Estimation and Optimization of Composite Outcomes.
Estimation and Optimization of Composite Outcomes.
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发表时间：
2021-01
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通讯作者：
Kosorok MR
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作者：
Luckett DJ;Laber EB;Kim S;Kosorok MR
精准医学作为一种通过根据个人特征量身定做治疗来改善患者预后的手段，引起了人们的极大兴趣。个性化治疗规则将精准医学正式化为从患者信息到推荐治疗的地图。如果治疗规则最大化了感兴趣人群中标量结果的平均值，例如症状减轻，则该治疗规则被定义为最优。然而，临床和干预科学家经常寻求平衡多种和可能相互竞争的结果，例如症状减轻和不良事件的风险。在这种情况下，精确医学的一种方法是得出一个平衡所有相互竞争的结果的复合结果；不幸的是，如果没有高质量的工具，直接从患者那里得出复合结果是困难的，而且专家得出的复合结果可能无法解释患者偏好的异质性。我们提出了一种使用观察数据研究精确医学的新范式，该范式完全依赖于这样的假设，即临床医生正在近似(即，不完美地)做出决策，以最大化个体患者的效用。估计的综合结果随后被用来构建个体化治疗规则的估计器，其最大化患者特定的综合结果的平均值。估计的综合结果和估计的最佳个体化治疗规则为了解患者偏好的异质性、临床医生的行为以及精确医学在给定领域的价值提供了新的见解。我们在温和的条件下推导了所提出的估计器的推理过程，并通过一组模拟实验和对双相抑郁研究的数据的说明性应用来展示它们的有限样本性能。
There is tremendous interest in precision medicine as a means to improve patient outcomes by tailoring treatment to individual characteristics. An individualized treatment rule formalizes precision medicine as a map from patient information to a recommended treatment. A treatment rule is defined to be optimal if it maximizes the mean of a scalar outcome in a population of interest, e.g., symptom reduction. However, clinical and intervention scientists often seek to balance multiple and possibly competing outcomes, e.g., symptom reduction and the risk of an adverse event. One approach to precision medicine in this setting is to elicit a composite outcome which balances all competing outcomes; unfortunately, eliciting a composite outcome directly from patients is difficult without a high-quality instrument, and an expert-derived composite outcome may not account for heterogeneity in patient preferences. We propose a new paradigm for the study of precision medicine using observational data that relies solely on the assumption that clinicians are approximately (i.e., imperfectly) making decisions to maximize individual patient utility. Estimated composite outcomes are subsequently used to construct an estimator of an individualized treatment rule which maximizes the mean of patient-specific composite outcomes. The estimated composite outcomes and estimated optimal individualized treatment rule provide new insights into patient preference heterogeneity, clinician behavior, and the value of precision medicine in a given domain. We derive inference procedures for the proposed estimators under mild conditions and demonstrate their finite sample performance through a suite of simulation experiments and an illustrative application to data from a study of bipolar depression.