Classical analytical methods for detecting matching effects on treatment outcome.

P W Wirtz, J P Carbonari, L R Muenz, R L Stout, J S Tonigan, G J Connors
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引用次数: 4

Abstract

This article presents a classical approach for analyzing repeated measures designs with specific application to treatment matching studies. The generic treatment matching hypothesis is formulated under the multivariate general linear model, transforming the dependent variables to account for the repeated measures structure of the data. Issues of primary importance in the use of this approach (such as correcting for inflated Type I error and robustness of statistical tests to parametric assumptions) are discussed. The article concludes with an assessment of the strengths and weaknesses of this approach compared with alternative approaches.

检测治疗结果匹配效应的经典分析方法。
本文提出了一种经典的方法来分析重复测量设计,并具体应用于治疗匹配研究。通用处理匹配假设是在多元一般线性模型下制定的,转换因变量以解释数据的重复测量结构。讨论了在使用这种方法中最重要的问题(如修正膨胀的I型误差和统计检验对参数假设的稳健性)。文章最后对该方法与其他方法相比的优缺点进行了评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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