Meta-analysis of individual- and aggregate-level data

Eating Disorders In weight-related Therapy (EDIT) Collaboration: rationale and study design

The cornerstone of obesity treatment is behavioural weight management, resulting in significant improvements in cardio-metabolic and psychosocial health. However, there is ongoing concern that dietary interventions used for weight management may precipitate the development of eating disorders. Systematic reviews demonstrate that, while for most participants medically supervised obesity treatment improves risk scores related to eating disorders, a subset of people who undergo obesity treatment may have poor outcomes for eating disorders. This review summarises the background and rationale for the formation of the Eating Disorders In weight-related Therapy (EDIT) Collaboration. The EDIT Collaboration will explore the complex risk factor interactions that precede changes to eating disorder risk following weight management. In this review, we also outline the programme of work and design of studies for the EDIT Collaboration, including expected knowledge gains. The EDIT studies explore risk factors and the interactions between them using individual-level data from international weight management trials. Combining all available data on eating disorder risk from weight management trials will allow sufficient sample size to interrogate our hypothesis: that individuals undertaking weight management interventions will vary in their eating disorder risk profile, on the basis of personal characteristics and intervention strategies available to them. The collaboration includes the integration of health consumers in project development and translation. An important knowledge gain from this project is a comprehensive understanding of the impact of weight management interventions on eating disorder risk.

Invited Commentary: Combining Information to Answer Epidemiologic Questions About a Target Population

Epidemiologists are attempting to address research questions of increasing complexity by developing novel methods for combining information from diverse sources. Cole et al. (Am J Epidemiol. 2023;192(3)467-474) provide 2 examples of the process of combining information to draw inferences about a population proportion. In this commentary, we consider combining information to learn about a target population as an epidemiologic activity and distinguish it from more conventional meta-analyses. We examine possible rationales for combining information and discuss broad methodological considerations, with an emphasis on study design, assumptions, and sources of uncertainty.

Sharing Medical Big Data While Preserving Patient Confidentiality in Innovative Medicines Initiative: A Summary and Case Report from BigData@Heart

Sharing individual patient data (IPD) is a simple concept but complex to achieve due to data privacy and data security concerns, underdeveloped guidelines, and legal barriers. Sharing IPD is additionally difficult in big data-driven collaborations such as Bigdata@Heart in the Innovative Medicines Initiative, due to competing interests between diverse consortium members. One project within BigData@Heart, case study 1, needed to pool data from seven heterogeneous data sets: five randomized controlled trials from three different industry partners, and two disease registries. Sharing IPD was not considered feasible due to legal requirements and the sensitive medical nature of these data. In addition, harmonizing the data sets for a federated data analysis was difficult due to capacity constraints and the heterogeneity of the data sets. An alternative option was to share summary statistics through contingency tables. Here it is demonstrated that this method along with anonymization methods to ensure patient anonymity had minimal loss of information. Although sharing IPD should continue to be encouraged and strived for, our approach achieved a good balance between data transparency while protecting patient privacy. It also allowed a successful collaboration between industry and academia.

Brief Alcohol Interventions are Effective through 6 Months: Findings from Marginalized Zero-inflated Poisson and Negative Binomial Models in a Two-step IPD Meta-analysis

To evaluate and optimize brief alcohol interventions (BAIs), it is critical to have a credible overall effect size estimate as a benchmark. Estimating such an effect size has been challenging because alcohol outcomes often represent responses from a mixture of individuals: those at high risk for alcohol misuse, occasional nondrinkers, and abstainers. Moreover, some BAIs exclusively focus on heavy drinkers, whereas others take a universal prevention approach. Depending on sample characteristics, the outcome distribution might have many zeros or very few zeros and overdispersion; consequently, the most appropriate statistical model may differ across studies. We synthesized individual participant data (IPD) from 19 studies in Project INTEGRATE (Mun et al., 2015b) that randomly allocated participants to intervention and control groups (N = 7,704 participants, 38.4% men, 74.7% White, 58.5% first-year students). We sequentially estimated marginalized zero-inflated Poisson (Long et al., 2014) or negative binomial regression models to obtain covariate-adjusted, study-specific intervention effect estimates in the first step, which were subsequently combined in a random-effects meta-analysis model in the second step. BAIs produced a statistically significant 8% advantage in the mean number of drinks at both 1-3 months (RR = 0.92, 95% CI = [0.85, 0.98]) and 6 months (RR = 0.92, 95% CI = [0.85, 0.99]) compared to controls. At 9-12 months, there was no statistically significant difference in the mean number of drinks between BAIs and controls. In conclusion, BAIs are effective at reducing the mean number of drinks through at least 6 months post intervention. IPD can play a critical role in deriving findings that could not be obtained in original individual studies or standard aggregate data meta-analyses.

Two-stage or not two-stage? That is the question for IPD meta-analysis projects

Individual participant data meta-analysis (IPDMA) projects obtain, check, harmonise and synthesise raw data from multiple studies. When undertaking the meta-analysis, researchers must decide between a two-stage or a one-stage approach. In a two-stage approach, the IPD are first analysed separately within each study to obtain aggregate data (e.g., treatment effect estimates and standard errors); then, in the second stage, these aggregate data are combined in a standard meta-analysis model (e.g., common-effect or random-effects). In a one-stage approach, the IPD from all studies are analysed in a single step using an appropriate model that accounts for clustering of participants within studies and, potentially, between-study heterogeneity (e.g., a general or generalised linear mixed model). The best approach to take is debated in the literature, and so here we provide clearer guidance for a broad audience. Both approaches are important tools for IPDMA researchers and neither are a panacea. If most studies in the IPDMA are small (few participants or events), a one-stage approach is recommended due to using a more exact likelihood. However, in other situations, researchers can choose either approach, carefully following best practice. Some previous claims recommending to always use a one-stage approach are misleading, and the two-stage approach will often suffice for most researchers. When differences do arise between the two approaches, often it is caused by researchers using different modelling assumptions or estimation methods, rather than using one or two stages per se.

Efficacy of clozapine compared with other second-generation antipsychotic drugs in patients with treatment-resistant schizophrenia: protocol for a systematic review and individual patient data meta-analysis of randomised controlled trials

INTRODUCTION

Guidelines recommend clozapine for treatment-resistant schizophrenia. However, meta-analysis of aggregate data (AD) did not demonstrate higher efficacy of clozapine compared with other second-generation antipsychotics but found substantial heterogeneity between trials and variation between participants in treatment effects. Therefore, we will conduct an individual participant data (IPD) meta-analysis to estimate the efficacy of clozapine compared with other second-generation antipsychotics while accounting for potentially important effect modifiers.

METHODS AND ANALYSIS

In a systematic review, two reviewers will independently search Cochrane Schizophrenia Group's trial register (without restrictions in date, language or state of publication) and related reviews. We will include randomised controlled trials (RCTs) in participants with treatment-resistant schizophrenia comparing clozapine with other second-generation antipsychotics for at least 6 weeks. We will apply no restrictions in age, gender, origin, ethnicity or setting, but exclude open-label studies, studies from China, experimental studies and phase II of cross-over trials. IPD will be requested from trial authors and cross-check against published results. AD will be extracted in duplicate. Risk of bias will be assessed using Cochrane's Risk of Bias 2 tool.The primary outcome will be overall symptoms of schizophrenia.We will synthesise results using random-effects meta-analysis and meta-regression methods in a 3-level Bayesian model. The model combines IPD with AD when IPD is not available for all studies, and include participant, intervention and study design characteristics as potential effect modifiers. The effect size measures will be mean difference (or standardised mean difference when different scales were used). Confidence in the evidence will be assessed using GRADE.

