Mao Fangya, Cook Richard J
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada.
Stat Med. 2023 Apr 15;42(8):1207-1232. doi: 10.1002/sim.9666. Epub 2023 Jan 23.
We consider the design and analysis of two-phase studies aiming to assess the relation between a fixed (eg, genetic) marker and an event time under current status observation. We consider a common setting in which a phase I sample is comprised of a large cohort of individuals with outcome (ie, current status) data and a vector of inexpensive covariates. Stored biospecimens for individuals in the phase I sample can be assayed to record the marker of interest for individuals selected in a phase II sub-sample. The design challenge is then to select the phase II sub-sample in order to maximize the precision of the marker effect on the time of interest under a proportional hazards model. This problem has not been examined before for current status data and the role of the assessment time is highlighted. Inference based on likelihood and inverse probability weighted estimating functions are considered, with designs centered on score-based residuals, extreme current status observations, or stratified sampling schemes. Data from a registry of patients with psoriatic arthritis is used in an illustration where we study the risk of diabetes as a comorbidity.
我们考虑两阶段研究的设计与分析，旨在评估在当前状态观察下固定（如基因）标志物与事件发生时间之间的关系。我们考虑一种常见情况，即第一阶段样本由大量具有结局（即当前状态）数据和一系列廉价协变量的个体组成。可以对第一阶段样本中个体的储存生物标本进行检测，以记录在第二阶段子样本中所选个体的感兴趣标志物。设计挑战在于选择第二阶段子样本，以便在比例风险模型下最大化标志物对感兴趣时间的效应精度。对于当前状态数据，此问题以前尚未研究过，并且评估时间的作用得到了突出强调。我们考虑基于似然和逆概率加权估计函数的推断，设计以基于得分的残差、极端当前状态观察或分层抽样方案为中心。在一个示例中使用了来自银屑病关节炎患者登记处的数据，我们在其中研究糖尿病作为一种合并症的风险。
