A fundamental aspect of epidemiological studies concerns the estimation of factor-outcome associations to identify risk factors, prognostic factors and potential causal factors. Because reliable estimates for these associations are important, there is a growing interest in methods for combining the results from multiple studies in individual participant data meta-analyses (IPD-MA). When there is substantial heterogeneity across studies, various random-effects meta-analysis models are possible that employ a one-stage or two-stage method. These are generally thought to produce similar results, but empirical comparisons are few.
We describe and compare several one- and two-stage random-effects IPD-MA methods for estimating factor-outcome associations from multiple risk-factor or predictor finding studies with a binary outcome. One-stage methods use the IPD of each study and meta-analyse using the exact binomial distribution, whereas two-stage methods reduce evidence to the aggregated level (e.g. odds ratios) and then meta-analyse assuming approximate normality. We compare the methods in an empirical dataset for unadjusted and adjusted risk-factor estimates.
Though often similar, on occasion the one-stage and two-stage methods provide different parameter estimates and different conclusions. For example, the effect of erythema and its statistical significance was different for a one-stage (OR = 1.35, ) and univariate two-stage (OR = 1.55, ). Estimation issues can also arise: two-stage models suffer unstable estimates when zero cell counts occur and one-stage models do not always converge.
When planning an IPD-MA, the choice and implementation (e.g. univariate or multivariate) of a one-stage or two-stage method should be prespecified in the protocol as occasionally they lead to different conclusions about which factors are associated with outcome. Though both approaches can suffer from estimation challenges, we recommend employing the one-stage method, as it uses a more exact statistical approach and accounts for parameter correlation.
The aim of this individual participant data (IPD) meta-analysis is to assess whether the effects of repeat prenatal corticosteroid treatment given to women at risk of preterm birth to benefit their babies are modified in a clinically meaningful way by factors related to the women or the trial protocol.
The Prenatal Repeat Corticosteroid International IPD Study Group: assessing the effects using the best level of Evidence (PRECISE) Group will conduct an IPD meta-analysis. The PRECISE International Collaborative Group was formed in 2010 and data collection commenced in 2011. Eleven trials with up to 5,000 women and 6,000 infants are eligible for the PRECISE IPD meta-analysis. The primary study outcomes for the infants will be serious neonatal outcome (defined by the PRECISE International IPD Study Group as one of death (foetal, neonatal or infant); severe respiratory disease; severe intraventricular haemorrhage (grade 3 and 4); chronic lung disease; necrotising enterocolitis; serious retinopathy of prematurity; and cystic periventricular leukomalacia); use of respiratory support (defined as mechanical ventilation or continuous positive airways pressure or other respiratory support); and birth weight (Z-scores). For the children, the primary study outcomes will be death or any neurological disability (however defined by trialists at childhood follow up and may include developmental delay or intellectual impairment (developmental quotient or intelligence quotient more than one standard deviation below the mean), cerebral palsy (abnormality of tone with motor dysfunction), blindness (for example, corrected visual acuity worse than 6/60 in the better eye) or deafness (for example, hearing loss requiring amplification or worse)). For the women, the primary outcome will be maternal sepsis (defined as chorioamnionitis; pyrexia after trial entry requiring the use of antibiotics; puerperal sepsis; intrapartum fever requiring the use of antibiotics; or postnatal pyrexia).
Data analyses are expected to commence in 2011 with results publicly available in 2012.
Diabetes–related lower limb amputations are associated with considerable morbidity and mortality and are usually preceded by foot ulceration. The available systematic reviews of aggregate data are compromised because the primary studies report both adjusted and unadjusted estimates. As adjusted meta-analyses of aggregate data can be challenging, the best way to standardise the analytical approach is to conduct a meta-analysis based on individual patient data (IPD).
There are however many challenges and fundamental methodological omissions are common; protocols are rare and the assessment of the risk of bias arising from the conduct of individual studies is frequently not performed, largely because of the absence of widely agreed criteria for assessing the risk of bias in this type of review. In this protocol we propose key methodological approaches to underpin our IPD systematic review of prognostic factors of foot ulceration in diabetes.
1. What are the most highly prognostic factors for foot ulceration (i.e. symptoms, signs, diagnostic tests) in people with diabetes?
2. Can the data from each study be adjusted for a consistent set of adjustment factors?
3. Does the model accuracy change when patient populations are stratified according to demographic and/or clinical characteristics?