ETHICS AND DISSEMINATION

This project has been approved by the ethics commission of the Technical University of Munich (#612/21 S-NP). The results will be published open-access in a peer-review journal and a plain-language version of the results will be disseminated.If we need to amend this protocol, we will describe the change and give the rationale in a specific section in the resulting publication 'Changes with respect to the protocol'.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO (#CRD42021254986).

Unit information prior for incorporating real-world evidence into randomized controlled trials

Randomized controlled trials (RCTs) have been widely recognized as the gold standard to infer the treatment effect in clinical research. Recently, there has been growing interest in enhancing and complementing the result in an RCT by integrating real-world evidence from observational studies. The unit information prior (UIP) is a newly proposed technique that can effectively borrow information from multiple historical datasets. We extend this generic approach to synthesize the non-randomized evidence into a current RCT. Not only does the UIP only require summary statistics published from observational studies for ease of implementation, but it also has clear interpretations and can alleviate the potential bias in the real-world evidence via weighting schemes. Extensive numerical experiments show that the UIP can improve the statistical efficiency in estimating the treatment effect for various types of outcome variables. The practical potential of our UIP approach is further illustrated with a real trial of hydroxychloroquine for treating COVID-19 patients.

Validating the Assumptions of Population Adjustment: Application of Multilevel Network Meta-regression to a Network of Treatments for Plaque Psoriasis

BACKGROUND

Network meta-analysis (NMA) and indirect comparisons combine aggregate data (AgD) from multiple studies on treatments of interest but may give biased estimates if study populations differ. Population adjustment methods such as multilevel network meta-regression (ML-NMR) aim to reduce bias by adjusting for differences in study populations using individual patient data (IPD) from 1 or more studies under the conditional constancy assumption. A shared effect modifier assumption may also be necessary for identifiability. This article aims to demonstrate how the assumptions made by ML-NMR can be assessed in practice to obtain reliable treatment effect estimates in a target population.

METHODS

We apply ML-NMR to a network of evidence on treatments for plaque psoriasis with a mix of IPD and AgD trials reporting ordered categorical outcomes. Relative treatment effects are estimated for each trial population and for 3 external target populations represented by a registry and 2 cohort studies. We examine residual heterogeneity and inconsistency and relax the shared effect modifier assumption for each covariate in turn.

RESULTS

Estimated population-average treatment effects were similar across study populations, as differences in the distributions of effect modifiers were small. Better fit was achieved with ML-NMR than with NMA, and uncertainty was reduced by explaining within- and between-study variation. We found little evidence that the conditional constancy or shared effect modifier assumptions were invalid.

CONCLUSIONS

ML-NMR extends the NMA framework and addresses issues with previous population adjustment approaches. It coherently synthesizes evidence from IPD and AgD studies in networks of any size while avoiding aggregation bias and noncollapsibility bias, allows for key assumptions to be assessed or relaxed, and can produce estimates relevant to a target population for decision-making.

HIGHLIGHTS

Multilevel network meta-regression (ML-NMR) extends the network meta-analysis framework to synthesize evidence from networks of studies providing individual patient data or aggregate data while adjusting for differences in effect modifiers between studies (population adjustment). We apply ML-NMR to a network of treatments for plaque psoriasis with ordered categorical outcomes.We demonstrate for the first time how ML-NMR allows key assumptions to be assessed. We check for violations of conditional constancy of relative effects (such as unobserved effect modifiers) through residual heterogeneity and inconsistency and the shared effect modifier assumption by relaxing this for each covariate in turn.Crucially for decision making, population-adjusted treatment effects can be produced in any relevant target population. We produce population-average estimates for 3 external target populations, represented by the PsoBest registry and the PROSPECT and Chiricozzi 2019 cohort studies.

Bayesian network meta-analysis methods for combining individual participant data and aggregate data from single arm trials and randomised controlled trials

BACKGROUND

Increasingly in network meta-analysis (NMA), there is a need to incorporate non-randomised evidence to estimate relative treatment effects, and in particular in cases with limited randomised evidence, sometimes resulting in disconnected networks of treatments. When combining different sources of data, complex NMA methods are required to address issues associated with participant selection bias, incorporating single-arm trials (SATs), and synthesising a mixture of individual participant data (IPD) and aggregate data (AD). We develop NMA methods which synthesise data from SATs and randomised controlled trials (RCTs), using a mixture of IPD and AD, for a dichotomous outcome.

METHODS

We propose methods under both contrast-based (CB) and arm-based (AB) parametrisations, and extend the methods to allow for both within- and across-trial adjustments for covariate effects. To illustrate the methods, we use an applied example investigating the effectiveness of biologic disease-modifying anti-rheumatic drugs for rheumatoid arthritis (RA). We applied the methods to a dataset obtained from a literature review consisting of 14 RCTs and an artificial dataset consisting of IPD from two SATs and AD from 12 RCTs, where the artificial dataset was created by removing the control arms from the only two trials assessing tocilizumab in the original dataset.

RESULTS

Without adjustment for covariates, the CB method with independent baseline response parameters (CBunadjInd) underestimated the effectiveness of tocilizumab when applied to the artificial dataset compared to the original dataset, albeit with significant overlap in posterior distributions for treatment effect parameters. The CB method with exchangeable baseline response parameters produced effectiveness estimates in agreement with CBunadjInd, when the predicted baseline response estimates were similar to the observed baseline response. After adjustment for RA duration, there was a reduction in across-trial heterogeneity in baseline response but little change in treatment effect estimates.

CONCLUSIONS

Our findings suggest incorporating SATs in NMA may be useful in some situations where a treatment is disconnected from a network of comparator treatments, due to a lack of comparative evidence, to estimate relative treatment effects. The reliability of effect estimates based on data from SATs may depend on adjustment for covariate effects, although further research is required to understand this in more detail.

How effects on health equity are assessed in systematic reviews of interventions

BACKGROUND

Enhancing health equity is endorsed in the Sustainable Development Goals. The failure of systematic reviews to consider potential differences in effects across equity factors is cited by decision-makers as a limitation to their ability to inform policy and program decisions.  OBJECTIVES: To explore what methods systematic reviewers use to consider health equity in systematic reviews of effectiveness.

SEARCH METHODS

We searched the following databases up to 26 February 2021: MEDLINE, PsycINFO, the Cochrane Methodology Register, CINAHL, Education Resources Information Center, Education Abstracts, Criminal Justice Abstracts, Hein Index to Foreign Legal Periodicals, PAIS International, Social Services Abstracts, Sociological Abstracts, Digital Dissertations and the Health Technology Assessment Database. We searched SCOPUS to identify articles that cited any of the included studies on 10 June 10 2021. We contacted authors and searched the reference lists of included studies to identify additional potentially relevant studies.

SELECTION CRITERIA

We included empirical studies of cohorts of systematic reviews that assessed methods for measuring effects on health inequalities. We define health inequalities as unfair and avoidable differences across socially stratifying factors that limit opportunities for health. We operationalised this by assessing studies which evaluated differences in health across any component of the PROGRESS-Plus acronym, which stands for Place of residence, Race/ethnicity/culture/language, Occupation, Gender or sex, Religion, Education, Socioeconomic status, Social capital. "Plus" stands for other factors associated with discrimination, exclusion, marginalisation or vulnerability such as personal characteristics (e.g. age, disability), relationships that limit opportunities for health (e.g. children in a household with parents who smoke) or environmental situations which provide limited control of opportunities for health (e.g. school food environment).

