Practical methodology of meta-analysis of individual patient data using a survival outcome.
Meta-analysis of individual patient data (MIPD) is considered as one of the statistical approaches to provide integrated information on the effect of a treatment or an intervention. Statistical analysis of such meta-analyses should account for the clustered structure of data which is induced by all factors varying across the trials. For survival analysis, several models can handle such clustering under proportional hazards. This comprises models with fixed or random trial effects, stratified models and marginal models. In this paper, we review these models and compare their performances using a numerical simulation study. Results show that frailty models, and particularly those with random treatment by trial interactions, are well suited for meta-analyses on individual patient data. This is further exemplified on a meta-analysis of three trials comparing high-dose therapy to conventional chemotherapy in multiple myeloma.
Katsahian
Sandrine
S
Département de Biostatistique et Informatique Médicale, Hôpital Saint-Louis, AP-HP, Université Paris 7, Paris, France; Inserm U717, Paris, France. sandrine.katsahian@paris7.jussieu.fr
Latouche
Aurélien
A
Mary
Jean-Yves
JY
Chevret
Sylvie
S
Porcher
Raphaël
R
D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D009101:Multiple Myeloma / Q000401:mortality / Q000628:therapy; D016011:Normal Distribution; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic; D016019:Survival Analysis