Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA.
Department of Statistics, Technion Israel Institute of Technology, Haifa, Israel.
Stat Med. 2020 Nov 10;39(25):3503-3520. doi: 10.1002/sim.8678. Epub 2020 Jul 30.
Dynamic treatment regimes operationalize precision medicine as a sequence of decision rules, one per stage of clinical intervention, that map up-to-date patient information to a recommended intervention. An optimal treatment regime maximizes the mean utility when applied to the population of interest. Methods for estimating an optimal treatment regime assume the data to be fully observed, which rarely occurs in practice. A common approach is to first use multiple imputation and then pool the estimators across imputed datasets. However, this approach requires estimating the joint distribution of patient trajectories, which can be high-dimensional, especially when there are multiple stages of intervention. We examine the application of inverse probability weighted estimating equations as an alternative to multiple imputation in the context of monotonic missingness. This approach applies to a broad class of estimators of an optimal treatment regime including both Q-learning and a generalization of outcome weighted learning. We establish consistency under mild regularity conditions and demonstrate its advantages in finite samples using a series of simulation experiments and an application to a schizophrenia study.
动态治疗方案将精准医学实施为一系列决策规则，每个规则对应临床干预的一个阶段，将最新的患者信息映射到推荐的干预措施上。最优治疗方案在应用于目标人群时，使平均效用最大化。用于估计最优治疗方案的方法假设数据是完全观察到的，但在实践中很少发生。一种常见的方法是首先使用多重插补，然后在插补数据集之间汇总估计值。然而，这种方法需要估计患者轨迹的联合分布，这在维度上可能很高，尤其是在存在多个干预阶段的情况下。我们研究了在单调缺失的情况下，将逆概率加权估计方程作为多重插补的替代方法的应用。这种方法适用于最优治疗方案的广泛类估计量，包括 Q 学习和结果加权学习的一种推广。我们在温和的正则条件下建立了一致性，并通过一系列模拟实验和对精神分裂症研究的应用展示了其在有限样本中的优势。