DATA COLLECTION AND ANALYSIS

Two review authors independently extracted data using a pre-tested form. Risk of bias was appraised for included studies according to the potential for bias in selection and detection of systematic reviews.  MAIN RESULTS: In total, 48,814 studies were identified and the titles and abstracts were screened in duplicate. In this updated review, we identified an additional 124 methodological studies published in the 10 years since the first version of this review, which included 34 studies. Thus, 158 methodological studies met our criteria for inclusion. The methods used by these studies focused on evidence relevant to populations experiencing health inequity (108 out of 158 studies), assess subgroup analysis across PROGRESS-Plus (26 out of 158 studies), assess analysis of a gradient in effect across PROGRESS-Plus (2 out of 158 studies) or use a combination of subgroup analysis and focused approaches (20 out of 158 studies). The most common PROGRESS-Plus factors assessed were age (43 studies), socioeconomic status in 35 studies, low- and middle-income countries in 24 studies, gender or sex in 22 studies, race or ethnicity in 17 studies, and four studies assessed multiple factors across which health inequity may exist. Only 16 studies provided a definition of health inequity. Five methodological approaches to consider health equity in systematic reviews of effectiveness were identified: 1) descriptive assessment of reporting and analysis in systematic reviews (140 of 158 studies used a type of descriptive method); 2) descriptive assessment of reporting and analysis in original trials (50 studies); 3) analytic approaches which assessed differential effects across one or more PROGRESS-Plus factors (16 studies); 4) applicability assessment (25 studies) and 5) stakeholder engagement (28 studies), which is a new finding in this update and examines the appraisal of whether relevant stakeholders with lived experience of health inequity were included in the design of systematic reviews or design and delivery of interventions. Reporting for both approaches (analytic and applicability) lacked transparency and was insufficiently detailed to enable the assessment of credibility.

AUTHORS' CONCLUSIONS

There is a need for improvement in conceptual clarity about the definition of health equity, describing sufficient detail about analytic approaches (including subgroup analyses) and transparent reporting of judgments required for applicability assessments in order to consider health equity in systematic reviews of effectiveness.

Efficient integration of aggregate data and individual participant data in one-way mixed models

Often both aggregate data (AD) studies and individual participant data (IPD) studies are available for specific treatments. Combining these two sources of data could improve the overall meta-analytic estimates of treatment effects. Moreover, often for some studies with AD, the associated IPD maybe available, albeit at some extra effort or cost to the analyst. We propose a method for combining treatment effects across trials when the response is from the exponential family of distribution and hence a generalized linear model structure can be used. We consider the case when treatment effects are fixed and common across studies. Using the proposed combination method, we study the relative efficiency of analyzing all IPD studies vs combining various percentages of AD and IPD studies. For many different models, design constraints under which the AD estimators are the IPD estimators, and hence fully efficient, are known. For such models, we advocate a selection procedure that chooses AD studies over IPD studies in a manner that force least departure from design constraints and hence ensures an efficient combined AD and IPD estimator.

Individual Patient Data Meta-Analysis and Network Meta-Analysis

Meta-analyses are often conducted using trial-level summary data. However, when individual patient data (IPD ) is available, there is greater flexibility in the analysis and a wider range of statistical models that can be fitted. There are two approaches to fitting IPD models. The traditional two-stage approach involves analyzing each trial individually in the first stage and then combining trial estimates of treatment effectiveness in the second stage using methods developed for aggregate data meta-analysis. Growing in popularity is the one-stage approach in which trials are analyzed and synthesized within one statistical model whilst the clustering of patients within trials is accounted for. This chapter outlines both fixed effect and random effects one- and two-stage meta-analysis models for continuous, binary, and time-to-event outcomes. The meta-analysis framework is then extended to the scenario where there are more than two treatments and network meta-analysis models are described.The availability of IPD provides greater statistical power for investigating interactions between treatments and covariates. Treatment-covariate interactions contain both within- and across-trial information where the across-trial information may be subject to ecological bias. This chapter presents network meta-analysis models separating out the within- and across-trial information and finishes by considering practical solutions for dealing with missing covariate data, assessing the consistency assumption, combining IPD and aggregate data and specific considerations for time-to-event outcomes.

Practical Considerations and Challenges When Conducting an Individual Participant Data (IPD) Meta-Analysis

This chapter provides a broad overview of the use of individual participant (sometimes referred to as patient) data (IPD ) within meta-analyses, the associated advantages of using IPD in meta-analysis compared to aggregate data, and when IPD should be used in meta-analysis.This chapter also outlines the steps of conducting an IPD meta-analysis, with practical guidance relating to requesting and obtaining IPD for meta-analysis. Challenges that can be associated with conducting an IPD meta-analysis are also discussed, including consideration of availability bias, when a subset of the relevant IPD is not available for meta-analysis.

Effectiveness of collaborative care in reducing suicidal ideation: An individual participant data meta-analysis

To assess whether CC is more effective at reducing suicidal ideation in people with depression compared with usual care, and whether study and patient factors moderate treatment effects.

METHOD

We searched Medline, Embase, PubMed, PsycINFO, CINAHL, CENTRAL from inception to March 2020 for Randomised Controlled Trials (RCTs) that compared the effectiveness of CC with usual care in depressed adults, and reported changes in suicidal ideation at 4 to 6 months post-randomisation. Mixed-effects models accounted for clustering of participants within trials and heterogeneity across trials. This study is registered with PROSPERO, CRD42020201747.

RESULTS

We extracted data from 28 RCTs (11,165 patients) of 83 eligible studies. We observed a small significant clinical improvement of CC on suicidal ideation, compared with usual care (SMD, -0.11 [95%CI, -0.15 to -0.08]; I², 0·47% [95%CI 0.04% to 4.90%]). CC interventions with a recognised psychological treatment were associated with small reductions in suicidal ideation (SMD, -0.15 [95%CI -0.19 to -0.11]). CC was more effective for reducing suicidal ideation among patients aged over 65 years (SMD, - 0.18 [95%CI -0.25 to -0.11]).

CONCLUSION

Primary care based CC with an embedded psychological intervention is the most effective CC framework for reducing suicidal ideation and older patients may benefit the most.

Most published meta-regression analyses based on aggregate data suffer from methodological pitfalls: a meta-epidemiological study

BACKGROUND

Due to clinical and methodological diversity, clinical studies included in meta-analyses often differ in ways that lead to differences in treatment effects across studies. Meta-regression analysis is generally recommended to explore associations between study-level characteristics and treatment effect, however, three key pitfalls of meta-regression may lead to invalid conclusions. Our aims were to determine the frequency of these three pitfalls of meta-regression analyses, examine characteristics associated with the occurrence of these pitfalls, and explore changes between 2002 and 2012.

METHODS

A meta-epidemiological study of studies including aggregate data meta-regression analysis in the years 2002 and 2012. We assessed the prevalence of meta-regression analyses with at least 1 of 3 pitfalls: ecological fallacy, overfitting, and inappropriate methods to regress treatment effects against the risk of the analysed outcome. We used logistic regression to investigate study characteristics associated with pitfalls and examined differences between 2002 and 2012.

RESULTS

Our search yielded 580 studies with meta-analyses, of which 81 included meta-regression analyses with aggregated data. 57 meta-regression analyses were found to contain at least one pitfall (70%): 53 were susceptible to ecological fallacy (65%), 14 had a risk of overfitting (17%), and 5 inappropriately regressed treatment effects against the risk of the analysed outcome (6%). We found no difference in the prevalence of meta-regression analyses with methodological pitfalls between 2002 and 2012, nor any study-level characteristic that was clearly associated with the occurrence of any of the pitfalls.

CONCLUSION

The majority of meta-regression analyses based on aggregate data contain methodological pitfalls that may result in misleading findings.

