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.