Most estimation algorithms for adaptive treatment strategies assume that treatment rules at each decision point are independent from one another in the sense that they do not possess any common parameters. This is often unrealistic, as the same decisions may be made repeatedly over time. Sharing treatment-decision parameters across decision points offers several advantages, including estimation of fewer parameters and the clinical ease of a single, time-invariant decision to implement. We propose a new computational approach to estimation of shared-parameter G-estimation, which is efficient and shares the double robustness of the “unshared” sequential G-estimation. We use this approach to analyze data from the Scottish Early Rheumatoid Arthritis (SERA) Inception Cohort.

中文翻译：

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慢性病的适应性治疗策略：共享参数 G 估计及其在类风湿关节炎中的应用。


大多数自适应治疗策略的估计算法都假设每个决策点的治疗规则彼此独立，因为它们不具有任何公共参数。这通常是不现实的，因为随着时间的推移，可能会重复做出相同的决定。跨决策点共享治疗决策参数具有多种优势，包括估计更少的参数以及在临床上易于实施单个、时间不变的决策。我们提出了一种新的计算方法来估计共享参数 G 估计，该方法非常高效，并且具有“非共享”顺序 G 估计的双重鲁棒性。我们使用这种方法来分析苏格兰早期类风湿关节炎 (SERA) 初始队列的数据。