Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Pharmacoepidemiol Drug Saf. 2011 Jul;20(7):675-83. doi: 10.1002/pds.2121. Epub 2011 May 30.
A key aspect of comparative effectiveness research is the assessment of competing treatment options and multiple outcomes rather than a single treatment option and a single benefit or harm. In this commentary, we describe a methodological framework that supports the simultaneous examination of a "matrix" of treatments and outcomes in non-randomized data.
We outline the methodological challenges to a matrix-type study (matrix design). We consider propensity score matching with multiple treatment groups, statistical analysis, and choice of association measure when evaluating multiple outcomes. We also discuss multiple testing, use of high-dimensional propensity scores for covariate balancing in light of multiple outcomes, and suitability of available software.
The matrix design study methods facilitate examination of the comparative benefits and harms of competing treatment choices, and also provides the input required for calculating the numbers needed to treat and for a broader benefit/harm assessment that weighs endpoints of varying severity.
比较实效研究的一个关键方面是评估竞争治疗方案和多个结果，而不是单一治疗方案和单一益处或危害。在这篇评论中，我们描述了一个方法框架，该框架支持在非随机数据中同时检查“矩阵”治疗方案和结果。
我们概述了矩阵式研究（矩阵设计）的方法学挑战。我们考虑了在评估多个结果时，使用多个治疗组的倾向评分匹配、统计分析和关联度量的选择。我们还讨论了在考虑多个结果时，为了进行协变量平衡而使用高维倾向评分以及可用软件的适用性。
矩阵设计研究方法有利于检查竞争治疗选择的相对益处和危害，还为计算需要治疗的人数以及更广泛的益处/危害评估提供了所需的投入，该评估权衡了不同严重程度的终点。
