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。各种特征和有效的功率和有效方法 已经开发了广义线性模型（GLM）中的PLE大小（PSS）估计。这些是主要的 二进制数据的连续数据和逻辑回归的线性模型区域。总的来说，方法是 更一般需要先前对几个研究细节的规范，以便在一个方面实施 Ticular应用程序，这可能会对应用的统计学家和非任命合作者构成挑战。这些 挑战阻碍了转化心理健康研究，随机临床试验的研究设计 精神病学人群以及其他涉及GLM的投资，以线性模型没有的方式。克服 这些挑战，该项目的总体目标是开发估计功率，所需样本的方法 涉及GLM的研究设计中的大小，或最小可检测的效果大小。为了广泛有用，这种方法 应该是准确，可解释的，并且重要的是易于规定。 该项目在GLM的框架内提供了一个通用公式，目的是重新捕获，以关闭近似 模仿，线性模型PSS的通用方法的特征，即使用部分多个R平方 作为效果大小的一般度量。这是通过引入两个R平方的GLM类似物来完成的。当地的 并考虑了更多截然不同的替代假设，后者需要更多的关注才能获得准确的重复 随着替代假设的进一步发展，苏尔特人从零零移动。 WALD和得分测试（这是一致的 也考虑了线性模型）。该项目具有三个特定的目的：使用多个的新型GLM类似物 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。目标3 是开发，测试，记录和传播实施新方法的软件。开发的方法 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- 构成如此多分析的基础的“面包和燃料”类别的“面包和布料”类别的时间 现代生物医学和公共卫生调查的方法。