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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.  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
3.  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
4.  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
5.  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
6.  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
7.  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
8.  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
9.  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
10.  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
11.  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
12.  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
13.  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
14.  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
15.  Repetitive Transcranial Magnetic Stimulation for the Treatment of Major Depressive Disorder 
Executive Summary
Objective
This review was conducted to assess the effectiveness of repetitive transcranial magnetic stimulation (rTMS) in the treatment of major depressive disorder (MDD).
The Technology
rTMS is a noninvasive way to stimulate nerve cells in areas of the brain. During rTMS, an electrical current passes through a wire coil placed over the scalp. The current induces a magnetic field that produces an electrical field in the brain that then causes nerve cells to depolarize, resulting in the stimulation or disruption of brain activity.
Researchers have investigated rTMS as an option to treat MDD, as an add-on to drug therapy, and, in particular, as an alternative to electroconvulsive therapy (ECT) for patients with treatment-resistant depression.
The advantages of rTMS over ECT for patients with severe refractory depression are that general anesthesia is not needed, it is an outpatient procedure, it requires less energy, the simulation is specific and targeted, and convulsion is not required. The advantages of rTMS as an add-on treatment to drug therapy may include hastening of the clinical response when used with antidepressant drugs.
Review Strategy
The Medical Advisory Secretariat used its standard search strategy to locate international health technology assessments and English-language journal articles published from January 1996 to March 2004.
Summary of Findings
Some early meta-analyses suggested rTMS might be effective for the treatment of MDD (for treatment-resistant MDD and as an add-on treatment to drug therapy for patients not specifically defined as treatment resistant). There were, however, several crucial methodological limitations in the included studies that were not critically assessed. These are discussed below.
Recent meta-analyses (including 2 international health technology assessments) have done evidence-based critical analyses of studies that have assessed rTMS for MDD. The 2 most recent health technology assessments (from the Oxford Cochrane Collaboration and the Norwegian Centre for Health Technology Assessment) concluded that there is no evidence that rTMS is effective for the treatment of MDD, either as compared with a placebo for patients with treatment-resistant or nontreatment-resistant MDD, or as an alternative to ECT for patients with treatment-resistant MDD. This mainly due to the poor quality of the studies.
The major methodological limitations were identified in older meta-analyses, recent health technology assessments, and the most recently published trials (Level 2–4 evidence) on the effectiveness of rTMS for MDD are discussed below.
Small sample size was a limitation acknowledged by many of the authors. There was also a lack of a priori sample size calculation or justification.
Biased randomization may have been a problem. Generally, the published reports lacked detailed information on the method of allocation concealment used. This is important because it is impossible to determine if there was a possible influence (direct or indirect) in the allocation of the patients to different treatment groups.
The trials were single blind, evaluated by external blinded assessors, rather than double blind. Double blinding is more robust, because neither the participants nor the investigators know which participants are receiving the active treatment and which are getting a placebo. Those administering rTMS, however, cannot be blinded to whether they are administering the active treatment or a placebo.
There was patient variability among the studies. In some studies, the authors said that patients were “medication resistant,” but the definitions of resistant, if provided, were inconsistent or unclear. For example, some described “medication resistant” as failing at least one trial of drugs during the current depressive episode. Furthermore, it was unclear if the term “medication resistant” referred to antidepressants only or to combinations of antidepressants and other drug augmentation strategies (such as neuroleptics, benzodiazepine, carbamazepine, and lithium). Also variable was the type of depression (i.e., unipolar and/or bipolar), if patients were inpatients or outpatients, if they had psychotic symptoms or no psychotic symptoms, and the chronicity of depression.
