Schulte Phillip J, Tsiatis Anastasios A, Laber Eric B, Davidian Marie
Biostatistician, Duke Clinical Research Institute, Durham, North Carolina 27701, USA (
Gertrude M. Cox Distinguished Professor, Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695-8203, USA (
Stat Sci. 2014 Nov;29(4):640-661. doi: 10.1214/13-STS450.
In clinical practice, physicians make a series of treatment decisions over the course of a patient's disease based on his/her baseline and evolving characteristics. A dynamic treatment regime is a set of sequential decision rules that operationalizes this process. Each rule corresponds to a decision point and dictates the next treatment action based on the accrued information. Using existing data, a key goal is estimating the optimal regime, that, if followed by the patient population, would yield the most favorable outcome on average. - and -learning are two main approaches for this purpose. We provide a detailed account of these methods, study their performance, and illustrate them using data from a depression study.
在临床实践中，医生会根据患者的基线特征和病情变化，在患者疾病的整个过程中做出一系列治疗决策。动态治疗方案是一组将这一过程具体化的序贯决策规则。每个规则对应一个决策点，并根据累积的信息决定下一步的治疗行动。利用现有数据，一个关键目标是估计最优方案，即如果患者群体遵循该方案，平均而言将产生最有利的结果。为此，有两种主要方法。我们详细介绍了这些方法，研究了它们的性能，并使用一项抑郁症研究的数据对其进行了说明。