Assessing the performance of population adjustment methods for anchored indirect comparisons: A simulation study

Standard network meta-analysis and indirect comparisons combine aggregate data from multiple studies on treatments of interest, assuming that any factors that interact with treatment effects (effect modifiers) are balanced across populations. Population adjustment methods such as multilevel network meta-regression (ML-NMR), matching-adjusted indirect comparison (MAIC), and simulated treatment comparison (STC) relax this assumption using individual patient data from one or more studies, and are becoming increasingly prevalent in health technology appraisals and the applied literature. Motivated by an applied example and two recent reviews of applications, we undertook an extensive simulation study to assess the performance of these methods in a range of scenarios under various failures of assumptions. We investigated the impact of varying sample size, missing effect modifiers, strength of effect modification and validity of the shared effect modifier assumption, validity of extrapolation and varying between-study overlap, and different covariate distributions and correlations. ML-NMR and STC performed similarly, eliminating bias when the requisite assumptions were met. Serious concerns are raised for MAIC, which performed poorly in nearly all simulation scenarios and may even increase bias compared with standard indirect comparisons. All methods incur bias when an effect modifier is missing, highlighting the necessity of careful selection of potential effect modifiers prior to analysis. When all effect modifiers are included, ML-NMR and STC are robust techniques for population adjustment. ML-NMR offers additional advantages over MAIC and STC, including extending to larger treatment networks and producing estimates in any target population, making this an attractive choice in a variety of scenarios.

Obtaining and managing data sets for individual participant data meta-analysis: scoping review and practical guide

BACKGROUND

Shifts in data sharing policy have increased researchers' access to individual participant data (IPD) from clinical studies. Simultaneously the number of IPD meta-analyses (IPDMAs) is increasing. However, rates of data retrieval have not improved. Our goal was to describe the challenges of retrieving IPD for an IPDMA and provide practical guidance on obtaining and managing datasets based on a review of the literature and practical examples and observations.

METHODS

We systematically searched MEDLINE, Embase, and the Cochrane Library, until January 2019, to identify publications focused on strategies to obtain IPD. In addition, we searched pharmaceutical websites and contacted industry organizations for supplemental information pertaining to recent advances in industry policy and practice. Finally, we documented setbacks and solutions encountered while completing a comprehensive IPDMA and drew on previous experiences related to seeking and using IPD.

RESULTS

Our scoping review identified 16 articles directly relevant for the conduct of IPDMAs. We present short descriptions of these articles alongside overviews of IPD sharing policies and procedures of pharmaceutical companies which display certification of Principles for Responsible Clinical Trial Data Sharing via Pharmaceutical Research and Manufacturers of America or European Federation of Pharmaceutical Industries and Associations websites. Advances in data sharing policy and practice affected the way in which data is requested, obtained, stored and analyzed. For our IPDMA it took 6.5 years to collect and analyze relevant IPD and navigate additional administrative barriers. Delays in obtaining data were largely due to challenges in communication with study sponsors, frequent changes in data sharing policies of study sponsors, and the requirement for a diverse skillset related to research, administrative, statistical and legal issues.

CONCLUSIONS

Knowledge of current data sharing practices and platforms as well as anticipation of necessary tasks and potential obstacles may reduce time and resources required for obtaining and managing data for an IPDMA. Sufficient project funding and timeline flexibility are pre-requisites for successful collection and analysis of IPD. IPDMA researchers must acknowledge the additional and unexpected responsibility they are placing on corresponding study authors or data sharing administrators and should offer assistance in readying data for sharing.

Mass deworming for improving health and cognition of children in endemic helminth areas: A systematic review and individual participant data network meta-analysis

Background

Soil transmitted (or intestinal) helminths and schistosomes affect millions of children worldwide.

Objectives

To use individual participant data network meta-analysis (NMA) to explore the effects of different types and frequency of deworming drugs on anaemia, cognition and growth across potential effect modifiers.

Search Methods

We developed a search strategy with an information scientist to search MEDLINE, CINAHL, LILACS, Embase, the Cochrane Library, Econlit, Internet Documents in Economics Access Service (IDEAS), Public Affairs Information Service (PAIS), Social Services Abstracts, Global Health CABI and CAB Abstracts up to March 27, 2018. We also searched grey literature, websites, contacted authors and screened references of relevant systematic reviews.

Selection Criteria

We included randomised and quasirandomised deworming trials in children for deworming compared to placebo or other interventions with data on baseline infection.

Data Collection and Analysis

We conducted NMA with individual participant data (IPD), using a frequentist approach for random-effects NMA. The covariates were: age, sex, weight, height, haemoglobin and infection intensity. The effect estimate chosen was the mean difference for the continuous outcome of interest.

Results

We received data from 19 randomized controlled trials with 31,945 participants. Overall risk of bias was low. There were no statistically significant subgroup effects across any of the potential effect modifiers. However, analyses showed that there may be greater effects on weight for moderate to heavily infected children (very low certainty evidence).

Authors' Conclusions

This analysis reinforces the case against mass deworming at a population-level, finding little effect on nutritional status or cognition. However, children with heavier intensity infections may benefit more. We urge the global community to adopt calls to make data available in open repositories to facilitate IPD analyses such as this, which aim to assess effects for the most vulnerable individuals.

A comparison of the statistical performance of different meta-analysis models for the synthesis of subgroup effects from randomized clinical trials

BACKGROUND

When investigating subgroup effects in meta-analysis, it is unclear whether accounting in meta-regression for between-trial variation in treatment effects, but not between-trial variation in treatment interaction effects when such effects are present, leads to biased estimates, coverage problems, or wrong standard errors, and whether the use of aggregate data (AD) or individual-patient-data (IPD) influences this assessment.

METHODS

Seven different models were compared in a simulation study. Models differed regarding the use of AD or IPD, whether they accounted for between-trial variation in interaction effects, and whether they minimized the risk of ecological fallacy.

RESULTS

Models that used IPD and that allowed for between-trial variation of the interaction effect had less bias, better coverage, and more accurate standard errors than models that used AD or ignored this variation. The main factor influencing the performance of models was whether they used IPD or AD. The model that used AD had a considerably worse performance than all models that used IPD, especially when a low number of trials was included in the analysis.

CONCLUSIONS

The results indicate that IPD models that allow for the between-trial variation in interaction effects should be given preference whenever investigating subgroup effects within a meta-analysis.

Integrated evaluation of targeted and non-targeted therapies in a network meta-analysis

Individualized therapies for patients with biomarkers are moving more and more into the focus of research interest when developing new treatments. Hereby, the term individualized (or targeted) therapy denotes a treatment specifically developed for biomarker-positive patients. A network meta-analysis model for a binary endpoint combining the evidence for a targeted therapy from individual patient data with the evidence for a non-targeted therapy from aggregate data is presented and investigated. The biomarker status of the patients is either available at patient-level in individual patient data or at study-level in aggregate data. Both types of biomarker information have to be included. The evidence synthesis model follows a Bayesian approach and applies a meta-regression to the studies with aggregate data. In a simulation study, we address three treatment arms, one of them investigating a targeted therapy. The bias and the root-mean-square error of the treatment effect estimate for the subgroup of biomarker-positive patients based on studies with aggregate data are investigated. Thereby, the meta-regression approach is compared to approaches applying alternative solutions. The regression approach has a surprisingly small bias even in the presence of few studies. By contrast, the root-mean-square error is relatively greater. An illustrative example is provided demonstrating implementation of the presented network meta-analysis model in a clinical setting.

