SAMPLE-SIZE SOFTWARE FOR ORDERED CATEGORICAL DATA

有序分类变量在癌症临床试验中经常出现 和其他生物医学研究。分析的统计程序 这样的数据是众所周知的，用于执行分析的软件是 现成的。其基本思想是以 由分类数据创建的列联表，从而获得 免费分发测试，可自动纠正平局。尽管 这一条件方法在有序范畴分析中的普及性 数据在功率和样本大小方面所做的工作很少 设计阶段的注意事项。一位生物医学调查员即将 启动一项临床试验，将两种疗法与Order进行比较 明确的结果将发现很难确定什么是 样本量是必需的。要么调查员必须假设数据 是连续的，否则数据是二进制的，因为这是唯一的 有可靠方法和软件可用的案例。两者都有 这种方法不适合有序的分类数据。我们建议 通过提供新的精确和蒙特卡罗方法来填补空白，这些方法提供了 有序条件检验的精确幂估计和样本量估计 绝对数据。

Ordered categorical variables arise frequently in cancer clinical trials and other biomedical studies. The statistical procedures for analyzing such data are well known and software for performing the analysis is readily available. The basic idea is to condition on the margins of the contingency table created by the categorical data and thereby obtain a distribution free test that automatically corrects for ties. Despite the popularity of this conditional approach for analyzing ordered categorical data there has been very little work done on power and sample-size considerations at the design phase. A biomedical investigator about to launch a clinical trial for comparing two treatments with ordered categorical outcomes will find it extremely difficult to determine what sample size is needed. Either the investigator must assume that the data are continuous, or else that the data are binary, since these are the only cases for which reliable methods and software are available. Both approaches are inappropriate for ordered categorical data. We propose to fill the void by providing new exact and Monte Carlo methods that provide accurate power and sample-size estimates for conditional tests on ordered categorical data.