MEDLINE and EMBASE databases from their inception until early 2012 were searched and the corresponding authors of all eligible primary studies invited to contribute their raw data. We developed relevant quality assurance items likely to identify occasions when study validity may have been compromised from several sources. A confidentiality agreement, arrangements for communication and reporting as well as ethical and governance considerations are explained.
We have agreement from the corresponding authors of all studies which meet the eligibility criteria and they collectively possess data from more than 17000 patients. We propose, as a provisional analysis plan, to use a multi-level mixed model, using “study” as one of the levels. Such a model can also allow for the within-patient clustering that occurs if a patient contributes data from both feet, although to aid interpretation, we prefer to use patients rather than feet as the unit of analysis. We intend to only attempt this analysis if the results of the investigation of heterogeneity do not rule it out and the model diagnostics are acceptable.
This review is central to the development of a global evidence-based strategy for the risk assessment of the foot in patients with diabetes, ensuring future recommendations are valid and can reliably inform international clinical guidelines.
Individual participant data (IPD) meta-analyses that obtain “raw” data from studies rather than summary data typically adopt a “two-stage” approach to analysis whereby IPD within trials generate summary measures, which are combined using standard meta-analytical methods. Recently, a range of “one-stage” approaches which combine all individual participant data in a single meta-analysis have been suggested as providing a more powerful and flexible approach. However, they are more complex to implement and require statistical support. This study uses a dataset to compare “two-stage” and “one-stage” models of varying complexity, to ascertain whether results obtained from the approaches differ in a clinically meaningful way.
Methods and Findings
We included data from 24 randomised controlled trials, evaluating antiplatelet agents, for the prevention of pre-eclampsia in pregnancy. We performed two-stage and one-stage IPD meta-analyses to estimate overall treatment effect and to explore potential treatment interactions whereby particular types of women and their babies might benefit differentially from receiving antiplatelets. Two-stage and one-stage approaches gave similar results, showing a benefit of using anti-platelets (Relative risk 0.90, 95% CI 0.84 to 0.97). Neither approach suggested that any particular type of women benefited more or less from antiplatelets. There were no material differences in results between different types of one-stage model.
For these data, two-stage and one-stage approaches to analysis produce similar results. Although one-stage models offer a flexible environment for exploring model structure and are useful where across study patterns relating to types of participant, intervention and outcome mask similar relationships within trials, the additional insights provided by their usage may not outweigh the costs of statistical support for routine application in syntheses of randomised controlled trials. Researchers considering undertaking an IPD meta-analysis should not necessarily be deterred by a perceived need for sophisticated statistical methods when combining information from large randomised trials.
Individual participant data (IPD) meta-analyses often analyze their IPD as if coming from a single study. We compare this approach with analyses that rather account for clustering of patients within studies.
Study Design and Setting
Comparison of effect estimates from logistic regression models in real and simulated examples.
The estimated prognostic effect of age in patients with traumatic brain injury is similar, regardless of whether clustering is accounted for. However, a family history of thrombophilia is found to be a diagnostic marker of deep vein thrombosis [odds ratio, 1.30; 95% confidence interval (CI): 1.00, 1.70; P = 0.05] when clustering is accounted for but not when it is ignored (odds ratio, 1.06; 95% CI: 0.83, 1.37; P = 0.64). Similarly, the treatment effect of nicotine gum on smoking cessation is severely attenuated when clustering is ignored (odds ratio, 1.40; 95% CI: 1.02, 1.92) rather than accounted for (odds ratio, 1.80; 95% CI: 1.29, 2.52). Simulations show models accounting for clustering perform consistently well, but downwardly biased effect estimates and low coverage can occur when ignoring clustering.
Researchers must routinely account for clustering in IPD meta-analyses; otherwise, misleading effect estimates and conclusions may arise.
Individual participant data meta-analysis; Individual patient data; Evidence synthesis; Cluster; Simulation; Binary outcome; Pooled analysis
A common and potentially life-threatening complication of the treatment of childhood cancer is infection, which frequently presents as fever with neutropenia. The standard management of such episodes is the extensive use of intravenous antibiotics, and though it produces excellent survival rates of over 95%, it greatly inconveniences the three-fourths of patients who do not require such aggressive treatment. There have been a number of studies which have aimed to develop risk prediction models to stratify treatment. Individual participant data (IPD) meta-analysis in therapeutic studies has been developed to improve the precision and reliability of answers to questions of treatment effect and recently have been suggested to be used to answer questions regarding prognosis and diagnosis to gain greater power from the frequently small individual studies.
