Predictive modeling and heterogeneity of baseline risk in meta-analysis of individual patient data

Abstract

We developed and evaluated methods for the analysis and interpretation of the baseline risk heterogeneity in meta-analysis of individual patient data (MIPD) based on information on predictive factors. We used data from a typical MIPD of eight clinical trials (1792 patients, 2947 years of follow-up) on the efficacy of high-dose acyclovir in human immunodeficiency virus infection. Cox models with four predictive factors (age, disease state, CD4 cell count and hemoglobin levels) were used to estimate predicted individual hazards both for single trials and for various MIPD modeling methods (simple pooling, adjusted for study, stratified per study, fixed and random effects for predictors). For each study and for each method of MIPD synthesis, we estimated the odds ratio for death in the upper versus the lower quartile of predicted risk (Extreme Quartile Odds Ratio, EQuOR) and the respective rate ratio (Extreme Quartile Rate Ratio, EQuRR). Only the CD4 cell count showed a significantly heterogeneous predictive effect across the eight studies ( P = .024). The EQuOR of single studies ranged from 3.5 (little heterogeneity) to 24 (intermediate heterogeneity), substantially lower than the EQuOR of the MIPD (167 to 275, depending on the model used). The EQuRR values ranged from 3.5 to 77 for single studies and from 77 to 116 for the various MIPD models. Predictive modeling can be a major strength of MIPD, when performed and interpreted with standardized approaches. All models consistently show that MIPD may be a study design with extreme heterogeneity of patient baseline risk.