随机对照试验
估计员
分数(化学)
计算机科学
统计
计量经济学
随机反应
结果(博弈论)
数学
医学
数学优化
数理经济学
外科
有机化学
化学
作者
Emily J. Huang,Ethan X. Fang,Daniel F. Hanley,Michael Rosenblum
出处
期刊:Biostatistics
[Oxford University Press]
日期:2016-12-26
卷期号:: kxw049-kxw049
被引量:5
标识
DOI:10.1093/biostatistics/kxw049
摘要
In many randomized controlled trials, the primary analysis focuses on the average treatment effect and does not address whether treatment benefits are widespread or limited to a select few. This problem affects many disease areas, since it stems from how randomized trials, often the gold standard for evaluating treatments, are designed and analyzed. Our goal is to learn about the fraction who benefit from a new treatment using randomized trial data. We consider the case where the outcome is ordinal, with binary outcomes as a special case. In general, the fraction who benefit is non-identifiable, and the best that can be obtained are sharp lower and upper bounds. Our contributions include (i) proving the plug-in estimator of the bounds can be inconsistent if support restrictions are made on the joint distribution of the potential outcomes; (ii) developing the first consistent estimator for this case; and (iii) applying this estimator to a randomized trial of a medical treatment to determine whether the estimates can be informative. Our estimator is computed using linear programming, allowing fast implementation. R code is provided.
科研通智能强力驱动
Strongly Powered by AbleSci AI