Dropouts or withdrawals were a concern. Some studies reported that patients dropped out, but provided no further details. Intent-to-treat analysis was not done in any of the trials. This is important, because ignoring patients who drop out of a trial can bias the results, usually in favour of the treatment. This is because patients who withdraw from trials are less likely to have had the treatment, more likely to have missed their interim checkups, and more likely to have experienced adverse effects when taking the treatment, compared with patients who do not withdraw. (1)
Measurement of treatment outcomes using scales or inventories makes interpreting results and drawing conclusions difficult. The most common scale, the Hamilton Depression Rating Scale (HDRS) is based on a semistructured interview. Some authors (2) reported that rating scales based on semistructured interviews are more susceptible to observation bias than are self-administered questionnaires such as the Beck Depression Inventory (BDI). Martin et al. (3) argued that the lack of consistency in effect as determined by the 2 scales (a positive result after 2 weeks of treatment as measured by the HDRS and a negative result for the BDI) makes definitive conclusions about the nature of the change in mood of patients impossible. It was suggested that because of difficulties interpreting results from psychometric scales, (4) and the subjective or unstable character of MDD, other, more objective, outcome measures such as readmission to hospital, time to hospital discharge, time to adjunctive treatment, and time off work should be used to assess rTMS for the treatment of depression.
A placebo effect could have influenced the results. Many studies reported response rates for patients who received placebo treatment. For example, Klein et al. (5) reported a control group response rate as high as 25%. Patients receiving placebo rTMS may receive a small dose of magnetic energy that may alter their depression.
Short-term studies were the most common. Patients received rTMS treatment for 1 to 2 weeks. Most studies followed-up patients for 2 to 4 weeks post-treatment. Dannon et al. (6) followed-up patients who responded to a course of ECT or rTMS for up to 6 months; however, the assessment procedure was not blinded, the medication regimen during follow-up was not controlled, and initial baseline data for the patient groups were not reported. The long-term effectiveness of rTMS for the treatment of depression is unknown, as is the long-term use, if any, of maintenance therapy. The cost-effectiveness of rTMS for the treatment of depression is also unknown. A lack of long-term studies makes cost-effectiveness analysis difficult.
The complexity of possible combinations for administering rTMS makes comparing like with like difficult. Wasserman and Lisanby (7) have said that the method for precisely targeting the stimulation in this area is unreliable. It is unknown if the left dorsolateral prefrontal cortex is the optimal location for treatment. Further, differences in rTMS administration include number of trains per session, duration of each train, and motor threshold.
Clinical versus statistical significance. Several meta-analyses and studies have found that the degree of therapeutic change associated with rTMS across studies is relatively modest; that is, results may be statistically, but not necessarily clinically, significant. (8-11). Conventionally, a 50% reduction in the HDRS scores is commonly accepted as a clinically important reduction in depression. Although some studies have observed a statistically significant reduction in the depression rating, many have not shows the clinically significant reduction of 50% on the HDRS. (11-13) Therefore, few patients in these studies would meet the standard criteria for response. (9)
Clinical/methodological diversity and statistical heterogeneity. In the Norwegian health technology assessment, Aarre et al. (14) said that a formal meta-analysis was not feasible because the designs of the studies varied too much, particularly in how rTMS was administered and in the characteristics of the patients. They noted that the quality of the study designs was poor. The 12 studies that comprised the assessment had small samples, and highly variable inclusion criteria and study designs. The patients’ previous histories, diagnoses, treatment histories, and treatment settings were often insufficiently characterized. Furthermore, many studies reported that patients had treatment-resistant MDD, yet did not listclear criteria for the designation. Without this information, Aarre and colleagues suggested that the interpretation of the results is difficult and the generalizability of results is questionable. They concluded that rTMS cannot be recommended as a standard treatment for depression: “More, larger and more carefully designed studies are needed to demonstrate convincingly a clinically relevant effect of rTMS.”
In the Cochrane Collaboration systematic review, Martin et al. (3;15) said that the complexity of possible combinations for administering rTMS makes comparison of like versus like difficult. A statistical test for heterogeneity (chi-square test) examines if the observed treatment effects are more different from each other than one would expect due to random error (or chance) alone. (16) However, this statistical test must be interpreted with caution because it has low power in the (common) situation of a meta-analysis when the trials have small sample sizes or are few. This means that while a statistically significant result may indicate a problem with heterogeneity, a nonsignificant result must not be taken as evidence of no heterogeneity.
