Mayer-Hamblett Nicole, Kronmal Richard A
Department of Pediatrics, University of Washington, Box 5371, CL-11, Seattle, WA 98195, USA.
Contemp Clin Trials. 2005 Feb;26(1):2-16. doi: 10.1016/j.cct.2004.08.008. Epub 2005 Jan 27.
In many clinical trials, the primary focus is whether treatment groups differ with respect to the change from baseline to end of therapy in a continuous response variable. Randomized clinical trials often use a repeated measures design in which subjects are followed-up at fixed times throughout the study. With this design, testing for differences between treatment groups with respect to the average change from baseline to end of therapy in the response variable is equivalent to testing for differences between the rates of change in the response variable, assuming the rates of change in each treatment group are linear. This analysis can be performed quite easily using methods such as generalized estimating equations (GEE). However, if the rate of change in the response cannot be assumed linear, the average change from baseline is many times calculated using simply differences between baseline and final measurements and additional data points are not included in the analysis. Instead, we propose using all available data in a repeated measures model that is based on the nonlinear treatment response pattern to estimate the average change from baseline to end of therapy in each treatment group. GEE with robust variance estimation is used for obtaining these model-based estimates of the treatment effect and a simple test for appropriateness of the model is presented. The GEE model presented, in conjunction with the test for appropriateness of the model, form the basis for an adaptive analysis approach for determining the method of estimation of the primary endpoint. This approach results in more efficient estimates of the treatment effect when the response pattern is specified correctly and minimizes the bias in the estimate when the hypothesized response pattern is misspecified. We are motivated by examples in the cystic fibrosis (CF) clinical trial setting and demonstrate the potential for this approach in reducing the sample size required for future CF clinical trials.
在许多临床试验中，主要关注点是治疗组在连续反应变量从基线到治疗结束的变化方面是否存在差异。随机临床试验通常采用重复测量设计，即在整个研究过程中按固定时间对受试者进行随访。采用这种设计时，在假设每个治疗组的变化率呈线性的情况下，检验治疗组在反应变量从基线到治疗结束的平均变化方面的差异，等同于检验反应变量的变化率之间的差异。使用广义估计方程（GEE）等方法可以很容易地进行这种分析。然而，如果不能假设反应的变化率是线性的，那么从基线的平均变化很多时候只是简单地用基线测量值与最终测量值之间的差异来计算，并且分析中不包括额外的数据点。相反，我们建议在基于非线性治疗反应模式的重复测量模型中使用所有可用数据，以估计每个治疗组从基线到治疗结束的平均变化。使用具有稳健方差估计的GEE来获得基于模型的治疗效果估计值，并给出了一个简单的模型适用性检验。所提出的GEE模型与模型适用性检验相结合，构成了一种用于确定主要终点估计方法的自适应分析方法的基础。当正确指定反应模式时，这种方法能更有效地估计治疗效果，而当假设的反应模式指定错误时，能使估计中的偏差最小化。我们受到囊性纤维化（CF）临床试验案例的启发，并展示了这种方法在减少未来CF临床试验所需样本量方面的潜力。
