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Power Analysis of Exposure Mixture Studies Via Monte Carlo Simulations

Author

Listed:
  • Phuc H. Nguyen

    (Duke University)

  • Amy H. Herring

    (Duke University)

  • Stephanie M. Engel

    (The University of North Carolina at Chapel Hill)

Abstract

Estimating sample size and statistical power is an essential part of a good epidemiological study design. Closed-form formulas exist for simple hypothesis tests but not for advanced statistical methods designed for exposure mixture studies. Estimating power with Monte Carlo simulations is flexible and applicable to these methods. However, it is not straightforward to code a simulation for non-experienced programmers and is often hard for a researcher to manually specify multivariate associations among exposure mixtures to set up a simulation. To simplify this process, we present the R package mpower for power analysis of observational studies of environmental exposure mixtures involving recently developed mixtures analysis methods. The components within mpower are also versatile enough to accommodate any mixtures methods that will be developed in future. The package allows users to simulate realistic exposure data and mixed-typed covariates based on public dataset such as the National Health and Nutrition Examination Survey or other existing dataset from prior studies. 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 of power analysis using mpower.

Suggested Citation

  • Phuc H. Nguyen & Amy H. Herring & Stephanie M. Engel, 2024. "Power Analysis of Exposure Mixture Studies Via Monte Carlo Simulations," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 321-346, July.
    • Handle: RePEc:spr:stabio:v:16:y:2024:i:2:d:10.1007_s12561-023-09385-7
    DOI: 10.1007/s12561-023-09385-7
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    References listed on IDEAS

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    1. Lewandowski, Daniel & Kurowicka, Dorota & Joe, Harry, 2009. "Generating random correlation matrices based on vines and extended onion method," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 1989-2001, October.
    2. Joe, Harry, 2006. "Generating random correlation matrices based on partial correlations," Journal of Multivariate Analysis, Elsevier, vol. 97(10), pages 2177-2189, November.
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