness as identified using the GRADE quality assessment tool (see Table 3). While blinding of patient to intervention/control is not feasible in these studies, blinding of study personnel during outcome assessment and allocation concealment were generally lacking. ITT was not clearly explained in one study and heterogeneity between study populations was evident from participants’ treatment regimens prior to study initiation. Although trials reported consistent results for HbA1c outcomes, the directness or generalizability of studies, particularly with respect to the generalizability of the diabetic population, was questionable as trials required patients to adhere to an intense SMBG regimen. This suggests that patients were highly motivated. In addition, since prior treatment regimens varied between participants (no requirement for patients to be on MDI), study findings may not be generalizable to the population eligible for a pump in Ontario. The GRADE quality of evidence for the use of CSII in adults with type 2 diabetes is, therefore, low and any estimate of effect is uncertain.
GRADE Quality Assessment for CSII pumps vs. MDI on HbA1c Adults with Type 2 Diabetes
Inadequate or unknown allocation concealment (all studies); Unblinded assessment (all studies) however lack of blinding due to the nature of the study; ITT not well explained in 1 of 2 studies
Indirect due to lack of generalizability of findings since participants varied with respect to prior treatment regimens and intensive SMBG suggests highly motivated populations used in trials.
Economic Analysis
An economic analysis of CSII pumps was carried out using the Ontario Diabetes Economic Model (ODEM) and has been previously described in the report entitled “Application of the Ontario Diabetes Economic Model (ODEM) to Determine the Cost-effectiveness and Budget Impact of Selected Type 2 Diabetes Interventions in Ontario”, part of the diabetes strategy evidence series. Based on the analysis, CSII pumps are not cost-effective for adults with type 2 diabetes, either for the age 65+ sub-group or for all patients in general. Details of the analysis can be found in the full report.
Conclusions
CSII pumps for the treatment of adults with type 2 diabetes
There is low quality evidence demonstrating that the efficacy of CSII pumps is not superior to MDI for adult type 2 diabetics.
There were no differences in the number of mild and severe hypoglycemic events in patients on CSII pumps versus MDI.
There are conflicting findings with respect to an improved quality of life for patients using CSII pumps as compared to MDI.
Significant limitations of the literature exist specifically:
All studies sponsored by insulin pump manufacturers
Prior treatment regimens varied
Types of insulins used in study varied (NPH vs. glargine)
Generalizability of studies in question as populations may not reflect eligible patient population in Ontario (participants not necessarily on MDI prior to study initiation, pen used in one study and frequency of SMBG required during study was high suggesting highly motivated participants)
Based on ODEM, insulin pumps are not cost-effective for adults with type 2 diabetes either for the age 65+ sub-group or for all patients in general.
PMCID: PMC3377523  PMID: 23074525
24.  A Bayesian approach to joint analysis of longitudinal measurements and competing risks failure time data 
Statistics in medicine  2009;28(11):1601-1619.
SUMMARY
In this paper, we develop a Bayesian method for joint analysis of longitudinal measurements and competing risks failure time data. The model allows one to analyze the longitudinal outcome with nonignorable missing data induced by multiple types of events, to analyze survival data with dependent censoring for the key event, and to draw inferences on multiple endpoints simultaneously. Compared with the likelihood approach, the Bayesian method has several advantages. It is computationally more tractable for high-dimensional random effects. It is also convenient to draw inference. Moreover, it provides a means to incorporate prior information that may help to improve estimation accuracy. An illustration is given using a clinical trial data of scleroderma lung disease. The performance of our method is evaluated by simulation studies.
doi:10.1002/sim.3562
PMCID: PMC3168565  PMID: 19308919
joint modeling; competing risks; longitudinal data; Bayesian approach
25.  Individual (N-of-1) trials can be combined to give population comparative treatment effect estimates: Methodologic considerations 
Journal of clinical epidemiology  2010;63(12):1312-1323.
Abstract/Summary
Objective
To compare different statistical models for combining N-of-1 trials to estimate a population treatment effect.
Study Design and Setting
Data from a published series of N-of-1 trials comparing amitriptyline therapy and combination treatment (amitriptyline + fluoxetine ) were analyzed to compare summary and individual participant data meta-analysis, repeated measures models, Bayesian hierarchical models, single-period, single-pair and averaged outcome crossover models.
Results
The best fitting model included a random intercept (response on amitriptyline) and fixed treatment effect (added fluoxetine). Results supported a common, uncorrelated within-patient covariance structure that is equal between-treatments and across patients. Assuming unequal within-patient variances, a random effects model was favored. Bayesian hierarchical models improved precision and were highly sensitive to within-patient variance priors.
Conclusion
Optimal models for combining N-of-1 trials need to consider goals, data sources, and relative within and between patient variances. Without sufficient patients, between-patient variation will be hard to explain with covariates. N-of-1 data with few observations per patients may not support models with heterogeneous within-patient variation. With common variances, models appear robust. Bayesian models may improve parameter estimation but are sensitive to prior assumptions about variance components. With limited resources, improving within-patient precision must be balanced by increased participants to explain population variation.
doi:10.1016/j.jclinepi.2010.04.020
PMCID: PMC2963698  PMID: 20863658
N-of-1 trials; methodology; comparisons; population estimate; meta-analysis; comparative effectiveness

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