在评估结果变量和一组协变量之间关联的生物医学队列研究中,通常,一些协变量只能在一组研究对象上测量。一个重要的设计问题是-选择哪些受试者进入子组以提高统计效率。当结果是二进制时,可以采用案例控制采样设计或平衡案例控制设计,其中案例和控制在少量完整的离散协变量上进一步匹配。虽然后者在估计匹配协变量的优势比(OR)参数方面取得了成功,对于剩余的协变量,还没有探索类似的两阶段设计选项,尤其是不完全收集的。这在不能完全收集感兴趣的协变量的研究中非常重要。为此,假设外部模型可用于将结果和完整协变量联系起来,我们提出了一种新颖的抽样方案,该方案基于外部模型对拟合优度较差的案例和对照进行超抽样,并与平衡设计类似地在完整协变量上进一步匹配它们.我们开发了一种用于估计或参数的伪似然方法。通过真实队列研究中的模拟研究和探索,我们发现,我们的设计通常会导致OR估计的渐近方差减小,并且匹配协变量的减小与平衡设计的减小相当。
In biomedical cohort studies for assessing the association between an outcome variable and a set of covariates, usually, some covariates can only be measured on a subgroup of study subjects. An important design question is-which subjects to select into the subgroup to increase statistical efficiency. When the outcome is binary, one may adopt a case-control sampling design or a balanced case-control design where cases and controls are further matched on a small number of complete discrete covariates. While the latter achieves success in estimating odds ratio (OR) parameters for the matching covariates, similar two-phase design options have not been explored for the remaining covariates, especially the incompletely collected ones. This is of great importance in studies where the covariates of interest cannot be completely collected. To this end, assuming that an external model is available to relate the outcome and complete covariates, we propose a novel sampling scheme that oversamples cases and controls with worse goodness-of-fit based on the external model and further matches them on complete covariates similarly to the balanced design. We develop a
pseudolikelihood method for estimating OR parameters. Through simulation studies and explorations in a real-cohort study, we find that our design generally leads to reduced asymptotic variances of the OR estimates and the reduction for the matching covariates is comparable to that of the balanced design.