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用于估计最优动态治疗策略的随机树搜索AI 解读

Yilun Sunab & Lu Wanga*a Department of Biostatistics, University of Michigan, Ann Arbor, MI; b Department of Radiation Oncology, University of Michigan, Ann Arbor, MISupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Lu Wang luwang@umich.edu Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109.AbstractA dynamic treatment regime (DTR) is a sequence of decision rules that adapt to the time-varying…

晚期前列腺癌序贯随机试验中可实施动态治疗策略的评估AI 解读

We present new statistical analyses of data arising from a clinical trial designed to compare two-stage dynamic treatment regimes (DTRs) for advanced prostate cancer. The trial protocol mandated that patients were to be initially randomized among four chemotherapies, and that those who responded poorly were to be rerandomized to one of the remaining candidate therapies. The primary aim was to compare the DTRs' overall success rates, with success defined by the occurrence of…

利用加权核估计方程处理结果缺失的非参数回归AI 解读

We consider nonparametric regression of a scalar outcome on a covariate when the outcome is missing at random (MAR) given the covariate and other observed auxiliary variables. We propose a class of augmented inverse probability weighted (AIPW) kernel estimating equations for nonparametric regression under MAR. We show that AIPW kernel estimators are consistent when the probability that the outcome is observed, that is, the selection probability, is either known by design or…