Power and sample size for generalized linear models

广义线性模型的功效和样本量

• 批准号: 10680962
• 负责人: Paul Joseph Rathouz
• 金额: $ 24.49万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10680962
• 关键词: Age; Applications Grants; Area; Attention; Chi-Square Distribution; Computer software; Data; Distal; Ensure; Formulation; Foundations; Freedom; Future; Goals; Investigation; Joints; Left; Linear Models; Logistic Regressions; Manuscripts; Mathematics; Measures; Mental Health; Methods; Modeling; Modernization; Nature; Outcome; Paper; Population; Probability; Procedures; Process; Public Health; Research; Research Design; Research Personnel; Resources; Sample Size; Specific qualifier value; Statistical Data Interpretation; Testing; Time; Work; analog; base; expectation; experience; in silico; indexing; interest; novel; novel strategies; randomized, clinical trials; response; sex; simulation; software development; usability

PROJECT SUMMARY / ABSTRACT This project responds to FOA PA-21-235. A variety of well-characterized and valid methods for power and sam- ple size (PSS) estimation in generalized linear models (GLM) have been developed. These are primarily in the area of linear models for continuous data and logistic regression for binary data. By and large, methods that are more general require a prior specification of several study-specific details in order to be implemented in a par- ticular application, and this can pose challenges for applied statisticians and non-statistical collaborators. These challenges have impeded the study design of translational mental health research, randomized clinical trials in psychiatric populations, and other investigations that involve GLM in ways that linear models do not. To overcome these challenges, the overarching goal of this project is to develop methods for estimating power, needed sample size, or minimally-detectable effect size in study designs involving GLMs. To be broadly useful, such methods should be accurate, interpretable, and, importantly, easily-specified. This project offers, in the framework of GLMs, a general formulation with the aim to recapture, to close approx- imation, features of the common approach to PSS for linear models, namely the use of partial multiple R-squared as a general measure of effect size. This is accomplished by introducing two GLM analogues of R-squared. Local and more distal alternative hypotheses are considered, the latter requiring more attention to yield accurate re- sults as the alternative hypothesis moves further from the null. Both Wald and score tests (which coincide under linear models) are also considered. This project has three specific aims: Using novel GLM analogues of multiple partial R-squared for linear models, develop approaches to estimate power, needed sample size, or minimally detectable effect size for Wald (Aim 1) and score (Aim 2) tests to be conducted in the framework of GLMs. Aim 3 is to develop, test, document and disseminate software implementing the new methods. The developed methods are guided by and applied to two collaborative projects in translational mental health. The expectation is a new and general suite of applicable and usable approaches to power and sample size es- timation for the “bread-and-butter” class of generalized linear models forming the foundation of so many analysis methods in modern biomedical and public health investigation.

项目摘要 /摘要 该项目响应FOA PA-21-235。 已开发了普通线性模型（GLM）中的PLE尺寸（PSS）。 二进制数据的连续数据和逻辑回归的线性模型。 更一般性需要先前的特定研究专门的细节，以便在一个方面实施 ticular应用，这可能会为应用静态和非静态代植物带来挑战。 挑战阻碍了转化心理健康研究的研究设计，随机临床 精神病学和其他涉及GLM的投资以线性模型无法克服的方式 这些挑战是这个挑战的总体目标 研究设计中的尺寸或最小的可检测效应大小与GLM相关。 应该是牙周，可解释的，并且重要的是易于规定。 在GLM的框架内，该项目提供了一种一般配方，目的是重新捕获，以关闭近似 Imaction，即线性模型PSS的常见方法的特征，即使用部分多个R平方 作为效果大小的一般度量，这是通过引入R平方的GLLM类似物来完成的。 并考虑了更多远端替代假设，后者需要更多的关注才能获得准确的重复 随着替代性瞬变的态度，sults从零下和得分测试中进一步移动。 线性模型也被考虑。 线性模型的部分R平方，开发估计功率，所需样本量或最少的方法 WALD（AIM 1）和得分（AIM 2）测试的可检测效应大小在GLM的框架中进行。 是开发，测试，记录和传播新方法的软件 受到转化心理健康方面的两个协作项目的指导并应用。 期望是一套新的，一般的套件，包括适用的和可用的电力和样本量ES- 构成许多分析的基础的“面包和布料”类别的“面包和布料”类别的定时 现代生物医学和公共卫生调查的方法。