An Alternative to Pooling Kaplan-Meier Curves in Time-to-Event Meta-Analysis

Abstract

A meta-analysis that uses individual-level data instead of study-level data is widely considered to be a gold standard approach, in part because it allows a time-to-event analysis. Unfortunately, with the common practice of presenting Kaplan-Meier survival curves after pooling subjects across randomized trials, using individual-level data can actually be a step backwards; a Simpson's paradox can occur in which pooling incorrectly reverses the direction of an association. We introduce a nonparametric procedure for synthesizing survival curves across studies that is designed to avoid this difficulty and preserve the integrity of randomization. The technique is based on a counterfactual formulation in which we ask what pooled survival curves would look like if all subjects in all studies had been assigned treatment, or if all subjects had been assigned to control arms. The method is related to a Kaplan-Meier adjustment proposed in 2005 by Xie and Liu to correct for confounding in nonrandomized studies, but is formulated for the meta-analysis setting. The procedure is discussed in the context of examining rosiglitazone and cardiovascular adverse events.