In the IPD protocol, we will collect and synthesise IPD from multiple studies and examine the outcomes of episodes of febrile neutropenia as a consequence of their treatment for malignant disease. We will develop and evaluate a risk stratification model using hierarchical regression models to stratify patients by their risk of experiencing adverse outcomes during an episode. We will also explore specific practical and methodological issues regarding adaptation of established techniques of IPD meta-analysis of interventions for use in synthesising evidence derived from IPD from multiple studies for use in predictive modelling contexts.
Our aim in using this model is to define a group of individuals at low risk for febrile neutropenia who might be treated with reduced intensity or duration of antibiotic therapy and so reduce the inconvenience and cost of these episodes, as well as to define a group of patients at very high risk of complications who could be subject to more intensive therapies. The project will also help develop methods of IPD predictive modelling for use in future studies of risk prediction.
individual participant data meta-analysis; predictive modelling; paediatric oncology; febrile neutropenia; collaborative studies
Raw data from 3 similar clinical trials were analyzed in this individual participant data (IPD) meta-analysis to define any possible efficacy of intravesical 2% chondroitin sulphate in IC/BPS.
We pooled IPD from an open label and 2 small randomized placebo controlled trials assessing chondroitin sulphate in IC/BPS (similar inclusion/exclusion criteria, treatment, outcome assessment). Our primary objective was to compare rates of global response assessment (GRA) responsiveness between chondroitin sulphate and vehicle control. Secondary objectives compared the Interstitial Cystitis Symptom/Problem Index (ICSI/PI) total score and improvement rates, and average daily urine frequency. The treatment response was calculated for individual trials and pooled data using IPD meta-analysis for pooling proportions.
In total, 213 patients were included in the pooling analysis. At the end of the treatment period, the overall GRA response rates were 43.2 (95% CI: 35.0, 51.5) and 27.4 (95% CI: 17.6, 37.2) for the chondroitin sulphate and vehicle control groups, respectively. Pooled RR was 1.55 (p = 0.014, 95% CI: 1.09, 2.22). The chance of being an ICSI responder was similarly 54% higher in the chondroitin sulphate group. The small decrease in total ICSI score and urine frequency between the two groups was less impressive (−0.8 and −0.5 points respectively) and not statistically significant.
Benefits from intravesical chondroitin sulphate treatment in IC/BPS patients can be confirmed by increasing the power of the available data using an IPD meta-analytical approach. However, disconnect between response rates and severity scores underline the importance of choosing the right patient for this organ-specific treatment.
The primary aim of this study is to assess, using individual participant data (IPD) meta-analysis, the effects of administration of antenatal magnesium sulphate given to women at risk of preterm birth on important clinical outcomes for their child such as death and neurosensory disability. The secondary aim is to determine whether treatment effects differ depending on important pre-specified participant and treatment characteristics, such as reasons at risk of preterm birth, gestational age, or type, dose and mode of administration of magnesium sulphate.
The Antenatal Magnesium Individual Participant Data (IPD) International Collaboration: assessing the benefits for babies using the best level of evidence (AMICABLE) Group will perform an IPD meta-analysis to answer these important clinical questions.
The AMICABLE Group was formed in 2009 with data collection commencing late 2010.
Five trials involving a total 6,145 babies are eligible for inclusion in the IPD meta-analysis.
Primary study outcomes
For the infants/children: Death or cerebral palsy. For the women: Any severe maternal outcome potentially related to treatment (death, respiratory arrest or cardiac arrest).
Results are expected to be publicly available in 2012.
Preterm birth; Magnesium sulphate; Individual patient data; Cerebral palsy; Meta-analysis
An Individual Patient Data (IPD) meta-analysis is often considered the gold-standard for synthesising survival data from clinical trials. An IPD meta-analysis can be achieved by either a two-stage or a one-stage approach, depending on whether the trials are analysed separately or simultaneously. A range of one-stage hierarchical Cox models have been previously proposed, but these are known to be computationally intensive and are not currently available in all standard statistical software. We describe an alternative approach using Poisson based Generalised Linear Models (GLMs).
We illustrate, through application and simulation, the Poisson approach both classically and in a Bayesian framework, in two-stage and one-stage approaches. We outline the benefits of our one-stage approach through extension to modelling treatment-covariate interactions and non-proportional hazards. Ten trials of hypertension treatment, with all-cause death the outcome of interest, are used to apply and assess the approach.
We show that the Poisson approach obtains almost identical estimates to the Cox model, is additionally computationally efficient and directly estimates the baseline hazard. Some downward bias is observed in classical estimates of the heterogeneity in the treatment effect, with improved performance from the Bayesian approach.
