The Multiple Comparison Procedures with Modeling Techniques (MCP-Mod) framework has been recently approved by the U.S. Food and Administration and European Medicines Agency as fit-per-purpose for phase II studies. Nonetheless, this approach relies on the asymptotic properties of Maximum Likelihood (ML) estimators, which might not be reasonable for small sample sizes. In this paper, we derived improved ML estimators and correction for their covariance matrices in the censored Weibull regression model based on the corrective and preventive approaches. We performed two simulation studies to evaluate ML and improved ML estimators with their covariance matrices in (i) a regression framework (ii) the Multiple Comparison Procedures with Modeling Techniques framework. We have shown that improved ML estimators are less biased than ML estimators yielding Wald-type statistics that controls type I error without loss of power in both frameworks. Therefore, we recommend the use of improved ML estimators in the MCP-Mod approach to control type I error at nominal value for sample sizes ranging from 5 to 25 subjects per dose.

翻译：多重比较与建模技术（MCP-Mod）框架近期已被美国食品药品监督管理局和欧洲药品管理局批准适用于II期研究。然而，该方法依赖于极大似然（ML）估计量的渐近性质，在小样本情况下可能不够合理。本文基于修正性和预防性方法，推导了删失威布尔回归模型中改进的极大似然估计量及其协方差矩阵的校正。我们通过两项模拟研究评估了ML和改进ML估计量及其协方差矩阵：（i）回归框架；（ii）多重比较与建模技术框架。研究表明，改进的ML估计量比ML估计量偏差更小，由此生成的Wald型统计量在两个框架中均能在不损失检验功效的前提下控制第一类错误。因此，我们建议在MCP-Mod方法中使用改进的ML估计量，以使每剂量组样本量为5至25例受试者时，第一类错误控制在名义水平。