Statistical issues and limitations in personalized medicine research with clinical trials.
Statistical issues and limitations in personalized medicine research with clinical trials.
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DOI：
10.1515/1557-4679.1423
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发表时间：
2012-07-20
期刊：
影响因子：
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通讯作者：
van der Laan, Mark J
中科院分区：
文献类型：
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作者：
Rubin, Daniel B;van der Laan, Mark J
我们讨论使用临床试验数据来构建和评估使用基线协变量为不同患者分配不同治疗的规则。考虑到这样的候选个性化规则，我们首先注意到，通常可以在不将该规则实际应用于受试者的情况下评估其性能，并且从统计效率的角度来表征一类估计器。我们还指出最近注意到将规则构建问题简化为分类任务，并将结果朝这个方向扩展。这些事实共同表明了交叉验证的自然形式，其中可以使用标准分类工具根据临床试验数据构建个性化医疗规则，然后在重复试验中进行评估。由于 FDA 通常要求在药品上市之前进行复制，以提供安全性和有效性的证据，因此有大量数据可用于探索更具针对性的治疗的潜在益处。我们使用基于两项用于治疗皮肤和皮肤结构感染的抗菌药物的主动对照随机临床试验的模拟来构建和评估个性化医疗规则。不幸的是，我们提供的负面结果并没有表明个性化可以带来好处。我们讨论了这一发现的含义以及为什么个性化医疗问题的统计方法经常面临困难的挑战。
We discuss using clinical trial data to construct and evaluate rules that use baseline covariates to assign different treatments to different patients. Given such a candidate personalization rule, we first note that its performance can often be evaluated without actually applying the rule to subjects, and a class of estimators is characterized from a statistical efficiency standpoint. We also point out a recently noted reduction of the rule construction problem to a classification task and extend results in this direction. Together these facts suggest a natural form of cross-validation in which a personalized medicine rule can be constructed from clinical trial data using standard classification tools and then evaluated in a replicated trial. Because replication is often required by the FDA to provide evidence of safety and efficacy before pharmaceutical drugs can be marketed, there are abundant data with which to explore the potential benefits of more tailored therapy. We constructed and evaluated personalized medicine rules using simulations based on two active-controlled randomized clinical trials of antibacterial drugs for the treatment of skin and skin structure infections. Unfortunately we present negative results that did not suggest benefit from personalization. We discuss the implications of this finding and why statistical approaches to personalized medicine problems will often face difficult challenges.