Our approach provides a highly flexible and computationally efficient framework, available in all standard statistical software, to the investigation of not only heterogeneity, but the presence of non-proportional hazards and treatment effect modifiers.
Properly performed systematic reviews and meta-analyses are thought by many to represent among the highest level of evidence addressing important clinical issues. Few would disagree that meta-analyses based on individual patient data (IPD) offer several advantages and represent the standard to which all other systematic reviews should be compared.
All cancer-related meta-analyses cited in Medline were classified as based on aggregate or individual patient data. A review was then undertaken of all reports comparing the comparative strengths and limitations of meta-analyses using either aggregate or individual patient data.
The majority of published meta-analyses are based on summary or aggregate patient data (APD). Reasons suggested for this include the considerable resources, years of study and often, broad international cooperation required for IPD meta-analyses. Many of the most important features of systematic reviews including formal meta-analyses are addressed by both IPD and APD meta-analyses. The need for defining an explicit and relevant clinical question, exhaustively searching for the totality of evidence, meticulous and unbiased data transfer or extraction, assessment of between study heterogeneity and the use of appropriate statistical methods for estimating summary effect measures are essentially the same for the two approaches.
IPD offers advantages and, when feasible, should be considered the best opportunity to summarize the results of multiple studies. However, the resources, time and cooperation required for such studies will continue to limit their use in many important areas of clinical medicine which can be meaningfully and cost-effectively approached by properly performed APD meta-analyses. APD meta-analyses continue to be the mainstay of systematic reviews utilized by the US Preventive Services Task Force, the Cochrane Collaboration and many professional societies to support clinical practice guidelines.
This article provides a unified methodology of meta-analysis that synthesizes medical evidence by using both available individual patient data (IPD) and published summary statistics within the framework of likelihood principle. Most up-to-date scientific evidence on medicine is crucial information not only to consumers but also to decision makers, and can only be obtained when existing evidence from the literature and the most recent individual patient data are optimally synthesized. We propose a general linear mixed effects model to conduct meta-analyses when individual patient data are only available for some of the studies and summary statistics have to be used for the rest of the studies. Our approach includes both the traditional meta-analyses in which only summary statistics are available for all studies and the other extreme case in which individual patient data are available for all studies as special examples. We implement the proposed model with statistical procedures from standard computing packages. We provide measures of heterogeneity based on the proposed model. Finally, we demonstrate the proposed methodology through a real life example studying the cerebrospinal fluid biomarkers to identify individuals with high risk of developing Alzheimer’s disease when they are still cognitively normal.
confidence interval; general linear mixed effects model; heterogeneity index; individual patient data; maximum likelihood estimate (MLE); meta-analyses
Since our individual patient data (IPD) meta-analysis (MA) of supportive care and chemotherapy for non–small-cell lung cancer (NSCLC), published in 1995, many trials have been completed. An updated, IPD MA has been carried out to assess newer regimens and determine conclusively the effect of chemotherapy.
Systematic searches for randomized controlled trials (RCTs) were undertaken, followed by central collection, checking, and reanalysis of updated IPD. Results from RCTs were combined to calculate individual and pooled hazard ratios (HRs).
Data were obtained from 2,714 patients from 16 RCTs. There were 1,293 deaths among 1,399 patients assigned supportive care and chemotherapy and 1,240 among 1,315 assigned supportive care alone. Results showed a significant benefit of chemotherapy (HR, 0.77; 95% CI, 0.71 to 0.83; P ≤ .0001), equivalent to a relative increase in survival of 23% or an absolute improvement in survival of 9% at 12 months, increasing survival from 20% to 29%. There was no clear evidence that this effect was influenced by the drugs used (P = .63) or whether they were used as single agents or in combination (P = .40). Despite changes in patient demographics, the effect of chemotherapy in recent trials did not differ from those included previously (P = .77). There was no clear evidence of a difference or trend in the relative effect of chemotherapy across patient subgroups.
This MA of chemotherapy in the supportive care setting demonstrates conclusively that chemotherapy improves overall survival in all patients with advanced NSCLC. Therefore, all patients who are fit enough and wish to receive chemotherapy should do so.
Based on small to moderate effect sizes for the wide range of symptomatic treatments in osteoarthritis (OA), and on the heterogeneity of OA patients, treatment guidelines for OA have stressed the need for research on clinical predictors of response to different treatments. A meta-analysis to quantify the effect modified by the predictors using individual patient data (IPD) is suggested. The initiative to collect and analyze IPD in OA research is commenced by the OA Trial Bank. The study aims are therefore: to evaluate the efficacy of intra-articular glucocorticoids for knee or hip OA in specific subgroups of patients with severe pain and (mild) inflammatory signs, over both short-term and long-term follow-up, using IPD from existing studies; to reach consensus on the rules for cooperation in a consortium; and to develop and explore the methodological issues of meta-analysis with individual OA patient data.
