Multiple Test Procedures for Identifying the Minimum Effective and Maximum Safe Doses of a Drug
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识别药物最低有效剂量与最高安全剂量的多重检验
- 研究问题
- 如何根据相对零剂量的疗效与毒性阈值识别药物治疗窗口，同时控制把无效剂量判为有效或把不安全剂量判为安全的错误？
- 方法与思路
- 作者在二元正态模型下提出逐步下降多重检验，并以Bonferroni分开检验及bootstrap联合检验处理未知相关系数。
- 材料陈述的贡献
- 摘要称正态理论程序控制联合族错误率；模拟中bootstrap联合检验获得功效提升，并以关节炎药物试验数据说明方法。
- 阅读关注点
- 应核对疗效与安全阈值、逐步下降规则及精确临界值的构造，尤其关注bootstrap放宽正态性后的误差控制依据。
- 阅读边界
- 仅据未注明稿件版本的OpenAlex索引摘要；bootstrap是近似方法，模拟功效增益不能推广为所有数据下的保证。
原文摘要
We address the problem of determining the therapeutic window of a drug by finding its minimum effective and maximum safe doses (MINED and MAXSD). The MINED is the lowest dose that exceeds the mean efficacy of the zero dose by a specified threshold, and the MAXSD is the highest dose that does not exceed the mean toxicity of the zero dose by a specified threshold. Step-down multiple test procedures are proposed to identify the MINED and MAXSD assuming a bivariate normal model. These procedures control the type I familywise error probability of declaring any ineffective dose as effective or any unsafe dose as safe at a prespecified level α. A new multivariate t-distribution is introduced whose critical points are required to implement the exact normal theory procedures. Because these critical points depend on the unknown correlation coefficient between the efficacy and safety variables, the Bonferroni method is proposed as an alternative, which amounts to separately testing for efficacy and safety, each at type I familywise error rate of α/2. The bootstrap versions of the exact normal theory procedures provide an approximate way to jointly test for efficacy and safety without the knowledge of the correlation coefficient, as well as to relax the bivariate normality assumption. The Bonferroni and bootstrap procedures are compared in a simulation study. It is shown that significant power gains are achieved by jointly testing for both efficacy and safety using bootstrap procedures. Coded data from an arthritis drug trial are analyzed to illustrate the procedures.