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      Statistical Analysis of Individual Participant Data Meta-Analyses: A Comparison of Methods and Recommendations for Practice


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          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.

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          Most cited references12

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          Antiplatelet agents for prevention of pre-eclampsia: a meta-analysis of individual patient data.

          Pre-eclampsia is a major cause of mortality and morbidity during pregnancy and childbirth. Antiplatelet agents, especially low-dose aspirin, might prevent or delay pre-eclampsia, and thereby improve outcome. Our aim was to assess the use of antiplatelet agents for the primary prevention of pre-eclampsia, and to explore which women are likely to benefit most. We did a meta-analysis of individual patient data from 32,217 women, and their 32,819 babies, recruited to 31 randomised trials of pre-eclampsia primary prevention. For women assigned to receive antiplatelet agents rather than control, the relative risk of developing pre-eclampsia was 0.90 (95% CI 0.84-0.97), of delivering before 34 weeks was 0.90 (0.83-0.98), and of having a pregnancy with a serious adverse outcome was 0.90 (0.85-0.96). Antiplatelet agents had no significant effect on the risk of death of the fetus or baby, having a small for gestational age infant, or bleeding events for either the women or their babies. No particular subgroup of women was substantially more or less likely to benefit from antiplatelet agents than any other. Antiplatelet agents during pregnancy are associated with moderate but consistent reductions in the relative risk of pre-eclampsia, of birth before 34 weeks' gestation, and of having a pregnancy with a serious adverse outcome.
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            Individual patient- versus group-level data meta-regressions for the investigation of treatment effect modifiers: ecological bias rears its ugly head.

            When performing a meta-analysis, interest often centres on finding explanations for heterogeneity in the data, rather than on producing a single summary estimate. Such exploratory analyses are frequently undertaken with published, study-level data, using techniques of meta-analytic regression. Our goal was to explore a real-world example for which both published, group-level and individual patient-level data were available, and to compare the substantive conclusions reached by both methods. We studied the benefits of anti-lymphocyte antibody induction therapy among renal transplant patients in five randomized trials, focusing on whether there are subgroups of patients in whom therapy might prove particularly beneficial. Allograft failure within 5 years was the endpoint studied. We used a variety of analytic approaches to the group-level data, including weighted least-squares regression (N=5 studies), logistic regression (N=628, the total number of subjects), and a hierarchical Bayesian approach. We fit logistic regression models to the patient-level data. In the patient-level analysis, we found that treatment was significantly more effective among patients with elevated (20 per cent or more) panel reactive antibodies (PRA) than among patients without elevated PRA. These patients comprise a small (about 15 per cent of patients) subgroup of patients that benefited from therapy. The group-level analyses failed to detect this interaction. We recommend using individual patient data, when feasible, to study patient characteristics, in order to avoid the potential for ecological bias introduced by group-level analyses. Copyright 2002 John Wiley & Sons, Ltd.
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              Meta-analysis of individual patient data from randomized trials: a review of methods used in practice.

              Meta-analyses based on individual patient data (IPD) are regarded as the gold standard for systematic reviews. However, the methods used for analysing and presenting results from IPD meta-analyses have received little discussion. We review 44 IPD meta-analyses published during the years 1999-2001. We summarize whether they obtained all the data they sought, what types of approaches were used in the analysis, including assumptions of common or random effects, and how they examined the effects of covariates. Twenty-four out of 44 analyses focused on time-to-event outcomes, and most analyses (28) estimated treatment effects within each trial and then combined the results assuming a common treatment effect across trials. Three analyses failed to stratify by trial, analysing the data is if they came from a single mega-trial. Only nine analyses used random effects methods. Covariate-treatment interactions were generally investigated by subgrouping patients. Seven of the meta-analyses included data from less than 80% of the randomized patients sought, but did not address the resulting potential biases. Although IPD meta-analyses have many advantages in assessing the effects of health care, there are several aspects that could be further developed to make fuller use of the potential of these time-consuming projects. In particular, IPD could be used to more fully investigate the influence of covariates on heterogeneity of treatment effects, both within and between trials. The impact of heterogeneity, or use of random effects, are seldom discussed. There is thus considerable scope for enhancing the methods of analysis and presentation of IPD meta-analysis.

                Author and article information

                Role: Editor
                PLoS One
                PLoS ONE
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                3 October 2012
                : 7
                : 10
                [1 ]Centre for Reviews and Dissemination, University of York, York, United Kingdom
                [2 ]Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom
                [3 ]NHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia
                [4 ]Nottingham Clinical Trials Unit, University of Nottingham, Nottingham, United Kingdom
                Sapienza University of Rome, Italy
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: GS DA LA LD MS LS. Performed the experiments: GS MS. Analyzed the data: GS DA LA LD MS LS. Contributed reagents/materials/analysis tools: GS MS. Wrote the paper: GS DA LA LD MS LS.


                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Pages: 8
                This project was funded by the MRC as part of the MRC-NIHR Methodology Research Programme. Grant ID 88053. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Research Article
                Confidence Intervals
                Statistical Methods
                Clinical Research Design
                Survey Research
                Obstetrics and Gynecology
                Hypertensive Disorders in Pregnancy
                Management of High-Risk Pregnancies
                Science Policy
                Research Assessment



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