Teixeira-Pinto Armando, Mauri Laura
Unit of Biostatistics, Faculty of Medicine and CINTESIS, University of Porto, Porto, Portugal.
Circ Cardiovasc Qual Outcomes. 2011 Nov 1;4(6):650-6. doi: 10.1161/CIRCOUTCOMES.111.961581.
Many studies collect multiple outcomes to characterize treatment effectiveness or evaluate risk factors. These outcomes tend to be correlated because they are measuring related quantities in the same individuals, but the common approach used by researchers is to ignore this correlation and analyze each outcome separately. There may be advantages to consider the simultaneous analysis of the outcomes using multivariate methods. Although the joint analysis of outcomes measured in the same scale (commensurate outcomes) can be undertaken with standard statistical methods, outcomes measured in different scales (noncommensurate outcomes), such as mixed binary and continuous outcomes, present more difficult challenges. In this article, we contrast some statistical approaches to analyze noncommensurate multiple outcomes. We discuss the advantages of a multivariate method for the analysis of noncommensurate outcomes, including situations of missing data. A real data example from a clinical trial, comparing bare-metal with sirolimus-eluting stents, is used to illustrate the differences between the statistical approaches.
许多研究收集多个结果以描述治疗效果或评估风险因素。这些结果往往是相关的，因为它们是在同一受试者中测量相关的量，但研究人员常用的方法是忽略这种相关性并分别分析每个结果。使用多变量方法同时分析这些结果可能会有优势。虽然对以相同尺度测量的结果（相称结果）进行联合分析可以采用标准统计方法，但以不同尺度测量的结果（不相称结果），如混合二元和连续结果，会带来更具挑战性的难题。在本文中，我们对比了一些分析不相称多个结果的统计方法。我们讨论了用于分析不相称结果的多变量方法的优势，包括存在缺失数据的情况。一个来自比较裸金属支架与西罗莫司洗脱支架的临床试验的真实数据示例，用于说明这些统计方法之间的差异。