Meta-Analysis of Complex Interventions

Meta-analysis is a prominent method for estimating the effects of public health interventions, yet these interventions are often complex in ways that pose challenges to using conventional meta-analytic methods. This article discusses meta-analytic techniques that can be used in research syntheses on the effects of complex public health interventions. We first introduce the use of complexity frameworks to conceptualize public health interventions. We then present a menu of meta-analytic procedures for addressing various sources of complexity when answering questions about the effects of public health interventions in research syntheses. We conclude with a review of important practices and key resources for conducting meta-analyses on complex interventions, as well as future directions for research synthesis more generally. Overall, we argue that it is possible to conduct meaningful quantitative syntheses of research on the effects of public health interventions, though these meta-analyses may require the use of advanced techniques to properly consider and attend to issues of complexity.

Diagnostic Accuracy of Neuroimaging to Delineate Diffuse Gliomas within the Brain: A Meta-Analysis

BACKGROUND

Brain imaging in diffuse glioma is used for diagnosis, treatment planning, and follow-up.

PURPOSE

In this meta-analysis, we address the diagnostic accuracy of imaging to delineate diffuse glioma.

DATA SOURCES

We systematically searched studies of adults with diffuse gliomas and correlation of imaging with histopathology.

STUDY SELECTION

Study inclusion was based on quality criteria. Individual patient data were used, if available.

DATA ANALYSIS

A hierarchic summary receiver operating characteristic method was applied. Low- and high-grade gliomas were analyzed in subgroups.

DATA SYNTHESIS

Sixty-one studies described 3532 samples in 1309 patients. The mean Standard for Reporting of Diagnostic Accuracy score (13/25) indicated suboptimal reporting quality. For diffuse gliomas as a whole, the diagnostic accuracy was best with T2-weighted imaging, measured as area under the curve, false-positive rate, true-positive rate, and diagnostic odds ratio of 95.6%, 3.3%, 82%, and 152. For low-grade gliomas, the diagnostic accuracy of T2-weighted imaging as a reference was 89.0%, 0.4%, 44.7%, and 205; and for high-grade gliomas, with T1-weighted gadolinium-enhanced MR imaging as a reference, it was 80.7%, 16.8%, 73.3%, and 14.8. In high-grade gliomas, MR spectroscopy (85.7%, 35.0%, 85.7%, and 12.4) and ¹¹C methionine-PET (85.1%, 38.7%, 93.7%, and 26.6) performed better than the reference imaging.

LIMITATIONS

True-negative samples were underrepresented in these data, so false-positive rates are probably less reliable than true-positive rates. Multimodality imaging data were unavailable.

CONCLUSIONS

The diagnostic accuracy of commonly used imaging is better for delineation of low-grade gliomas than high-grade gliomas on the basis of limited evidence. Improvement is indicated from advanced techniques, such as MR spectroscopy and PET.

Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models

Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage individual participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest. We assume that studies are independent, and utilise a decomposition of Fisher's information matrix to decompose the total variance matrix of parameter estimates into study-specific contributions, from which percentage weights are derived. This approach generalises how percentage weights are calculated in a traditional, single parameter meta-analysis model. Application is made to one- and two-stage individual participant data meta-analyses, meta-regression and network (multivariate) meta-analysis of multiple treatments. These reveal percentage study weights toward clinically important estimates, such as summary treatment effects and treatment-covariate interactions, and are especially useful when some studies are potential outliers or at high risk of bias. We also derive percentage study weights toward methodologically interesting measures, such as the magnitude of ecological bias (difference between within-study and across-study associations) and the amount of inconsistency (difference between direct and indirect evidence in a network meta-analysis).

One-stage individual participant data meta-analysis models: estimation of treatment-covariate interactions must avoid ecological bias by separating out within-trial and across-trial information

Stratified medicine utilizes individual‐level covariates that are associated with a differential treatment effect, also known as treatment‐covariate interactions. When multiple trials are available, meta‐analysis is used to help detect true treatment‐covariate interactions by combining their data. Meta‐regression of trial‐level information is prone to low power and ecological bias, and therefore, individual participant data (IPD) meta‐analyses are preferable to examine interactions utilizing individual‐level information. However, one‐stage IPD models are often wrongly specified, such that interactions are based on amalgamating within‐ and across‐trial information. We compare, through simulations and an applied example, fixed‐effect and random‐effects models for a one‐stage IPD meta‐analysis of time‐to‐event data where the goal is to estimate a treatment‐covariate interaction. We show that it is crucial to centre patient‐level covariates by their mean value in each trial, in order to separate out within‐trial and across‐trial information. Otherwise, bias and coverage of interaction estimates may be adversely affected, leading to potentially erroneous conclusions driven by ecological bias. We revisit an IPD meta‐analysis of five epilepsy trials and examine age as a treatment effect modifier. The interaction is −0.011 (95% CI: −0.019 to −0.003; p = 0.004), and thus highly significant, when amalgamating within‐trial and across‐trial information. However, when separating within‐trial from across‐trial information, the interaction is −0.007 (95% CI: −0.019 to 0.005; p = 0.22), and thus its magnitude and statistical significance are greatly reduced. We recommend that meta‐analysts should only use within‐trial information to examine individual predictors of treatment effect and that one‐stage IPD models should separate within‐trial from across‐trial information to avoid ecological bias. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Methods for network meta-analysis of continuous outcomes using individual patient data: a case study in acupuncture for chronic pain

BACKGROUND

Network meta-analysis methods, which are an extension of the standard pair-wise synthesis framework, allow for the simultaneous comparison of multiple interventions and consideration of the entire body of evidence in a single statistical model. There are well-established advantages to using individual patient data to perform network meta-analysis and methods for network meta-analysis of individual patient data have already been developed for dichotomous and time-to-event data. This paper describes appropriate methods for the network meta-analysis of individual patient data on continuous outcomes.

METHODS

This paper introduces and describes network meta-analysis of individual patient data models for continuous outcomes using the analysis of covariance framework. Comparisons are made between this approach and change score and final score only approaches, which are frequently used and have been proposed in the methodological literature. A motivating example on the effectiveness of acupuncture for chronic pain is used to demonstrate the methods. Individual patient data on 28 randomised controlled trials were synthesised. Consistency of endpoints across the evidence base was obtained through standardisation and mapping exercises.

RESULTS

Individual patient data availability avoided the use of non-baseline-adjusted models, allowing instead for analysis of covariance models to be applied and thus improving the precision of treatment effect estimates while adjusting for baseline imbalance.

CONCLUSIONS

The network meta-analysis of individual patient data using the analysis of covariance approach is advocated to be the most appropriate modelling approach for network meta-analysis of continuous outcomes, particularly in the presence of baseline imbalance. Further methods developments are required to address the challenge of analysing aggregate level data in the presence of baseline imbalance.

Bayesian network meta-analysis for cluster randomized trials with binary outcomes

Network meta-analysis is becoming a common approach to combine direct and indirect comparisons of several treatment arms. In recent research, there have been various developments and extensions of the standard methodology. Simultaneously, cluster randomized trials are experiencing an increased popularity, especially in the field of health services research, where, for example, medical practices are the units of randomization but the outcome is measured at the patient level. Combination of the results of cluster randomized trials is challenging. In this tutorial, we examine and compare different approaches for the incorporation of cluster randomized trials in a (network) meta-analysis. Furthermore, we provide practical insight on the implementation of the models. In simulation studies, it is shown that some of the examined approaches lead to unsatisfying results. However, there are alternatives which are suitable to combine cluster randomized trials in a network meta-analysis as they are unbiased and reach accurate coverage rates. In conclusion, the methodology can be extended in such a way that an adequate inclusion of the results obtained in cluster randomized trials becomes feasible. Copyright © 2016 John Wiley & Sons, Ltd.

