Lui Kung-Jong, Chang Kuang-Chao
Department of Mathematics and Statistics, College of Sciences, San Diego State University, San Diego, CA, 92182-7720, USA.
Biom J. 2013 Jul;55(4):603-16. doi: 10.1002/bimj.201200224. Epub 2013 May 2.
The proportion ratio (PR) of responses between an experimental treatment and a control treatment is one of the most commonly used indices to measure the relative treatment effect in a randomized clinical trial. We develop asymptotic and permutation-based procedures for testing equality of treatment effects as well as derive confidence intervals of PRs for multivariate binary matched-pair data under a mixed-effects exponential risk model. To evaluate and compare the performance of these test procedures and interval estimators, we employ Monte Carlo simulation. When the number of matched pairs is large, we find that all test procedures presented here can perform well with respect to Type I error. When the number of matched pairs is small, the permutation-based test procedures developed in this paper is of use. Furthermore, using test procedures (or interval estimators) based on a weighted linear average estimator of treatment effects can improve power (or gain precision) when the treatment effects on all response variables of interest are known to fall in the same direction. Finally, we apply the data taken from a crossover clinical trial that monitored several adverse events of an antidepressive drug to illustrate the practical use of test procedures and interval estimators considered here.
在随机临床试验中，实验治疗与对照治疗之间的反应比例比（PR）是衡量相对治疗效果最常用的指标之一。我们开发了基于渐近和置换的程序来检验治疗效果的相等性，并在混合效应指数风险模型下推导多元二元匹配对数据的PR置信区间。为了评估和比较这些检验程序和区间估计量的性能，我们采用了蒙特卡罗模拟。当匹配对数量较大时，我们发现这里提出的所有检验程序在I型错误方面都能表现良好。当匹配对数量较小时，本文开发的基于置换的检验程序是有用的。此外，当已知对所有感兴趣的反应变量的治疗效果都朝着相同方向时，使用基于治疗效果加权线性平均估计量的检验程序（或区间估计量）可以提高检验效能（或提高精度）。最后，我们应用来自一项交叉临床试验的数据，该试验监测了一种抗抑郁药物的几种不良事件，以说明这里考虑的检验程序和区间估计量的实际应用。