Despite not finding statistically significant heterogeneity, Martin et al. reported that the overall mean baseline depression values for the severity of depression were higher in the treatment group than in the placebo group. (3;15) Although these differences were not significant at the level of each study, they may have introduced potential bias into the meta-analysis of pooled data by accentuating the tendency for regression to the mean of the more extreme values. Individual patient data from all the studies were not available; therefore, an appropriate adjustment according to baseline severity was not possible. Martin et al. concluded that the findings from the systematic review and meta-analysis provided insufficient evidence to suggest that rTMS is effective in the treatment of depression. Moreover, there were several confounding factors (e.g., definition of treatment resistance) in the studies, thus the authors concluded, “The rTMS technique needs more high quality trials to show its effectiveness for therapeutic use.”
Conclusion
Due to several serious methodological limitations in the studies that have examined the effectiveness of rTMS in patients with MDD, it is not possible to conclude that rTMS either is or is not effective as a treatment for MDD (in treatment-resistant depression or in nontreatment-resistant depression).
PMCID: PMC3387754  PMID: 23074457
16.  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
17.  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
18.  On Estimation of the Survivor Average Causal Effect in Observational Studies when Important Confounders are Missing Due to Death 
Biometrics  2009;65(2):497-504.
We focus on estimation of the causal effect of treatment on the functional status of individuals at a fixed point in time t* after they have experienced a catastrophic event, from observational data with the following features: (1) treatment is imposed shortly after the event and is non-randomized, (2) individuals who survive to t* are scheduled to be interviewed, (3) there is interview non-response, (4) individuals who die prior to t* are missing information on pre-event confounders, (5) medical records are abstracted on all individuals to obtain information on post-event, pre-treatment confounding factors. To address the issue of survivor bias, we seek to estimate the survivor average causal effect (SACE), the effect of treatment on functional status among the cohort of individuals who would survive to t* regardless of whether or not assigned to treatment. To estimate this effect from observational data, we need to impose untestable assumptions, which depend on the collection of all confounding factors. Since pre-event information is missing on those who die prior to t*, it is unlikely that these data are missing at random (MAR). We introduce a sensitivity analysis methodology to evaluate the robustness of SACE inferences to deviations from the MAR assumption. We apply our methodology to the evaluation of the effect of trauma center care on vitality outcomes using data from the National Study on Costs and Outcomes of Trauma Care.
doi:10.1111/j.1541-0420.2008.01111.x
PMCID: PMC2700847  PMID: 18759833
19.  Implementing the Bayesian paradigm: reporting research results over the World-Wide Web. 
For decades, statisticians, philosophers, medical investigators and others interested in data analysis have argued that the Bayesian paradigm is the proper approach for reporting the results of scientific analyses for use by clients and readers. To date, the methods have been too complicated for non-statisticians to use. In this paper we argue that the World-Wide Web provides the perfect environment to put the Bayesian paradigm into practice: the likelihood function of the data is parsimoniously represented on the server side, the reader uses the client to represent her prior belief, and a downloaded program (a Java applet) performs the combination. In our approach, a different applet can be used for each likelihood function, prior belief can be assessed graphically, and calculation results can be reported in a variety of ways. We present a prototype implementation, BayesApplet, for two-arm clinical trials with normally-distributed outcomes, a prominent model for clinical trials. The primary implication of this work is that publishing medical research results on the Web can take a form beyond or different from that currently used on paper, and can have a profound impact on the publication and use of research results.
PMCID: PMC2232964  PMID: 8947703
20.  Diffusion-based spatial priors for functional magnetic resonance images 
Neuroimage  2008;41(2-3):408-423.
