论文标题
评论：预期零计数时使用MCPMOD分析二进制数据
Commentary: analyzing binary data using MCPMod when zero counts are expected
论文作者
论文摘要
Bretz等人（2005）提出了多重比较程序和建模方法（MCPMOD）方法来设计和分析剂量调查研究。然后，Pinheiro（2014）将其概括为各种类型的端点，包括但不限于二进制端点，生存端点，计数数据和纵向数据。 Pinheiro（2013）建议使用从观察到的数据中使用估计的协方差矩阵来重新计算许多II期研究的测试的最佳对比度和测试的临界值，通常，共同安慰剂响应率较低的每ARM的样本量很小。在这种情况下，不能排除观察到零的数量。例如，当安慰剂应答率为10％时，在安慰剂组中观察到零反应者或其他剂量组的机会大约有4％的机会，其响应率与安慰剂相似。在此手稿中，我们想使用案例研究和模拟来说明Pinheiro（2013）的潜在问题。评估了使用Firth逻辑回归的另一种方法，以获得每个剂量组响应的稳定估计。此外，我们评估了有问题的对比度系数解决该问题的两个选项。
Bretz et al (2005) proposed multiple Comparison Procedure and Modeling (MCPMod) method to design and analyze dose-finding study. Pinheiro (2014) then generalized it to various types of endpoint, including but not limited to binary endpoint, survival endpoint, count data, and longitudinal data. Pinheiro (2013) recommended to use the estimated covariance matrix from the observed data to recalculate the optimal contrast and the critical value of the test For many phase II studies it is common to have small sample sizes per arm with low placebo response rates jointly. Under such circumstances, it cannot be excluded to have a zero count observed. For example, when the placebo response rate is 10%, there is about 4% chance to observe zero responders in the placebo group, or other dose group(s), which has a similar response rate as placebo. In this manuscript, we would like to illustrate the potential problem of Pinheiro (2013) using a case study and simulations. An alternative method using Firth's logistic regression was evaluated to get a stable estimate of response for each dose group. In addition, we evaluated two options to address the issue with problematic contrast coefficients.
