Methods: Based on our experience of analyzing individual participant data for the past 10 years for Project INTEGRATE, we present three major analytic approaches to validly analyze individual participant data from multiple sources in data applications.
Results: The first approach is to combine data from different studies into a single data pool while treating study membership as a fixed effect and adjusting for standard errors using a sandwich estimator to adjust for nested data structure. The second approach is to analyze individual participant data separately and sequentially for each study and combine them across studies using meta-analysis models (typically called a two-step IPD meta-analysis approach). The third approach is to analyze individual participant data in one simultaneous analysis (also called a one-step individual participant data meta-analysis or IDA). Across all approaches, it is required the interpretation of results should be equivalent across studies, which is a major challenge due to between-study heterogeneity. We provide illustrative examples of the innovative statistical analyses that we have undertaken to make sure that resulting data interpretation is valid. We have conducted simulation studies to make sure our computing codes correctly recover true parameters. Furthermore, we have also conducted sensitivity analysis, such as leaving one study out at a time and repeating analysis, to assess how robust findings are.
Conclusions: The use of individual participant data in meta-analysis presents major opportunities for new discoveries and evidence based practices.