Efficiency and Power of Tests for Multiple Binary Outcomes
多个二元结局检验的效率与功效AI 解读 ↓
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多个二元结局检验的效率与功效
- 研究问题
- 比较多个组的相关二元结局时，共同结局效应检验与允许各结局效应不同的全局检验，在效率和功效上如何取舍？
- 方法与思路
- 作者推广多组得分检验，用多元极坐标描述备择空间，通过渐近相对效率及模拟比较多种全局检验。
- 材料陈述的贡献
- 摘要称共同结局效应检验在较广情形下有效，并给出容纳无效应结局数量的计算公式，部分其他检验也表现良好。
- 阅读关注点
- 应核对结局相关结构、共同效应和多维备择的定义，以及无效应结局数量公式与模拟比较的适用条件。
- 阅读边界
- 所给材料为版本未注明的 OpenAlex 索引摘要；效率优势限于所考察情形，不能由二元结局推断离散选择或因果识别。
原文摘要
Global tests provide a useful tool for comparing two or more groups with respect to multiple correlated outcomes. We adapt and compare the performance of tests that have been suggested for use with multiple continuous outcomes to the case of multiple binary outcomes. Comparisons and guidelines are based on asymptotic relative efficiencies (ARE's) and simulations. These results are illustrated using an application from teratology. We extend the work of Lefkopoulou and Ryan to include general M-group comparisons alternatives where group effects may differ for each outcome. A concise form for this general class of score tests is derived. To compute the ARE's for this class of tests, we devise a useful characterization of the alternative space based on multivariate polar coordinates. Our findings indicate that the common outcome effect tests are efficient for a remarkably large range of circumstances. A simple formula applies to compute the maximum number of unaffected outcomes that can be included in a set of outcomes for which the common outcome effect tests remain more efficient than those derived under multidimensional alternatives. For comparison, other global tests are also considered in the simulations: two tests based on resampling (maximal and minimal z tests), a rank-sum test, a generalized least squares test, and a test based on collapsing multiple endpoints to a single binary outcome. Besides the common outcome effect tests, the resampling tests and the rank-sum test are found to perform very well for the cases under consideration.