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

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

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 ...

IPDMA: Individual Patient/Participant Data Meta-Analysis

In a previous article in KT Update (Dijkers, 2014), the development of meta-analysis (MA) was sketched from (1) meta-analysis of individual study findings (e.g., means and standard deviations, correlations) reported in the literature, to (2) meta-analysis of individual subject data harvested from the published literature, to (3) merging of datasets from multiple studies that share subject popul...

How individual participant data meta-analyses have influenced trial design, conduct, and analysis

OBJECTIVES To demonstrate how individual participant data (IPD) meta-analyses have impacted directly on the design and conduct of trials and highlight other advantages IPD might offer. STUDY DESIGN AND SETTING Potential examples of the impact of IPD meta-analyses on trials were identified at an international workshop, attended by individuals with experience in the conduct of IPD meta-analyses...

Combining multiple imputation and meta-analysis with individual participant data

Multiple imputation is a strategy for the analysis of incomplete data such that the impact of the missingness on the power and bias of estimates is mitigated. When data from multiple studies are collated, we can propose both within-study and multilevel imputation models to impute missing data on covariates. It is not clear how to choose between imputation models or how to combine imputation and...