mpower: An R Package for Power Analysis of Exposure Mixture Studies via Monte Carlo Simulations
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916203686002688 |
|---|---|
| author | Nguyen, Phuc H. Engel, Stephanie M. Herring, Amy H. |
| author_facet | Nguyen, Phuc H. Engel, Stephanie M. Herring, Amy H. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_08036 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | mpower: An R Package for Power Analysis of Exposure Mixture Studies via Monte Carlo Simulations Nguyen, Phuc H. Engel, Stephanie M. Herring, Amy H. Methodology Computation 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. |
| title | mpower: An R Package for Power Analysis of Exposure Mixture Studies via Monte Carlo Simulations |
| topic | Methodology Computation |
| url | https://arxiv.org/abs/2209.08036 |