We recently outlined a Bayesian scheme for analyzing fMRI data using diffusion-based spatial priors [Harrison, L.M., Penny, W., Ashburner, J., Trujillo-Barreto, N., Friston, K.J., 2007. Diffusion-based spatial priors for imaging. NeuroImage 38, 677–695]. The current paper continues this theme, applying it to a single-subject functional magnetic resonance imaging (fMRI) study of the auditory system. We show that spatial priors on functional activations, based on diffusion, can be formulated in terms of the eigenmodes of a graph Laplacian. This allows one to discard eigenmodes with small eigenvalues, to provide a computationally efficient scheme. Furthermore, this formulation shows that diffusion-based priors are a generalization of conventional Laplacian priors [Penny, W.D., Trujillo-Barreto, N.J., Friston, K.J., 2005. Bayesian fMRI time series analysis with spatial priors. NeuroImage 24, 350–362]. Finally, we show how diffusion-based priors are a special case of Gaussian process models that can be inverted using classical covariance component estimation techniques like restricted maximum likelihood [Patterson, H.D., Thompson, R., 1974. Maximum likelihood estimation of components of variance. Paper presented at: 8th International Biometrics Conference (Constanta, Romania)]. The convention in SPM is to smooth data with a fixed isotropic Gaussian kernel before inverting a mass-univariate statistical model. This entails the strong assumption that data are generated smoothly throughout the brain. However, there is no way to determine if this assumption is supported by the data, because data are smoothed before statistical modeling. In contrast, if a spatial prior is used, smoothness is estimated given non-smoothed data. Explicit spatial priors enable formal model comparison of different prior assumptions, e.g., that data are generated from a stationary (i.e., fixed throughout the brain) or non-stationary spatial process. Indeed, for the auditory data we provide strong evidence for a non-stationary process, which concurs with a qualitative comparison of predicted activations at the boundary of functionally selective regions.
doi:10.1016/j.neuroimage.2008.02.005
PMCID: PMC2643093  PMID: 18387821
Single-subject fMRI; Spatial priors; Weighted graph Laplacian; [non-]stationary spatial process; Diffusion kernel; Eigenmodes; Covariance components; Matrix-variate normal density; Bayesian model comparison; Expectation maximization; Fisher-scoring
21.  Risk-Sensitivity in Bayesian Sensorimotor Integration 
PLoS Computational Biology  2012;8(9):e1002698.
Information processing in the nervous system during sensorimotor tasks with inherent uncertainty has been shown to be consistent with Bayesian integration. Bayes optimal decision-makers are, however, risk-neutral in the sense that they weigh all possibilities based on prior expectation and sensory evidence when they choose the action with highest expected value. In contrast, risk-sensitive decision-makers are sensitive to model uncertainty and bias their decision-making processes when they do inference over unobserved variables. In particular, they allow deviations from their probabilistic model in cases where this model makes imprecise predictions. Here we test for risk-sensitivity in a sensorimotor integration task where subjects exhibit Bayesian information integration when they infer the position of a target from noisy sensory feedback. When introducing a cost associated with subjects' response, we found that subjects exhibited a characteristic bias towards low cost responses when their uncertainty was high. This result is in accordance with risk-sensitive decision-making processes that allow for deviations from Bayes optimal decision-making in the face of uncertainty. Our results suggest that both Bayesian integration and risk-sensitivity are important factors to understand sensorimotor integration in a quantitative fashion.
Author Summary
Statistically optimal decision-makers use probabilistic predictive models of their environment to achieve their goals. However, in real life such probabilistic models can be wrong or only approximately true, in which case basing decisions exclusively on the statistics of such models can constitute a problematic decision criterion. In contrast, risk-sensitive decision-makers can take model uncertainty into account. They allow deviations from their probabilistic model depending on the quality of the predictions of the model. In particular, they trust their model less if it makes imprecise predictions and bias their decisions towards worst-case or best-case outcomes. Here we designed a sensorimotor task where subjects exhibit Bayesian information integration when they infer the hidden location of a target and they had to decide to make a more or less costly movement. We found that subjects exhibited a bias with respect to the statistically optimal movement towards less costly outcomes, the higher the uncertainty about the target location was. This interplay between estimation uncertainty and movement cost is consistent with a risk-sensitive decision criterion that takes model uncertainty into account.
doi:10.1371/journal.pcbi.1002698
PMCID: PMC3459842  PMID: 23028294
22.  Expert Prior Elicitation and Bayesian Analysis of the Mycotic Ulcer Treatment Trial I 
Purpose.
