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一种用于进行重复测量功效计算的实用且准确的近似方法。

A Practical and Accurate Approximation for Carrying Out Repeated Measures Power Calculations.

作者信息

Hutson Alan D

机构信息

Department of Biostatistics, 706 Kimball Tower, 3435 Main Street, University at Buffalo, Buffalo, NY 14214-3000.

出版信息

Commun Stat Case Stud Data Anal Appl. 2015;1(3):161-167. doi: 10.1080/23737484.2015.1129917. Epub 2016 Feb 24.

DOI:10.1080/23737484.2015.1129917
PMID:28154841
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5279913/
Abstract

Oftentimes consulting researchers are faced with generating power calculations for pilot designs that entail complex correlation/covariance structures. In many of these instances it is necessary for the statistician to elicit information pertaining to the correlation/covariance structure from researchers with little or no statistical background. We detail a simple strategy that seems to work well with clients for standard repeated measures normal based models. The algorithm is based solely on eliciting minimum and maximum values. The procedure allows a statistician to develop a "baseline" for parameter values used in the power calculations. We then illustrate how to proceed with the calculations using simulation techniques and normal based approximations.

摘要

通常情况下,咨询研究人员在为涉及复杂相关/协方差结构的试点设计进行功效计算时会面临挑战。在许多这类情况下,统计学家有必要从几乎没有或完全没有统计学背景的研究人员那里获取与相关/协方差结构有关的信息。我们详细介绍一种简单的策略,该策略在基于标准重复测量正态模型的客户中似乎效果良好。该算法仅基于获取最小值和最大值。此过程使统计学家能够为功效计算中使用的参数值制定一个“基线”。然后,我们说明如何使用模拟技术和基于正态的近似方法进行计算。