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.

中文翻译：

---

在单调缺失下估计最佳治疗方案时权重的确定性质。

动态治疗方案将精确医学作为一系列决策规则（在临床干预的每个阶段使用），将最新的患者信息映射到推荐的干预措施。当应用于目标人群时，最佳治疗方案可使平均效用最大化。估计最佳治疗方案的方法假定要完全观察到数据，而在实践中很少发生。一种常见的方法是先使用多重插补，然后在估算的数据集中汇总估计量。但是，这种方法需要估计患者轨迹的联合分布，这可能是高维的，尤其是在干预的多个阶段时。在单调缺失的情况下，我们研究了逆概率加权估计方程作为多重插补的一种替代方法。该方法适用于最佳治疗方案的广泛估计量，包括Q学习和结果加权学习的一般化。我们在温和的规律性条件下建立一致性，并使用一系列模拟实验及其在精神分裂症研究中的应用证明了其在有限样本中的优势。