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Meta-STEPP with random effects
Xin Victoria Wang1,2, Bernard Cole3, Marco Bonetti4
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA.
Abstract:
We recently developed a method called Meta-STEPP based on the fixed-effects meta-analytic approach to explore treatment effect heterogeneity across a continuous covariate for individual time-to-event data arising from multiple clinical trials. Meta-STEPP forms overlapping subpopulation windows (meta-windows) along a continuous covariate of interest, estimates the overall treatment effect in each meta-window using standard fixed-effects method, plots them against the continuous covariate, and tests for treatment-effect heterogeneity across the range of covariate values. Here, we extend this method using random-effects methods and find it to be more conservative than the fixed-effects method. Both the random- and fixed-effects Meta-STEPP are implemented in R.
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