Avoiding Methodological Biases in Meta-Analysis

Abstract. Individual participant data (IPD) meta-analysis is the gold standard of meta-analyses. This paper points out several advantages of IPD meta-analysis over classical meta-analysis, such as avoiding aggregation bias (e.g., ecological fallacy or Simpson’s paradox) and shows how its two main disadvantages (time and cost) can be overcome through Internet-based research. Ideally, we recommend carrying out IPD meta-analyses that consider online versus offline data gathering processes and examine data quality. Through a comprehensive literature search, we investigated whether IPD meta-analyses published in the field of educational psychology already follow these recommendations; this was not the case. For this reason, the paper demonstrates characteristics of ideal meta-analysis on teachers’ judgment accuracy and links it to recent meta-analyses on that topic. The recommendations are important for meta-analysis researchers and for readers and reviewers of meta-analyses. Our paper is also relevant to current discussions within the psychological community on study replication.

Evaluating the Importance of Heterogeneity of Treatment Effect: Variation in Patient Utilities Can Influence Choice of the "Optimal" Oral Anticoagulant for Atrial Fibrillation

OBJECTIVES

To investigate heterogeneity of treatment effect (HTE) for anticoagulants in atrial fibrillation across subgroups defined by 1) clinical characteristics and 2) variation in patient utilities for benefits and harms of treatment.

METHODS

We reanalyzed aggregate data from a published network meta-analysis that compared four anticoagulants for atrial fibrillation (apixaban, dabigatran, edoxaban, and rivaroxaban) as well as warfarin. Event rates for stroke/systemic embolism (SE) and major bleeding were generated for each agent across seven subgroups, and rankings were developed on the basis of clinical performance. Utilities were derived from a national catalog and then applied to generate summary measures of benefit. The choice between any two agents was examined across a range of plausible utility values, defined as the interquartile range for stroke/SE and major bleeding.

RESULTS

Little HTE was apparent in clinical and utility-adjusted analyses. Dabigatran 150 mg produced the lowest rates of stroke/SE, and edoxaban 30 mg had the lowest rate of major bleeding. Greater HTE was observed when utilities were varied across a plausible utility range. For example, among patients 75 years and older, dabigatran 150 mg would be preferred over edoxaban 30 mg when mean utility estimates are used. The preferred agent, however, would change at plausible utility thresholds of 0.6 and 0.7 for major bleeding and stroke/SE, respectively. Nearly 25% of all possible comparisons would see a change in preferred treatment within the plausible utility range.

CONCLUSIONS

The optimal choice of anticoagulant in atrial fibrillation differs across subgroups defined by clinical characteristics and reasonable ranges of utilities.

Parameterization of a disease progression simulation model for sequentially treated metastatic human epidermal growth factor receptor 2 positive breast cancer patients

Background The objective of this study is twofold: 1) to propose a simulation model for HER2+ metastatic breast cancer (mBC) which could further be used to assess the overall cost-effectiveness of the treatment sequences that would maximize survival of patients, and 2) to estimate transitional probabilities between treatment lines required to parameterize the simulation model, in the absence of individual patient data (IPD). Methods Individual patient data (IPD) were reconstructed for treatment lines composing four treatment sequences. Parametric models were tested to select the model that best fits the IPD. The transitional probability equations, used for disease progression modeling, were obtained by substituting the parameters of the general equation for transitional probabilities by the parameters estimated from fitted distributions. Results The log-logistic model best fitted the reconstructed data for progression-free and overall survival curves for each line of treatment. The shapes and scales of the log-logistic models were used to develop the transitional probability equations for the HER2+ mBC simulation model.

KEY LIMITATIONS

The estimation of the transitional probabilities depends heavily on the accuracy of the IPD reconstruction. Nonetheless, analytical and graphical tests can be performed to check the face validity of the reconstructed data. Additionally, sensitivity analyses can be conducted to test the impact of uncertainty surrounding the estimated parameters defining equations for transitional probabilities. Conclusion The results of this study can be used as input in model-based economic evaluations of sequential therapy for HER2+ mBC.

Comparing the Overall Result and Interaction in Aggregate Data Meta-Analysis and Individual Patient Data Meta-Analysis

The aim of the study was to examine how well aggregate data meta-analyses (ADMAs) and individual patient data meta-analyses (IPDMAs) agree in their overall results and how frequently interactions are detected in IPDMAs and ADMAs.ADMA articles immediately published before the IPDMA and matching the research topic were identified. Agreement in the overall result was achieved if the estimate was in the same direction. The number of subgroup analyses, in particular that of significant interactions, was compared between the 2 types of meta-analyses.A total of 829 IPDMA articles were identified; 129 (15.6%) were found to have a matched ADMA article and 204 paired meta-analyses were identified. Agreement in the overall effect was observed in 187 (91.7%) of the 204 paired meta-analyses. Fifty-three (26.0%) ADMAs and 121 (59.3%) IPDMAs conducted subgroup analyses and presented 150 and 634 subgroup analyses, respectively. The IPDMAs conducted 7 times more subgroup analyses on interaction (544 in IPDMAs vs 68 in ADMAs) and identified 14 times more potential interactions (44 in IPDMAs vs 3 in ADMAs).ADMAs will almost always agree with their corresponding IPDMAs in the overall result if greater efforts are made to improve the methodology in conducting ADMAs. The IPDMA is required mostly if interactions are suspected.

Contacting authors to retrieve individual patient data: study protocol for a randomized controlled trial

Background

Individual patient data (IPD) meta-analysis is considered the “gold standard” for exploring the effectiveness of interventions in different subgroups of patients. However, obtaining IPD is time-consuming and contact with the researchers responsible for the original trials is usually required. To date, there are no studies evaluating different strategies to optimize the process for retrieval of IPD from such researchers. Our aim is to examine the impact of providing incentives to the researchers responsible for the trials eligible for a meta-analysis to submit their IPD.

Methods/Design

We updated our previously published systematic reviews for type 1 diabetes mellitus comparing long- and intermediate-acting insulin regimens (from January 2013 to June 2015) and for Alzheimer’s dementia comparing cognitive enhancers (from January 2015 to May 2015). Eligible were randomized controlled trials (RCTs) fulfilling the eligibility criteria of the systematic reviews. We will randomly allocate authors of the reports of these RCTs into an intervention or control group. Those allocated to the intervention group will be contacted by email, mail, and phone, and will be asked to provide the IPD from their RCT and will be given a financial incentive. Those allocated to the control group will be contacted by email, mail, and phone, but will not receive a financial incentive. Our primary outcome will be the proportion of authors who provide the IPD. The secondary outcomes will be the time to return the dataset (defined as the period between the information request and the authors’ response with the dataset), and completeness of data. We will compare the response rates in the two groups using the odds ratio and the corresponding 95 % confidence interval. We will also use binary logistic regression and cox regression analyses to examine whether different RCT characteristics, such as study size and sponsor information, influence the probability of providing IPD and the time needed to share the data.

Discussion

This study will determine whether a financial incentive affects response rates when seeking IPD from the original researchers. We will disseminate our findings in an open access scientific journal and present results at national and international conferences.

Trial registration

This trial is registered in Clinical Trials.gov, ID number NCT02569411. Date of registration 5 October 2015.

The Many Flavors of Model-Based Meta-Analysis: Part I-Introduction and Landmark Data

Meta-analysis is an increasingly important aspect of drug development as companies look to benchmark their own compounds with the competition. There is scope to carry out a wide range of analyses addressing key research questions from preclinical through to postregistration. This set of tutorials will take the reader through key model-based meta-analysis (MBMA) methods with this first installment providing a general introduction before concentrating on classical and Bayesian methods for landmark data.