For the current IPD analysis we will collect and synthesize IPD from randomized trials studying the effect of intra-articular glucocorticoid injections in patients with hip or knee OA. Subgroup analyses will be performed for the primary outcome of pain at both short-term and long-term follow-up, in the subgroups of patients with and without severe pain and with and without inflammatory signs.
This study protocol includes the first study of the OA Trial Bank, an international collaboration that initiates meta-analyses on predefined subgroups of OA patients from existing literature. This approach ensures a widely supported initiative and is therefore likely to be successful in data collection of existing trials. The collaboration developed (that is, the OA Trial Bank) may also lead to future IPD analyses on subgroups of patients with several intervention strategies applied in OA patients.
Osteoarthritis; Individual patient data; Corticosteroid injection
It is unknown whether women derive comparable benefits and have a similar safety and tolerability profile as men from acamprosate, a widely prescribed drug for the maintenance of abstinence in alcohol dependence. The objective of this study was to assess sex-specific differences in the efficacy, safety and tolerability of acamprosate in the treatment of women and men with alcohol dependence.
A sex-specific meta-analysis was conducted based on individual patient data (IPD). Data were obtained from double-blind, randomized controlled trials (RCTs) with quantitative drinking measures in patients with alcohol dependence receiving oral acamprosate or placebo. Sources included PubMed, PsychInfo and Cochrane electronic databases; reference lists from retrieved articles and presentations at professional meetings; and direct access to authors and companies who provided IPD.
Individual records were obtained on 1,317 women and 4,794 men who participated in 22 eligible studies conducted in 18 countries. IPD meta-analyses found a significant beneficial effect of acamprosate relative to placebo across all 4 efficacy endpoints: an incremental gain of 10.4% (95% CI 7.1 to 13.7, p<.001) in percent of abstinent days, an incremental gain of 11.0% (7.4 to 14.6, p<.001) in percent of no heavy drinking days, an odds ratio of 1.9 (1.6 to 2.2, p<.001) for rate of complete abstinence and an odds ratio of 1.9 (1.6 to 2.3, p<.001) for rate of no heavy drinking, over the study duration. Acamprosate was also associated with significantly higher rates of treatment completion (p=0.004) and medication compliance (p<.001) than placebo. Men and women did not differ on any measure of acamprosate efficacy, safety or tolerability.
This sex-specific IPD meta-analysis provides evidence that acamprosate has a significant effect compared with placebo in improving rates of abstinence and no heavy drinking in both women and men with alcohol dependence. Further, acamprosate was associated with significantly higher rates of treatment completion and medication compliance than placebo among both women and men, and had a comparable safety and tolerability profile.
acamprosate; women; alcoholism treatment; individual patient data; meta-analysis
Efforts to prevent the development of overweight and obesity have increasingly focused early in the life course as we recognise that both metabolic and behavioural patterns are often established within the first few years of life. Randomised controlled trials (RCTs) of interventions are even more powerful when, with forethought, they are synthesised into an individual patient data (IPD) prospective meta-analysis (PMA). An IPD PMA is a unique research design where several trials are identified for inclusion in an analysis before any of the individual trial results become known and the data are provided for each randomised patient. This methodology minimises the publication and selection bias often associated with a retrospective meta-analysis by allowing hypotheses, analysis methods and selection criteria to be specified a priori.
The Early Prevention of Obesity in CHildren (EPOCH) Collaboration was formed in 2009. The main objective of the EPOCH Collaboration is to determine if early intervention for childhood obesity impacts on body mass index (BMI) z scores at age 18-24 months. Additional research questions will focus on whether early intervention has an impact on children's dietary quality, TV viewing time, duration of breastfeeding and parenting styles. This protocol includes the hypotheses, inclusion criteria and outcome measures to be used in the IPD PMA. The sample size of the combined dataset at final outcome assessment (approximately 1800 infants) will allow greater precision when exploring differences in the effect of early intervention with respect to pre-specified participant- and intervention-level characteristics.
Finalisation of the data collection procedures and analysis plans will be complete by the end of 2010. Data collection and analysis will occur during 2011-2012 and results should be available by 2013.
