mpower: An R Package for Power Analysis of Exposure Mixture Studies via Monte Carlo Simulations

TL;DR

mpower is an R package that enables power analysis for exposure mixture studies using Monte Carlo simulations, accounting for predictor correlations and complex data structures.

Contribution

It introduces a flexible tool for conducting power analysis with Monte Carlo simulations that incorporate dependence structures among variables in environmental studies.

Findings

Supports data generative models preserving variable dependence

Allows generation of power curves for study design optimization

Facilitates application to correlated environmental exposure data

Abstract

Estimating sample size and statistical power is an essential part of a good study design. This R package allows users to conduct power analysis based on Monte Carlo simulations in settings in which consideration of the correlations between predictors is important. It runs power analyses given a data generative model and an inference model. It can set up a data generative model that preserves dependence structures among variables given existing data (continuous, binary, or ordinal) or high-level descriptions of the associations. Users can generate power curves to assess the trade-offs between sample size, effect size, and power of a design. This paper presents tutorials and examples focusing on applications for environmental mixture studies when predictors tend to be moderately to highly correlated. It easily interfaces with several existing and newly developed analysis strategies for assessing associations between exposures and health outcomes. However, the package is sufficiently general to facilitate power simulations in a wide variety of settings.