Use of multiple imputation in supersampled nested case-control and case-cohort studies
Use of multiple imputation in supersampled nested case-control and case-cohort studies
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在超采样嵌套病例对照和病例队列研究中使用多重插补
DOI：
10.1111/sjos.12624
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发表时间：
2022
影响因子：
1
通讯作者：
Borgan Ø
中科院分区：
文献类型：
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作者：
Borgan Ø
巢式病例对照和病例队列研究对于研究协变量与事件发生时间之间的关联非常有用，因为某些协变量的测量成本很高。仅在嵌套病例对照或病例队列样本中收集完整的协变量信息，而通常在整个队列中观察到廉价测量的协变量。此类病例对照样本的标准分析会忽略任何完整的队列数据。以前的工作已经表明，如何通过对昂贵的协变量进行多重插补，然后进行全队列分析，有效地使用全队列的数据。对于大型队列，这在计算上是昂贵的，甚至是不可行的。另一种方法是用额外的对照来补充病例对照样本，在这些对照样本上观察到廉价测量的协变量。我们展示了如何多重插补可以用于分析这样的超采样数据。模拟结果表明，相对于传统分析，这带来了效率增益，并且相对于使用完整队列数据的效率损失并不显著。
Nested case‐control and case‐cohort studies are useful for studying associations between covariates and time‐to‐event when some covariates are expensive to measure. Full covariate information is collected in the nested case‐control or case‐cohort sample only, while cheaply measured covariates are often observed for the full cohort. Standard analysis of such case‐control samples ignores any full cohort data. Previous work has shown how data for the full cohort can be used efficiently by multiple imputation of the expensive covariate(s), followed by a full‐cohort analysis. For large cohorts this is computationally expensive or even infeasible. An alternative is to supplement the case‐control samples with additional controls on which cheaply measured covariates are observed. We show how multiple imputation can be used for analysis of such supersampled data. Simulations show that this brings efficiency gains relative to a traditional analysis and that the efficiency loss relative to using the full cohort data is not substantial.