Mediation analysis is a widely used statistical technique for identifying the mechanisms underlying the relationship between an exposure and an outcome. However, accurate power analysis and sample size determination for mediation models that involve non-normal distributions or mixtures of continuous and binary variables are challenging. We propose a computationally efficient simulation-based approach for general mediation analysis. By applying monotone smoothing splines to estimate empirical critical values derived from extensive simulations, our method enables accurate power calculations without the need for real-time simulation. We validated the method across varying scenarios, including continuous, binary variables and time-to-event outcome with strict Type I error control. The method-quantified large effects (0.35) yielded >80% power at minimal sample sizes (n = 25–50) across all settings, while small effects (0.02) required larger samples. Continuous models achieved 80% power for small effects at n = 410, whereas fully binary models required n > 500. For medium effects (0.15), the power was >0.80 at n = 75 with binary mediators. This study presents a robust framework that combines the flexibility of simulation-based inference with the speed of analytical approximations. We provide an accompanying R package to facilitate efficient sample size planning for mediation models.
Rizvi et al. (2026) studied this question.