Gamalo-Siebers Margaret, Tiwari Ram, LaVange Lisa
a Eli Lilly & Co. , Indianapolis , Indiana , USA.
b Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration , Silver Spring , Maryland , USA.
J Biopharm Stat. 2016;26(6):1040-1055. doi: 10.1080/10543406.2016.1226327. Epub 2016 Aug 22.
The paradigm shift towards precision medicine reignited interest in determining whether there are differential treatment effects in subgroups of trial participants. Intrinsic to this problem is that any assessment of a differential treatment effect is predicated on being able to estimate the treatment response accurately while satisfying constraints of balancing the risk of overlooking an important subgroup with the potential to make a decision based on a false discovery. While shrinkage models have been widely used to improve accuracy of subgroup parameter estimates by leveraging the relationship between them, there is still a possibility that it can lead to excessively conservative or anti-conservative results. This can possibly be due to the use of the normal distribution as prior, which forces outlying subjects to have their means over-shrunk towards the population mean, and the data from such subjects may be excessively influential in estimation of both the overall mean response and the mean response for each subgroup, or a model misspecification due to unaccounted variation or clustering. To address this issue, we investigate the use of nonparametric Bayes, particularly Dirichlet process priors, to create a flexible shrinkage model. This model represents uncertainty in the prior distribution for the overall response while accommodating heterogeneity among individual subgroups. We simulated data to compare estimates when there is no differential subgroup effect and when there is a differential subgroup effect. In either of these scenarios, the flexible shrinkage model does not force estimates to shrink excessively when similarity of treatment effects is not supported but still retains the attractiveness of improved precision given by the narrower credible intervals. We also applied the same method to a dataset based on trials conducted for an antimicrobial therapy on several related indications.
向精准医学的范式转变重新激发了人们对于确定试验参与者亚组中是否存在差异治疗效果的兴趣。这个问题的本质在于，任何对差异治疗效果的评估都基于能够在满足平衡忽视一个重要亚组的风险与基于错误发现做出决策的可能性的约束条件下，准确估计治疗反应。虽然收缩模型已被广泛用于通过利用亚组参数之间的关系来提高亚组参数估计的准确性，但仍有可能导致过于保守或反保守的结果。这可能是由于使用正态分布作为先验分布，这会迫使离群个体的均值过度向总体均值收缩，并且来自这些个体的数据在估计总体平均反应和每个亚组的平均反应时可能具有过大的影响力，或者是由于未考虑到的变异或聚类导致模型设定错误。为了解决这个问题，我们研究了使用非参数贝叶斯方法，特别是狄利克雷过程先验，来创建一个灵活的收缩模型。该模型在表示总体反应的先验分布中的不确定性时，同时考虑了各个亚组之间的异质性。我们模拟了数据，以比较在不存在亚组差异效应和存在亚组差异效应时的估计值。在这两种情况下，当治疗效果的相似性不被支持时，灵活的收缩模型不会迫使估计值过度收缩，但仍保留了由更窄的可信区间给出的提高精度的吸引力。我们还将相同的方法应用于一个基于针对几种相关适应症进行抗菌治疗试验的数据集。
