Subgroup identification in clinical trials via the predicted individual treatment effect.
Subgroup identification in clinical trials via the predicted individual treatment effect.
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DOI：
10.1371/journal.pone.0205971
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发表时间：
2018
期刊：
影响因子：
3.7
通讯作者：
Posch M
中科院分区：
文献类型：
--
作者：
Ballarini NM;Rosenkranz GK;Jaki T;König F;Posch M
通过临床试验的不同阶段确定治疗应答者的亚组有可能增加药物开发的成功率。亚组分析的最新发展考虑了根据预测的个体治疗效果定义的亚组，即每个个体的治疗下预测结果与控制下预测结果之间的差异，这反过来可能取决于多种生物标志物。在这项工作中，我们研究了不同的建模策略的属性，以估计预测的个体治疗效果。我们探索线性模型，并比较不同的估计方法，如最大似然法和Lasso有和没有随机响应。对于后者，我们实现了基于选择性推理框架的置信区间，以考虑模型选择阶段。我们说明了阿尔茨海默病（正常反应）的治疗数据集和前列腺癌（生存结果）的治疗数据集的方法。我们还通过模拟评估了使用预测的个体治疗效果来识别亚组的性能，与对照治疗相比，新治疗导致更好的结果。
Identifying subgroups of treatment responders through the different phases of clinical trials has the potential to increase success in drug development. Recent developments in subgroup analysis consider subgroups that are defined in terms of the predicted individual treatment effect, i.e. the difference between the predicted outcome under treatment and the predicted outcome under control for each individual, which in turn may depend on multiple biomarkers. In this work, we study the properties of different modelling strategies to estimate the predicted individual treatment effect. We explore linear models and compare different estimation methods, such as maximum likelihood and the Lasso with and without randomized response. For the latter, we implement confidence intervals based on the selective inference framework to account for the model selection stage. We illustrate the methods in a dataset of a treatment for Alzheimer disease (normal response) and in a dataset of a treatment for prostate cancer (survival outcome). We also evaluate via simulations the performance of using the predicted individual treatment effect to identify subgroups where a novel treatment leads to better outcomes compared to a control treatment.
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