To perform a Bayesian analysis of the Mycotic Ulcer Treatment Trial I (MUTT I) using expert opinion as a prior belief.
Methods.
MUTT I was a randomized clinical trial comparing topical natamycin or voriconazole for treating filamentous fungal keratitis. A questionnaire elicited expert opinion on the best treatment of fungal keratitis before MUTT I results were available. A Bayesian analysis was performed using the questionnaire data as a prior belief and the MUTT I primary outcome (3-month visual acuity) by frequentist analysis as a likelihood.
Results.
Corneal experts had a 41.1% prior belief that natamycin improved 3-month visual acuity compared with voriconazole. The Bayesian analysis found a 98.4% belief for natamycin treatment compared with voriconazole treatment for filamentous cases as a group (mean improvement 1.1 Snellen lines, 95% credible interval 0.1–2.1). The Bayesian analysis estimated a smaller treatment effect than the MUTT I frequentist analysis result of 1.8-line improvement with natamycin versus voriconazole (95% confidence interval 0.5–3.0, P = 0.006). For Fusarium cases, the posterior demonstrated a 99.7% belief for natamycin treatment, whereas non-Fusarium cases had a 57.3% belief.
Conclusions.
The Bayesian analysis suggests that natamycin is superior to voriconazole when filamentous cases are analyzed as a group. Subgroup analysis of Fusarium cases found improvement with natamycin compared with voriconazole, whereas there was almost no difference between treatments for non-Fusarium cases. These results were consistent with, though smaller in effect size than, the MUTT I primary outcome by frequentist analysis. The accordance between analyses further validates the trial results. (ClinicalTrials.gov number, NCT00996736.)
We elicited the opinions of corneal specialists on treating filamentous fungal keratitis, to perform a Bayesian analysis of the Mycotic Ulcer Treatment Trial I. The Bayesian analysis result was consistent with, but suggested a smaller treatment effect than, the frequentist result.
doi:10.1167/iovs.13-11716
PMCID: PMC3684218  PMID: 23702779
fungal keratitis; corneal ulceration; clinical trial; Bayesian; statistics
23.  Comparison of population-averaged and cluster-specific models for the analysis of cluster randomized trials with missing binary outcomes: a simulation study 
Abstracts
Background
The objective of this simulation study is to compare the accuracy and efficiency of population-averaged (i.e. generalized estimating equations (GEE)) and cluster-specific (i.e. random-effects logistic regression (RELR)) models for analyzing data from cluster randomized trials (CRTs) with missing binary responses.
Methods
In this simulation study, clustered responses were generated from a beta-binomial distribution. The number of clusters per trial arm, the number of subjects per cluster, intra-cluster correlation coefficient, and the percentage of missing data were allowed to vary. Under the assumption of covariate dependent missingness, missing outcomes were handled by complete case analysis, standard multiple imputation (MI) and within-cluster MI strategies. Data were analyzed using GEE and RELR. Performance of the methods was assessed using standardized bias, empirical standard error, root mean squared error (RMSE), and coverage probability.
Results
GEE performs well on all four measures — provided the downward bias of the standard error (when the number of clusters per arm is small) is adjusted appropriately — under the following scenarios: complete case analysis for CRTs with a small amount of missing data; standard MI for CRTs with variance inflation factor (VIF) <3; within-cluster MI for CRTs with VIF≥3 and cluster size>50. RELR performs well only when a small amount of data was missing, and complete case analysis was applied.
Conclusion
GEE performs well as long as appropriate missing data strategies are adopted based on the design of CRTs and the percentage of missing data. In contrast, RELR does not perform well when either standard or within-cluster MI strategy is applied prior to the analysis.
doi:10.1186/1471-2288-13-9
PMCID: PMC3560270  PMID: 23343209
Marginal model; Population-averaged model; Cluster-specific model; Multiple imputation; Cluster randomized trial; Covariate dependent missingness; Generalized estimating equations; Random-effects logistic regression
24.  Optimism as a Prior Belief about the Probability of Future Reward 
PLoS Computational Biology  2014;10(5):e1003605.
