临床试验中亚组处理效应估计方法的比较
Comparing Approaches to Treatment Effect Estimation for Subgroups in Clinical Trials
摘要：
确定在疗效和安全性方面对治疗反应不同的亚组是药物开发的重要组成部分。探索性亚组分析中的一个众所周知的挑战是所考虑的亚组中的样本量较小，通常太低而无法进行明确的比较。在早期试验中，这个问题被进一步夸大，因为关于药物和合理亚组的临床既往信息有限或没有。我们评估了新的策略，处理效应估计在这些设置在模拟研究的动机是真实的临床试验情况。我们比较了几种方法来估计选定的亚组的处理效应，采用模型平均，rescue和Lasso回归方法。采用两种子群识别方法，一种基于协变量的分类，另一种基于样条。我们的研究结果表明，天真的估计处理效应，忽略了选择已经发生，导致偏见和过于乐观的结论。对于所考虑的模拟场景，几乎所有评价的新方法都在偏倚、MSE和置信区间覆盖范围方面为选定亚组提供了更充分的处理效应估计。
英文摘要：
Identifying subgroups, which respond differently to a treatment, both in terms of efficacy and safety, is an important part of drug development. A well-known challenge in exploratory subgroup analyses is the small sample size in the considered subgroups, which is usually too low to allow for definite comparisons. In early phase trials this problem is further exaggerated, because limited or no clinical prior information on the drug and plausible subgroups is available. We evaluate novel strategies for treatment effect estimation in these settings in a simulation study motivated by real clinical trial situations. We compare several approaches to estimate treatment effects for selected subgroups, employing model averaging, resampling and Lasso regression methods. Two subgroup identification approaches are employed, one based on categorization of covariates and the other based on splines. Our results show that naive estimation of the treatment effect, which ignores that a selection has taken place, leads to bias and overoptimistic conclusions. For the considered simulation scenarios virtually all evaluated novel methods provide more adequate estimates of the treatment effect for selected subgroups, in terms of bias, MSE and confidence interval coverage.
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