Wang Shouao, Moodie Erica Em, Stephens David A, Nijjar Jagtar S
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC Canada, H3A 1A2.
Department of Mathematics and Statistics, McGill University, Montreal, QC Canada, H3A 0B9.
Biostatistics. 2020 Aug 27. doi: 10.1093/biostatistics/kxaa033.
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.
大多数用于自适应治疗策略的估计算法都假定，每个决策点处的治疗规则彼此独立，即它们不具有任何共同参数。这往往不切实际，因为相同的决策可能会随着时间反复做出。在决策点之间共享治疗决策参数有几个优点，包括估计更少的参数以及实施单一、时不变决策在临床上的便利性。我们提出了一种新的计算方法来估计共享参数G估计，该方法高效且具有“非共享”序贯G估计的双重稳健性。我们使用这种方法来分析来自苏格兰早期类风湿性关节炎（SERA）起始队列的数据。
