Lavori Philip W, Dawson Ree
Department of Health Research and Policy, Stanford University School of Medicine, Mail Code 5405, Stanford, CA 94305, USA.
Clin Trials. 2007;4(4):297-308. doi: 10.1177/1740774507081327.
Given the history of treatments to date, and the responses of the patient, what is the best treatment to try next? An ensemble of sequential, multistage rules guiding such adaptive decision making can be described as an ;adaptive treatment strategy (ATS)'. Robins' G-computation can be used for estimation of the mean outcome of an ATS from a ;sequential multiple assignment randomized (SMAR)' trial.
To develop a variance estimate for the G-computation formula, based on a sequential analysis of the states and treatments observed in the trial, and compare its properties with those of the ;marginal mean' method described by Murphy, which is based on an estimating equation.
We use both mathematical calculation and simulation studies to demonstrate the properties of the G-computation and its sequential variance estimate, including finite-sample bias and coverage.
The sequential method is unbiased and more efficient when the variation in intervening states contributes substantially to the variation in final outcome, and when the study can be designed to guarantee full observation of the ATS under study. The method extends to the comparison of two or more ATS.
If full observation cannot be guaranteed, the method may have poor finite-sample properties.
When the states used to adapt treatment contribute substantially to the outcome, and good design technique can be applied, the sequential method provides more efficient estimation.
鉴于迄今为止的治疗史以及患者的反应，接下来尝试的最佳治疗方法是什么？一系列指导这种适应性决策的顺序多阶段规则可被描述为“适应性治疗策略（ATS）”。罗宾斯的G计算可用于从“序贯多重分配随机（SMAR）”试验中估计ATS的平均结果。
基于对试验中观察到的状态和治疗的序贯分析，为G计算公式开发方差估计，并将其性质与墨菲描述的基于估计方程的“边际均值”方法的性质进行比较。
我们使用数学计算和模拟研究来证明G计算及其序贯方差估计的性质，包括有限样本偏差和覆盖率。
当干预状态的变化对最终结果的变化有很大贡献，并且研究可以设计为保证对所研究的ATS进行全面观察时，序贯方法是无偏且更有效的。该方法可扩展到比较两种或更多种ATS。
如果不能保证全面观察，该方法可能具有较差的有限样本性质。
当用于调整治疗的状态对结果有很大贡献，并且可以应用良好的设计技术时，序贯方法可提供更有效的估计。
