Testing Equality of Treatments under an Incomplete Block Crossover Design with Ordinal Responses

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2017-02-03 DOI:10.1515/ijb-2016-0069

K. Lui

引用次数: 2

Abstract

Abstract The generalized odds ratio (GOR) for paired sample is considered to measure the relative treatment effect on patient responses in ordinal data. Under a three-treatment two-period incomplete block crossover design, both asymptotic and exact procedures are developed for testing equality between treatments with ordinal responses. Monte Carlo simulation is employed to evaluate and compare the finite-sample performance of these test procedures. A discussion on advantages and disadvantages of the proposed test procedures based on the GOR versus those based on Wald’s tests under the normal random effects proportional odds model is provided. The data taken as a part of a crossover trial studying the effects of low and high doses of an analgesic versus a placebo for the relief of pain in primary dysmenorrhea over the first two periods are applied to illustrate the use of these test procedures.

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具有顺序响应的不完全块体交叉设计下处理平等性的检验

摘要配对样本的广义比值比（GOR）被认为是衡量有序数据中患者反应的相对治疗效果。在三个处理两个周期的不完全块交叉设计下，开发了渐近和精确程序来测试具有顺序响应的处理之间的相等性。蒙特卡罗模拟用于评估和比较这些测试程序的有限样本性能。讨论了在正态随机效应比例优势模型下，基于GOR的测试程序与基于Wald测试的测试程序的优缺点。作为交叉试验的一部分，研究前两个时期低剂量和高剂量镇痛药与安慰剂对原发性痛经疼痛的缓解作用，应用这些数据来说明这些测试程序的使用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Biostatistics Mathematics-Statistics and Probability

CiteScore

2.30

自引率

8.30%

发文量

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.