Teixeira-Pinto Armando, Siddique Juned, Gibbons Robert, Normand Sharon-Lise
Department of Biostatistics and Medical Informatics, Faculty of Medicine, CINTESIS, University of Porto, Porto, Portugal.
Psychiatr Ann. 2009 Jul 1;39(7):729-735. doi: 10.3928/00485713-20090625-08.
Increasingly, multiple outcomes are collected in order to characterize treatment effectiveness or to evaluate risk factors. These outcomes tend to be correlated because they are measuring related quantities in the same individuals. While the analysis of outcomes measured in the same scale (commensurate outcomes) can be undertaken with standard statistical methods, outcomes measured in different scales (non-commensurate outcomes), such as mixed binary and continuous outcomes, present more difficult challenges.In this paper we contrast some statistical approaches to analyze non-commensurate multiple outcomes. We discuss the advantages of a multivariate method for the analysis of non-commensurate outcomes including situations of missing data. A real data example from a clinical trial, comparing different treatments for depression in low-income women, is used to illustrate the differences between the statistical approaches.
为了描述治疗效果或评估风险因素，越来越多地收集多种结果。这些结果往往是相关的，因为它们测量的是同一批个体中的相关量。虽然对于以相同尺度测量的结果（相称结果）可以采用标准统计方法进行分析，但以不同尺度测量的结果（非相称结果），如混合二元和连续结果，带来了更具挑战性的难题。在本文中，我们对比了一些用于分析非相称多种结果的统计方法。我们讨论了一种多变量方法在分析非相称结果（包括数据缺失情况）方面的优势。一个来自临床试验的实际数据示例，比较了低收入女性抑郁症的不同治疗方法，用于说明这些统计方法之间的差异。
