Biostatistics Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA.
Epidemiology. 2011 Sep;22(5):704-12. doi: 10.1097/EDE.0b013e318227af1a.
Exposure assessment using biologic specimens is important for epidemiology but may become impracticable if assays are expensive, specimen volumes are marginally adequate, or analyte levels fall below the limit of detection. Pooled exposure assessment can provide an effective remedy for these problems in unmatched case-control studies. We extend pooled exposure strategies to handle specimens collected in a matched case-control study. We show that if a logistic model applies to individuals, then a logistic model also applies to an analysis using pooled exposures. Consequently, the individual-level odds ratio can be estimated while conserving both cost and specimen. We discuss appropriate pooling strategies for a single exposure, with adjustment for multiple, possibly continuous, covariates (confounders) and assessment of effect modification by a categorical variable. We assess the performance of the approach via simulations and conclude that pooled strategies can markedly improve efficiency for matched as well as unmatched case-control studies.
使用生物标本进行暴露评估对于流行病学很重要,但如果检测昂贵、标本量勉强足够或分析物水平低于检测限,则可能变得不切实际。在不匹配的病例对照研究中,集中暴露评估可以为这些问题提供有效的补救措施。我们将集中暴露策略扩展到处理在匹配病例对照研究中收集的标本。我们表明,如果逻辑模型适用于个体,那么使用集中暴露进行分析也适用。因此,可以在节省成本和标本的同时估计个体水平的比值比。我们讨论了单一暴露的适当集中策略,同时调整了多个可能连续的协变量(混杂因素),并评估了分类变量的效应修饰。我们通过模拟评估了该方法的性能,并得出结论,集中策略可以显著提高匹配和不匹配病例对照研究的效率。