Trial registration number
The Immuno Polymorphism Database (IPD) (http://www.ebi.ac.uk/ipd/) is a set of specialist databases related to the study of polymorphic genes in the immune system. The IPD project works with specialist groups or nomenclature committees who provide and curate individual sections before they are submitted to IPD for online publication. The IPD project stores all the data in a set of related databases. IPD currently consists of four databases: IPD-KIR, contains the allelic sequences of Killer-cell Immunoglobulin-like Receptors, IPD-MHC, is a database of sequences of the Major Histocompatibility Complex of different species; IPD-human platelet antigens, alloantigens expressed only on platelets and IPD-ESTDAB, which provides access to the European Searchable Tumour cell-line database, a cell bank of immunologically characterised melanoma cell lines. The data is currently available online from the website and ftp directory.
The Immuno Polymorphism Database (IPD), http://www.ebi.ac.uk/ipd/ is a set of specialist databases related to the study of polymorphic genes in the immune system. The IPD project works with specialist groups or nomenclature committees who provide and curate individual sections before they are submitted to IPD for online publication. The IPD project stores all the data in a set of related databases. IPD currently consists of four databases: IPD-KIR, contains the allelic sequences of killer-cell immunoglobulin-like receptors, IPD-MHC, a database of sequences of the major histocompatibility complex of different species; IPD-HPA, alloantigens expressed only on platelets; and IPD-ESTDAB, which provides access to the European Searchable Tumour Cell-Line Database, a cell bank of immunologically characterized melanoma cell lines. The data is currently available online from the website and FTP directory. This article describes the latest updates and additional tools added to the IPD project.
The 92 capsular serotypes of Streptococcus pneumoniae differ greatly in nasopharyngeal carriage prevalence, invasiveness and disease incidence. There has been some debate, though, as to whether serotype independently affects the outcome of invasive pneumococcal disease (IPD). Published studies have shown variable results with regards to case-fatality ratios for specific serotypes and the role of host factors in affecting these relationships. We evaluated whether risk of death from IPD is a stable serotype-associated property across studies, and then compared the pooled effect estimates with epidemiologic and biological correlates.
We performed a systematic review and meta-analysis of serotype-specific disease outcome for pneumonia and meningitis cases. Study-specific estimates of risk of death (risk ratio, RR) were pooled from 9 studies that provided serotype-specific data on pneumonia and meningitis using a random-effects method with serotype 14 as the reference. Pooled RRs were compared to RRs from adult cases with low co-morbidity scores to evaluate potential confounding by host factors.
There were significant differences in the RR estimates between serotypes among bacteremic pneumonia cases. Overall, types 1, 7F and 8 were associated with decreased RRs and types 3, 6A, 6B, 9N and 19F were associated with increased RRs. Outcomes among meningitis cases did not differ significantly between types. Serotypes with increased RRs tended to have a high carriage prevalence, low invasiveness, and were more heavily encapsulated in vitro. These results suggest that IPD outcome, like other epidemiologic measures, is a stable serotype-associated property.
Serotype; pneumococcus; case-fatality ratio; mortality; capsule; meta-analysis
Job strain (i.e., high job demands combined with low job control) is a frequently used indicator of harmful work stress, but studies have often used partial versions of the complete multi-item job demands and control scales. Understanding whether the different instruments assess the same underlying concepts has crucial implications for the interpretation of findings across studies, harmonisation of multi-cohort data for pooled analyses, and design of future studies. As part of the 'IPD-Work' (Individual-participant-data meta-analysis in working populations) consortium, we compared different versions of the demands and control scales available in 17 European cohort studies.
Six of the 17 studies had information on the complete scales and 11 on partial scales. Here, we analyse individual level data from 70 751 participants of the studies which had complete scales (5 demand items, 6 job control items).
We found high Pearson correlation coefficients between complete scales of job demands and control relative to scales with at least three items (r > 0.90) and for partial scales with two items only (r = 0.76-0.88). In comparison with scores from the complete scales, the agreement between job strain definitions was very good when only one item was missing in either the demands or the control scale (kappa > 0.80); good for job strain assessed with three demand items and all six control items (kappa > 0.68) and moderate to good when items were missing from both scales (kappa = 0.54-0.76). The sensitivity was > 0.80 when only one item was missing from either scale, decreasing when several items were missing in one or both job strain subscales.
Partial job demand and job control scales with at least half of the items of the complete scales, and job strain indices based on one complete and one partial scale, seemed to assess the same underlying concepts as the complete survey instruments.
