Meta-analysis of individual participant data: rationale, conduct, and reporting

Abstract

Meta-analysis methods involve combining and analysing quantitative evidence from related studies to produce results based on a whole body of research. As such, metaanalyses are an integral part of evidence based medicine. Traditional methods for meta-analysis synthesise aggregate study level data obtained from study publications or study authors, such as a treatment effect estimate (for example, an odds ratio) and its associated uncertainty (for example, a standard error or confidence interval). An alternative but increasingly popular approach is meta-analysis of individual participant data, or individual patient data, in which the raw individual level data for each study are obtained and used for synthesis.1 In this article we describe the rationale for individual participant data meta-analysis and illustrate through applied examples why this strategy offers numerous advantages, both clinically and statistically, over the aggregate data approach.1 2 We outline when and how to initiate an individual participant data meta-analysis, the statistical issues in conducting one, how the findings should be reported, and what challenges this approach may bring. What are individual participant data?The term "individual participant data" relates to the data recorded for each participant in a study. In a hypertension trial, for example, the individual participant data could be the pre-treatment and post-treatment blood pressure, a treatment group indicator, and important baseline clinical characteristics such as age and sex, for each patient in each study (table). A set of individual participant data from multiple studies often comprises thousands of patients; this is the case in the table, so for brevity we do not show all rows of data here. This concept is in contrast to the term "aggregate data," which relates to information averaged or estimated across all individuals in a study, such as the mean treatment effect on blood pressure, the mean age, or the proportion of participants who are male. Such aggregate data are derived from the individual participant data themselves, so individual participant data can be considered the original source material. What is an individual participant data meta-analysis?As with any meta-analysis, an individual participant data meta-analysis aims to summarise the evidence on a particular clinical question from multiple related studies, such as whether a treatment is effective. The statistical implementation of an individual participant data meta-analysis crucially must preserve the clustering of patients within studies; it is inappropriate to simply analyse individual participant data as if they all came from a single study. Clusters can be retained during analysis by using a two step or a one step approach. 3 In the two step approach, the individual participant data are first analysed in each separate study independently by using a statistical method appropriate for the type of data being analysed; for example, a linear regression model might be fitted for continuous responses such as blo...

Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ

“…For example, IPD allows the meta‐analyst to standardise the inclusion criteria and analyses across studies, to obtain study results that had not been provided by the trial publications and to check modelling assumptions 3. An important advantage is being able to model individual‐level interactions directly within studies, which has substantially greater power and avoids ecological bias compared with a meta‐regression of aggregate data across studies 4, 5. For such reasons, there has been an increase in the number of IPD meta‐analyses in the last decade 5, 6.…”

“…An important advantage is being able to model individual‐level interactions directly within studies, which has substantially greater power and avoids ecological bias compared with a meta‐regression of aggregate data across studies 4, 5. For such reasons, there has been an increase in the number of IPD meta‐analyses in the last decade 5, 6.…”

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

“…Here, the participant‐level data are analysed in either a two‐stage or a one‐stage approach in order to summarize the interaction between treatment effect and individual covariates 10, 11, 12, 13. The two‐stage approach is the most straightforward, where firstly the treatment‐covariate interactions are estimated in each trial separately, and then secondly, these are pooled using a traditional (e.g.…”

“…Data sharing is becoming expected in medical research 29, and the number of IPD meta‐analyses is rising 11, 30, many of which aim to identify treatment effect modifiers. New protocols are being published each month for IPD meta‐analyses, which pre‐define their statistical analysis plan.…”

The acute effects of physical activity on cigarette cravings: Exploration of potential moderators, mediators and physical activity attributes using individual participant data (IPD) meta-analyses

“…This paper aims to address these issues using IPD metaanalysis methods. Compared with meta-analysis using aggregate data, IPD meta-analysis allows adjustment for participant level baseline covariates, which may increase the power to detect a treatment effect (Riley et al 2010); also, the use of IPD is beneficial when exploration of associations between treatments and patient-level characteristics is important (Cochrane handbook, 2011). If data are available at baseline and post-intervention, IPD meta-analysis also facilitates the investigation of potential mediators of the intervention.…”