Commentary: Like it and lump it? Meta-analysis using individual participant data

Commentary: like it and lump it? Meta-analysis using individual participant data.

Perry MBChB, University of Cambridge, UK; KK Ray MD, University of Cambridge, UK; N Sarwar PhD, University of Cambridge, UK; M Alexander MPhil, University of Cambridge, UK; A Thompson PhD, University of Cambridge, UK; SG Thompson DSc, MRC Biostatistics Unit, UK; M Walker PhD, University of Cambridge, UK; S Watson MMath, University of Cambridge, UK; F Wensley MSc, University of Cambridge, UK; IR...

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...

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

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...