Job demands; Job control; Job strain; Work stress; Agreement
In systematic reviews and meta-analyses, time-to-event outcomes are most appropriately analysed using hazard ratios (HRs). In the absence of individual patient data (IPD), methods are available to obtain HRs and/or associated statistics by carefully manipulating published or other summary data. Awareness and adoption of these methods is somewhat limited, perhaps because they are published in the statistical literature using statistical notation.
This paper aims to 'translate' the methods for estimating a HR and associated statistics from published time-to-event-analyses into less statistical and more practical guidance and provide a corresponding, easy-to-use calculations spreadsheet, to facilitate the computational aspects.
A wider audience should be able to understand published time-to-event data in individual trial reports and use it more appropriately in meta-analysis. When faced with particular circumstances, readers can refer to the relevant sections of the paper. The spreadsheet can be used to assist them in carrying out the calculations.
The methods cannot circumvent the potential biases associated with relying on published data for systematic reviews and meta-analysis. However, this practical guide should improve the quality of the analysis and subsequent interpretation of systematic reviews and meta-analyses that include time-to-event outcomes.
Mesothelin is currently considered the best available serum biomarker of malignant pleural mesothelioma. To examine the diagnostic accuracy and use of serum mesothelin in early diagnosis, we performed an individual patient data (IPD) meta-analysis.
The literature search identified 16 diagnostic studies of serum mesothelin, measured with the Mesomark enzyme-linked immunosorbent assay. IPD of 4,491 individuals were collected, including several control groups and 1,026 patients with malignant pleural mesothelioma. Mesothelin levels were standardized for between-study differences and age, after which the diagnostic accuracy and the factors affecting it were examined with receiver operating characteristic (ROC) regression analysis.
At a common diagnostic threshold of 2.00 nmol/L, the sensitivities and specificities of mesothelin in the different studies ranged widely from 19% to 68% and 88% to 100%, respectively. This heterogeneity can be explained by differences in study population, because type of control group, mesothelioma stage, and histologic subtype significantly affected the diagnostic accuracy. The use of mesothelin in early diagnosis was evaluated by differentiating 217 patients with stage I or II epithelioid and biphasic mesothelioma from 1,612 symptomatic or high-risk controls. The resulting area under the ROC curve was 0.77 (95% CI, 0.73 to 0.81). At 95% specificity, mesothelin displayed a sensitivity of 32% (95% CI, 26% to 40%).
In patients suspected of having mesothelioma, a positive blood test for mesothelin at a high-specificity threshold is a strong incentive to urge further diagnostic steps. However, the poor sensitivity of mesothelin clearly limits its added value to early diagnosis and emphasizes the need for further biomarker research.
Low back pain (LBP) is one of the leading causes of disability and has a major socioeconomic impact. Despite a large amount of research in the field, there remains uncertainty about the best treatment approach for chronic LBP, and identification of relevant patient subgroups is an important goal. Exercise therapy is a commonly used strategy to treat chronic low back pain and is one of several interventions that evidence suggests is moderately effective.
In parallel with an update of the 2005 Cochrane review, we will undertake an individual participant data (IPD) meta-analysis, which will allow us to standardize analyses across studies and directly derive results, and to examine differential treatment effects across individuals to estimate how patients’ characteristics modify treatment benefit.
We will use standard systematic review methods advocated by the Cochrane Collaboration to identify relevant trials. We will include trials evaluating exercise therapy compared to any or no other interventions in adult non-specific chronic LBP. Our primary outcomes of interest include pain, functional status, and return-to-work/absenteeism. We will assess potential risk of bias for each study meeting selection criteria, using criteria and methods recommended by the Cochrane BRG.
The original individual participant data will be requested from the authors of selected trials having moderate to low risk of bias. We will test original data and compile a master dataset with information about each trial mapped on a pre-specified framework, including reported characteristics of the study sample, exercise therapy characteristics, individual patient characteristics at baseline and all follow-up periods, subgroup and treatment effect modifiers investigated. Our analyses will include descriptive, study-level meta-analysis and meta-regression analyses of the overall treatment effect, and individual-level IPD meta-analyses of treatment effect modification. IPD meta-analyses will be conducted using a one-step approach where the IPD from all studies are modeled simultaneously while accounting for the clustering of participants with studies.
We will analyze IPD across a large number of LBP trials. The resulting larger sample size and consistent presentation of data will allow additional analyses to explore patient-level heterogeneity in treatment outcomes and prognosis of chronic LBP.
Low back pain; Exercise therapy; Meta-analysis; Systematic review
Development of prognostic models enables identification of variables that are influential in predicting patient outcome and the use of these multiple risk factors in a systematic, reproducible way according to evidence based methods. The reliability of models depends on informed use of statistical methods, in combination with prior knowledge of disease. We reviewed published articles to assess reporting and methods used to develop new prognostic models in cancer.