Comparative safety and effectiveness of cognitive enhancers for Alzheimer's dementia: protocol for a systematic review and individual patient data network meta-analysis

INTRODUCTION

Alzheimer's dementia (AD) is the most common cause of dementia, and several organisations, such as the National Institute for Health and Care Excellence, suggest that management of patients with AD should be tailored to their needs. To date, little research has been conducted on the treatment effect in different subgroups of patients with AD. The aim of this study is to examine the comparative effectiveness and safety of cognitive enhancers for different patient characteristics.

METHODS AND ANALYSIS

We will update our previous literature search from January 2015 forward, using the same terms and electronic databases (eg, MEDLINE) from our previous review. We will additionally search grey literature and scan the reference lists of the included studies. Randomised clinical trials of any duration conducted at any time comparing cognitive enhancers alone or in any combination against other cognitive enhancers, or placebo in adults with AD will be eligible. The outcomes of interest are cognition according to the Mini-Mental State Examination, and overall serious adverse events. For each outcome and treatment comparison, we will perform a Bayesian hierarchical random-effects meta-analysis combining the individual patient data (IPD) from each eligible study. If the identified treatment comparisons form a connected network diagram, we will perform an IPD network meta-analysis (NMA) to estimate subgroup effects for patients with different characteristics, such as AD severity and sex. We will combine aggregated data from studies that we will not be able to obtain IPD, with the IPD provided by the original authors, in a single model. We will use the PRISMA-IPD and PRISMA-NMA statements to report our findings.

ETHICS AND DISSEMINATION

The findings of this study will be of interest to stakeholders, including decision makers, guideline developers, clinicians, methodologists and patients, and they will help to improve guidelines for the management of patients with AD.

TRIAL REGISTRATION NUMBER

CRD42015023507.

Safety and effectiveness of long-acting versus intermediate-acting insulin for patients with type 1 diabetes: protocol for a systematic review and individual patient data network meta-analysis

INTRODUCTION

The choice of a basal insulin regimen to manage type 1 diabetes mellitus (T1DM) may have different risks of adverse events and effectiveness, due to the difference in the effectiveness of these agents across patient characteristics (eg, baseline glycosylated haemoglobin; A1C). Currently, there is a lack of high quality evidence to support the tailoring of insulin regimens according to an individual's needs. The aim of this study is to update our previous systematic review and perform an individual patient data network meta-analysis (IPD-NMA) to evaluate the comparative safety and effectiveness of long-acting versus intermediate-acting insulin in different subgroups of patients with T1DM.

METHODS AND ANALYSIS

We will update our previous literature search from January 2013 onwards searching relevant electronic databases (eg, MEDLINE), as well as perform grey literature search through relevant society/association websites, and conference abstracts, and scan reference lists of the eligible studies. We will include randomised clinical trials of any duration examining long-acting versus intermediate-acting insulin preparations for adult patients with T1DM. We will focus on A1C and severe hypoglycaemia outcomes. For each pairwise treatment comparison, we will combine all IPD from all studies in a single multilevel model, where each study is a different cluster. For a connected network of trials, we will perform an IPD-NMA to identify potential effect modifiers, and estimate the most effective and safe treatments for patients with different characteristics. If we are not successful in obtaining IPD for at least one study, we will include aggregated data (AD) abstracted from the included RCTs in our analysis, combining IPD and AD into a single model. We will report our results using the PRISMA-IPD statement.

ETHICS AND DISSEMINATION

The results of this systematic review and IPD-NMA will be of interest to stakeholders and will help in improving existing guideline recommendations.

PROSPERO REGISTRY NUMBER

CRD42015023511.

Get real in individual participant data (IPD) meta-analysis: a review of the methodology

Individual participant data (IPD) meta-analysis is an increasingly used approach for synthesizing and investigating treatment effect estimates. Over the past few years, numerous methods for conducting an IPD meta-analysis (IPD-MA) have been proposed, often making different assumptions and modeling choices while addressing a similar research question. We conducted a literature review to provide an overview of methods for performing an IPD-MA using evidence from clinical trials or non-randomized studies when investigating treatment efficacy. With this review, we aim to assist researchers in choosing the appropriate methods and provide recommendations on their implementation when planning and conducting an IPD-MA.

Challenges and Opportunities for Exploring Patient-Level Data

The proper exploration of patient-level data will pave the way towards personalised medicine. To better assess the state of the art in this field we identify the challenges and uncover the opportunities for the exploration of patient-level data through the review of well-known initiatives and projects focusing on the exploration of patient-level data. These cover a broad array of topics, from genomics to patient registries up to rare diseases research, among others. For each, we identified basic goals, involved partners, defined strategies and key technological and scientific outcomes, establishing the foundation for our analysis framework with four pillars: control, sustainability, technology, and science. Substantial research outcomes have been produced towards the exploration of patient-level data. The potential behind these data will be essential to realise the personalised medicine premise in upcoming years. Hence, relevant stakeholders continually push forward new developments in this domain, bringing novel opportunities that are ripe for exploration. Despite last decade's translational research advances, personalised medicine is still far from being a reality. Patients' data underlying potential goes beyond daily clinical practice. There are miscellaneous challenges and opportunities open for the exploration of these data by academia and business stakeholders.

Meta-analysis of a continuous outcome combining individual patient data and aggregate data: a method based on simulated individual patient data

When some trials provide individual patient data (IPD) and the others provide only aggregate data (AD), meta-analysis methods for combining IPD and AD are required. We propose a method that reconstructs the missing IPD for AD trials by a Bayesian sampling procedure and then applies an IPD meta-analysis model to the mixture of simulated IPD and collected IPD. The method is applicable when a treatment effect can be assumed fixed across trials. We focus on situations of a single continuous outcome and covariate and aim to estimate treatment-covariate interactions separated into within-trial and across-trial effect. An illustration with hypertension data which has similar mean covariates across trials indicates that the method substantially reduces mean square error of the pooled within-trial interaction estimate in comparison with existing approaches. A simulation study supposing there exists one IPD trial and nine AD trials suggests that the method has suitable type I error rate and approximately zero bias as long as the available IPD contains at least 10% of total patients, where the average gain in mean square error is up to about 40%. However, the method is currently restricted by the fixed effect assumption, and extension to random effects to allow heterogeneity is required.

Methods and characteristics of published network meta-analyses using individual patient data: protocol for a scoping review

INTRODUCTION

Individual patient data (IPD) meta-analysis (MA) offers advantages over aggregate MA of using standardised criteria for patient characteristics across trials, and allowing reliable investigation of subgroup effects of interventions. Network meta-analysis (NMA) allows for the comparison of multiple treatments in a comprehensive analysis and the determination of the best treatment among several competing treatments, including those that have never been compared in a head-to-head study. Including IPD in NMA may enable the prevention of misleading inferences due to several biases, such as aggregation bias. Application of IPD-NMA methods in healthcare have begun to appear in medical journals. Our objective is to conduct a scoping review of existing IPD-NMA methods, and summarise their properties. We also aim to describe the characteristics of empirical IPD-NMAs, and examine how their results are reported.

METHODS AND ANALYSIS

We will search relevant electronic databases from inception until October 2014 (eg, MEDLINE), grey literature, and Google. The scoping review will consider published and unpublished papers that report completion of an IPD-NMA, describe a method, or report the methodological quality of IPD-NMA. We will include IPD-NMA of any quantitative study (eg, experimental, quasiexperimental, observational studies). Two reviewers will independently screen titles, abstracts and full-text articles, and will complete data abstraction. The anticipated outcome will be a collection of all the IPD-NMAs completed to date, and a description of their methods and reporting of results. We will create summary tables providing the characteristics of the included studies, and the various methods. Quantitative data (eg, number of patients) will be summarised by medians and IQRs, and categorical data (eg, type of effect size) by frequencies and percentages.

