论文标题
MPOPTOR：通过Monte Carlo模拟进行暴露混合物研究功率分析的R包装
mpower: An R Package for Power Analysis of Exposure Mixture Studies via Monte Carlo Simulations
论文作者
论文摘要
估计样本量和统计能力是良好研究设计的重要组成部分。该R软件包允许用户在设置中基于蒙特卡洛模拟进行功率分析，在这些设置中，考虑预测变量之间的相关性很重要。它在给定数据生成模型和推理模型的情况下运行功率分析。它可以设置一个数据生成模型，该模型可以保留给定现有数据（连续，二进制或序数）或关联的高级描述的变量之间的依赖性结构。用户可以生成功率曲线，以评估样本量，效果大小和设计功率之间的权衡。本文介绍了当预测因子往往适度到高度相关时，介绍了针对环境混合研究应用的教程和示例。它很容易与几种现有和新开发的分析策略进行互动，以评估暴露与健康成果之间的关联。但是，该包装足以促进各种环境中的电源模拟。
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.