We developed a systematic search string and identified articles from PubMed. Forty-seven articles were included that satisfied the following inclusion criteria: published in 2005; aiming to predict patient outcome; presenting new prognostic models in cancer with outcome time to an event and including a combination of at least two separate variables; and analysing data using multivariable analysis suitable for time to event data.
In 47 studies, prospective cohort or randomised controlled trial data were used for model development in only 33% (15) of studies. In 30% (14) of the studies insufficient data were available, having fewer than 10 events per variable (EPV) used in model development. EPV could not be calculated in a further 40% (19) of the studies. The coding of candidate variables was only reported in 68% (32) of the studies. Although use of continuous variables was reported in all studies, only one article reported using recommended methods of retaining all these variables as continuous without categorisation. Statistical methods for selection of variables in the multivariate modelling were often flawed. A method that is not recommended, namely, using statistical significance in univariate analysis as a pre-screening test to select variables for inclusion in the multivariate model, was applied in 48% (21) of the studies.
We found that published prognostic models are often characterised by both use of inappropriate methods for development of multivariable models and poor reporting. In addition, models are limited by the lack of studies based on prospective data of sufficient sample size to avoid overfitting. The use of poor methods compromises the reliability of prognostic models developed to provide objective probability estimates to complement clinical intuition of the physician and guidelines.
In clinical practice a diagnosis is based on a combination of clinical history, physical examination and additional diagnostic tests. At present, studies on diagnostic research often report the accuracy of tests without taking into account the information already known from history and examination. Due to this lack of information, together with variations in design and quality of studies, conventional meta-analyses based on these studies will not show the accuracy of the tests in real practice. By using individual patient data (IPD) to perform meta-analyses, the accuracy of tests can be assessed in relation to other patient characteristics and allows the development or evaluation of diagnostic algorithms for individual patients.
In this study we will examine these potential benefits in four clinical diagnostic problems in the field of gynaecology, obstetrics and reproductive medicine.
Based on earlier systematic reviews for each of the four clinical problems, studies are considered for inclusion. The first authors of the included studies will be invited to participate and share their original data. After assessment of validity and completeness the acquired datasets are merged. Based on these data, a series of analyses will be performed, including a systematic comparison of the results of the IPD meta-analysis with those of a conventional meta-analysis, development of multivariable models for clinical history alone and for the combination of history, physical examination and relevant diagnostic tests and development of clinical prediction rules for the individual patients. These will be made accessible for clinicians.
The use of IPD meta-analysis will allow evaluating accuracy of diagnostic tests in relation to other relevant information. Ultimately, this could increase the efficiency of the diagnostic work-up, e.g. by reducing the need for invasive tests and/or improving the accuracy of the diagnostic workup. This study will assess whether these benefits of IPD meta-analysis over conventional meta-analysis can be exploited and will provide a framework for future IPD meta-analyses in diagnostic and prognostic research.
Despite an increasing implantation rate of interspinous process distraction (IPD) devices in the treatment of intermittent neurogenic claudication (INC), definitive evidence on the clinical effectiveness of implants is lacking. The main objective of this review was to perform a meta-analysis of all systematic reviews, randomized clinical trials and prospective cohort series to quantify the effectiveness of IPDs and to evaluate the potential side-effects.
Data from all studies prospectively describing clinical results based on validated outcome scales and reporting complications of treatment of patients with INC with IPD placement. We searched MEDLINE, EMBASE, Web of Science, Cochrane (CENTRAL), CINAHL, Academic Search Premier, Science Direct up to July 2010. Studies describing patients with INC caused by lumbar stenosis, reporting complication rate and reporting based on validated outcome scores, were eligible. Studies with only instrumented IPD results were excluded.
Eleven studies eligible studies were identified. Two independently RCTs and eight prospective cohorts were available. In total 563 patients were treated with IPDs. All studies showed improvement in validated outcome scores after 6 weeks and 1 year. Pooled data based on the Zurich Claudication Questionnaire of the RCTs were more in favor of IPD treatment compared with conservative treatment (pooled estimate 23.2, SD 18.5–27.8). Statistical heterogeneity after pooled data was low (I-squared 0.0, p = 0.930). Overall complication rate was 7%.
As the evidence is relatively low and the costs are high, more thorough (cost-) effectiveness studies should be performed before worldwide implementation is introduced.
Degenerative; Lumbar spinal; Stenosis; IPD; Effectiveness; Meta-analysis; Complications