ETHICS AND DISSEMINATION

Ethical approval is not required as our study will not include confidential participant data and interventions. We will disseminate our results through an open access, peer-reviewed publication.

Network meta-analysis combining individual patient and aggregate data from a mixture of study designs with an application to pulmonary arterial hypertension

Background

Network meta-analysis (NMA) is a methodology for indirectly comparing, and strengthening direct comparisons of two or more treatments for the management of disease by combining evidence from multiple studies. It is sometimes not possible to perform treatment comparisons as evidence networks restricted to randomized controlled trials (RCTs) may be disconnected. We propose a Bayesian NMA model that allows to include single-arm, before-and-after, observational studies to complete these disconnected networks. We illustrate the method with an indirect comparison of treatments for pulmonary arterial hypertension (PAH).

Methods

Our method uses a random effects model for placebo improvements to include single-arm observational studies into a general NMA. Building on recent research for binary outcomes, we develop a covariate-adjusted continuous-outcome NMA model that combines individual patient data (IPD) and aggregate data from two-arm RCTs with the single-arm observational studies. We apply this model to a complex comparison of therapies for PAH combining IPD from a phase-III RCT of imatinib as add-on therapy for PAH and aggregate data from RCTs and single-arm observational studies, both identified by a systematic review.

Results

Through the inclusion of observational studies, our method allowed the comparison of imatinib as add-on therapy for PAH with other treatments. This comparison had not been previously possible due to the limited RCT evidence available. However, the credible intervals of our posterior estimates were wide so the overall results were inconclusive. The comparison should be treated as exploratory and should not be used to guide clinical practice.

Conclusions

Our method for the inclusion of single-arm observational studies allows the performance of indirect comparisons that had previously not been possible due to incomplete networks composed solely of available RCTs. We also built on many recent innovations to enable researchers to use both aggregate data and IPD. This method could be used in similar situations where treatment comparisons have not been possible due to restrictions to RCT evidence and where a mixture of aggregate data and IPD are available.

Electronic supplementary material

The online version of this article (doi:10.1186/s12874-015-0007-0) contains supplementary material, which is available to authorized users.

Meta-analysis of absolute mean differences from randomised trials with treatment-related clustering associated with care providers

Nesting of patients within care providers in trials of physical and talking therapies creates an additional level within the design. The statistical implications of this are analogous to those of cluster randomised trials, except that the clustering effect may interact with treatment and can be restricted to one or more of the arms. The statistical model that is recommended at the trial level includes a random effect for the care provider but allows the provider and patient level variances to differ across arms. Evidence suggests that, while potentially important, such within-trial clustering effects have rarely been taken into account in trials and do not appear to have been considered in meta-analyses of these trials. This paper describes summary measures and individual-patient-data methods for meta-analysing absolute mean differences from randomised trials with two-level nested clustering effects, contrasting fixed and random effects meta-analysis models. It extends methods for incorporating trials with unequal variances and homogeneous clustering to allow for between-arm and between-trial heterogeneity in intra-class correlation coefficient estimates. The work is motivated by a meta-analysis of trials of counselling in primary care, where the control is no counselling and the outcome is the Beck Depression Inventory. Assuming equal counsellor intra-class correlation coefficients across trials, the recommended random-effects heteroscedastic model gave a pooled absolute mean difference of -2.53 (95% CI -5.33 to 0.27) using summary measures and -2.51 (95% CI -5.35 to 0.33) with the individual-patient-data. Pooled estimates were consistently below a minimally important clinical difference of four to five points on the Beck Depression Inventory.

Multivariate meta-analysis using individual participant data

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment–covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models.

DataSHIELD: taking the analysis to the data, not the data to the analysis

Background: Research in modern biomedicine and social science requires sample sizes so large that they can often only be achieved through a pooled co-analysis of data from several studies. But the pooling of information from individuals in a central database that may be queried by researchers raises important ethico-legal questions and can be controversial. In the UK this has been highlighted by recent debate and controversy relating to the UK’s proposed ‘care.data’ initiative, and these issues reflect important societal and professional concerns about privacy, confidentiality and intellectual property. DataSHIELD provides a novel technological solution that can circumvent some of the most basic challenges in facilitating the access of researchers and other healthcare professionals to individual-level data.

Methods: Commands are sent from a central analysis computer (AC) to several data computers (DCs) storing the data to be co-analysed. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands transmitted back and forth between the DCs and the AC. This paper describes the technical implementation of DataSHIELD using a modified R statistical environment linked to an Opal database deployed behind the computer firewall of each DC. Analysis is controlled through a standard R environment at the AC.

Results: Based on this Opal/R implementation, DataSHIELD is currently used by the Healthy Obese Project and the Environmental Core Project (BioSHaRE-EU) for the federated analysis of 10 data sets across eight European countries, and this illustrates the opportunities and challenges presented by the DataSHIELD approach.

Conclusions: DataSHIELD facilitates important research in settings where: (i) a co-analysis of individual-level data from several studies is scientifically necessary but governance restrictions prohibit the release or sharing of some of the required data, and/or render data access unacceptably slow; (ii) a research group (e.g. in a developing nation) is particularly vulnerable to loss of intellectual property—the researchers want to fully share the information held in their data with national and international collaborators, but do not wish to hand over the physical data themselves; and (iii) a data set is to be included in an individual-level co-analysis but the physical size of the data precludes direct transfer to a new site for analysis.

Network meta-analysis of (individual patient) time to event data alongside (aggregate) count data

BACKGROUND

Network meta-analysis methods extend the standard pair-wise framework to allow simultaneous comparison of multiple interventions in a single statistical model. Despite published work on network meta-analysis mainly focussing on the synthesis of aggregate data, methods have been developed that allow the use of individual patient-level data specifically when outcomes are dichotomous or continuous. This paper focuses on the synthesis of individual patient-level and summary time to event data, motivated by a real data example looking at the effectiveness of high compression treatments on the healing of venous leg ulcers.

METHODS

This paper introduces a novel network meta-analysis modelling approach that allows individual patient-level (time to event with censoring) and summary-level data (event count for a given follow-up time) to be synthesised jointly by assuming an underlying, common, distribution of time to healing. Alternative model assumptions were tested within the motivating example. Model fit and adequacy measures were used to compare and select models.

RESULTS

Due to the availability of individual patient-level data in our example we were able to use a Weibull distribution to describe time to healing; otherwise, we would have been limited to specifying a uniparametric distribution. Absolute effectiveness estimates were more sensitive than relative effectiveness estimates to a range of alternative specifications for the model.

CONCLUSIONS

The synthesis of time to event data considering individual patient-level data provides modelling flexibility, and can be particularly important when absolute effectiveness estimates, and not just relative effect estimates, are of interest.

Fixed- and random-effects meta-analytic structural equation modeling: examples and analyses in R

Meta-analytic structural equation modeling (MASEM) combines the ideas of meta-analysis and structural equation modeling for the purpose of synthesizing correlation or covariance matrices and fitting structural equation models on the pooled correlation or covariance matrix. Cheung and Chan (Psychological Methods 10:40-64, 2005b, Structural Equation Modeling 16:28-53, 2009) proposed a two-stage structural equation modeling (TSSEM) approach to conducting MASEM that was based on a fixed-effects model by assuming that all studies have the same population correlation or covariance matrices. The main objective of this article is to extend the TSSEM approach to a random-effects model by the inclusion of study-specific random effects. Another objective is to demonstrate the procedures with two examples using the metaSEM package implemented in the R statistical environment. Issues related to and future directions for MASEM are discussed.