mpower：一个通过蒙特卡洛模拟进行暴露混合物研究功效分析的R包
mpower: An R Package for Power Analysis of Exposure Mixture Studies via Monte Carlo Simulations
AI总结：
本研究推出R包mpower，可基于蒙特卡洛模拟开展暴露混合物等场景的功效分析，支持保留变量依赖结构，生成功效曲线，适配多种关联分析策略，通用性较强。
摘要：
估算样本量和统计功效是良好研究设计的核心组成部分。该R包支持用户在预测变量间相关性的考量十分重要的场景下，基于蒙特卡洛模拟开展功效分析。它在给定数据生成模型和推断模型的条件下执行功效分析，可基于现有数据（连续型、二分类或有序型）或关联的高层描述，构建能保留变量间依赖结构的数据生成模型。用户可生成功效曲线，以评估研究设计中样本量、效应量与功效之间的权衡关系。本文展示了聚焦于环境混合物研究应用的教程与示例，这类研究中预测变量往往呈中等至高度相关。该包可轻松对接多种现有及新开发的分析策略，用于评估暴露与健康结局之间的关联，且其通用性足以支持各类场景下的功效模拟。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