Optimists hold positive a priori beliefs about the future. In Bayesian statistical theory, a priori beliefs can be overcome by experience. However, optimistic beliefs can at times appear surprisingly resistant to evidence, suggesting that optimism might also influence how new information is selected and learned. Here, we use a novel Pavlovian conditioning task, embedded in a normative framework, to directly assess how trait optimism, as classically measured using self-report questionnaires, influences choices between visual targets, by learning about their association with reward progresses. We find that trait optimism relates to an a priori belief about the likelihood of rewards, but not losses, in our task. Critically, this positive belief behaves like a probabilistic prior, i.e. its influence reduces with increasing experience. Contrary to findings in the literature related to unrealistic optimism and self-beliefs, it does not appear to influence the iterative learning process directly.
Author Summary
The optimism bias is regarded as one of the most prevalent and robust cognitive biases documented in psychology and behavioral economics. In individuals, trait optimism is usually measured using self-report questionnaires. However, choices in simple behavioral tasks can also be used to infer how optimistic people are in practice. We asked human subjects to fill in questionnaires about trait optimism, then to participate in a behavioral experiment where they needed to infer the likelihood of visual targets to be associated with a reward. Using modeling, we could then quantify the link between self-report trait optimism and decision or learning biases. We find that people who report that they are optimistic have a positive a priori bias on the likelihood of future reward, whose influence reduces with experience. In our task, trait optimism doesn't distort how new information is integrated: subjects update their estimates similarly following information that is better or worse than expected.
doi:10.1371/journal.pcbi.1003605
PMCID: PMC4031045  PMID: 24853098
25.  Filling-In and Suppression of Visual Perception from Context: A Bayesian Account of Perceptual Biases by Contextual Influences 
PLoS Computational Biology  2008;4(2):e14.
Visual object recognition and sensitivity to image features are largely influenced by contextual inputs. We study influences by contextual bars on the bias to perceive or infer the presence of a target bar, rather than on the sensitivity to image features. Human observers judged from a briefly presented stimulus whether a target bar of a known orientation and shape is present at the center of a display, given a weak or missing input contrast at the target location with or without a context of other bars. Observers are more likely to perceive a target when the context has a weaker rather than stronger contrast. When the context can perceptually group well with the would-be target, weak contrast contextual bars bias the observers to perceive a target relative to the condition without contexts, as if to fill in the target. Meanwhile, high-contrast contextual bars, regardless of whether they group well with the target, bias the observers to perceive no target. A Bayesian model of visual inference is shown to account for the data well, illustrating that the context influences the perception in two ways: (1) biasing observers' prior belief that a target should be present according to visual grouping principles, and (2) biasing observers' internal model of the likely input contrasts caused by a target bar. According to this model, our data suggest that the context does not influence the perceived target contrast despite its influence on the bias to perceive the target's presence, thereby suggesting that cortical areas beyond the primary visual cortex are responsible for the visual inferences.
Author Summary
We study how visual perception of a target bar can be biased by contextual bars in the image, and how a Bayesian model of object inference can account for the data. Human observers are more likely to perceive a target bar when the contextual contrast, i.e., the luminance difference between the contextual bars and background, is weaker rather than stronger. Relative to the situation without the context, they are biased to perceive the target in a context of weak contrast when the target can perceptually group well with the context, as if the context fills in the target. Meanwhile, they are biased not to perceive the target in a context of strong contrast, as if the context suppresses the perception, regardless of whether it could perceptually group well with the would-be target. The Bayesian model illustrates that the context influences the perception by biasing (1) observers' prior belief that a target should be present and (2) observers' internal model of the likely input contrasts from a target bar. Our data suggest that brain areas beyond the primary visual cortex along the visual pathway are responsible for inferring object causes for input images.
doi:10.1371/journal.pcbi.0040014
PMCID: PMC2242827  PMID: 18282080

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