Meta STEPP: subpopulation treatment effect pattern plot for individual patient data meta analysis.
We have developed a method, called Meta STEPP (subpopulation treatment effect pattern plot for meta analysis) to explore treatment effect heterogeneity across covariate values in the meta analysis setting for time to event data when the covariate of interest is continuous. Meta STEPP forms overlapping subpopulations from individual patient data containing similar numbers of events with increasing covariate values, estimates subpopulation treatment effects using standard fixed effects meta analysis methodology, displays the estimated subpopulation treatment effect as a function of the covariate values, and provides a statistical test to detect possibly complex treatment covariate interactions. Simulation studies show that this test has adequate type I error rate recovery as well as power when reasonable window sizes are chosen. When applied to eight breast cancer trials, Meta STEPP suggests that chemotherapy is less effective for tumors with high estrogen receptor expression compared with those with low expression. Copyright (c) 2016 John Wiley Sons, Ltd.
Statistics in medicine