论文标题
两阶段病例对照研究的最佳设计用于一般预测效应
Optimal Designs of Two-Phase Case-Control Studies for General Predictor Effects
论文作者
论文摘要
在两阶段的设计下，在第一阶段测量了结果和几个协变量和混杂因素，并且可以在第二阶段的子样本上测量一种新的感兴趣的预测指标，而无需招募受试者的成本。通过使用第一阶段收集的信息，可以选择第二相子样本来提高测试的效率并估算新预测因子对结果的影响。过去的研究集中在最佳的两相抽样方案上，以对局部（$β= o（1）$）效应的统计推断。在这项研究中，我们提出了两相设计的扩展，该设计采用了最佳抽样方案来估计预测效应，并在病例对照研究中使用伪有条件的可能性估计量进行估算。这种方法适用于局部和非本地效应。我们通过模拟研究和对170名患者的数据进行分析来证明提出的抽样方案的有效性，以治疗COVID-19。结果表明，感兴趣参数的估计有显着改善。
Under two-phase designs, the outcome and several covariates and confounders are measured in the first phase, and a new predictor of interest, which may be costly to collect, can be measured on a subsample in the second phase, without incurring the costs of recruiting subjects. By using the information gathered in the first phase, the second-phase subsample can be selected to enhance the efficiency of testing and estimating the effect of the new predictor on the outcome. Past studies have focused on optimal two-phase sampling schemes for statistical inference on local ($β= o(1)$) effects of the predictor of interest. In this study, we propose an extension of the two-phase designs that employs an optimal sampling scheme for estimating predictor effects with pseudo conditional likelihood estimators in case-control studies. This approach is applicable to both local and non-local effects. We demonstrate the effectiveness of the proposed sampling scheme through simulation studies and analysis of data from 170 patients hospitalized for treatment of COVID-19. The results show a significant improvement in the estimation of the parameter of interest.
