Semiparametric Methods for Secondary Outcomes in Case-control Studies

描述（由申请人提供）：现代病例对照研究通常涉及收集大量变量的数据，通常需要大量的后勤和资金支出。这些数据对随后的研究人员具有潜在的巨大价值，这些研究人员虽然不关心作为原始研究对象的疾病，但可能希望利用现有信息分析暴露对确定原始病例系列的疾病以外的次要结果的影响。以这种方式重复使用数据的一个困难是，如果使用传统的分析方法，原始研究中使用的病例对照抽样方案可能会在二次研究中对对数比值比和其他参数的估计中引起偏差。在本提案中，我们计划开发新的统计方法，以便在病例对照抽样下对不同结果的暴露对次要结果的影响进行稳健和高效的推断。一个重要的进步是，所提出的方法适用于调查人员是否知道抽样概率，并且在大多数观察性研究中，需要对中等到大量混杂因素进行调整时特别有用。我们将使用提出的新方法来准确评估铅暴露对骨质疏松症风险和认知功能下降的影响，使用来自护士健康研究铅和高血压病例对照研究的数据。

DESCRIPTION (provided by applicant): Modern case-control studies typically involve the collection of data on a large number of variables, often at considerable logistical and monetary expense. These data are of potentially great value to subsequent researchers, who although not concerned with the disease that was the subject of the original study, may want to use the available information towards an analysis of the effects of an exposure on a secondary outcome other than the disease that defined the original case series. A difficulty with re-using data in this way is that the case-control sampling scheme used in the original study will likely induce bias in estimates of log odds ratios and other parameters in the secondary study, if conventional analytical approaches are used. In this proposal, we plan to develop novel statistical methodology for making robust and highly efficient inferences on the effects of an exposure on a secondary outcome under case-control sampling on a different outcome. An important advance is that the proposed methodology applies whether or not sampling probabilities are known to the investigator, and is particularly useful when, as in most observational studies, one needs to adjust for a moderate to large number of confounders. We will use the proposed new methodology to accurately evaluate the effects of lead exposure on the risk of osteoporosis and on decline in cognitive function, using data from the Nurses' Health Study case-control study of lead and hypertension. PUBLIC HEALTH RELEVANCE: Modern case-control studies typically involve the collection of data on a large number of variables, often at considerable logistical and monetary expense. These data are of potentially great value to subsequent researchers, who although not concerned with the disease that was the subject of the original study, may want to use the available information towards an analysis of the effects of an exposure on a secondary outcome other than the disease that defined the original case series. A difficulty with re-using data in this way is that the case-control sampling scheme used in the original study will likely induce bias in estimates of regression parameters in the secondary study, if conventional analytical approaches are used. In this proposal, we plan to develop novel statistical methodology for making robust and highly efficient inferences on the effects of an exposure on a secondary outcome of a categorical or continuous nature, under case-control sampling on a different outcome. An important advance is that the proposed methodology applies whether or not sampling probabilities are known to the investigator. Furthermore, the methods are particularly useful when, as in most observational studies, one needs to adjust for a moderate to large number of confounders, and data on some key covariates may be missing for a subset of subjects. The new methodology will be evaluated via extensive simulation studies, and will be used to accurately assess the effects of lead exposure on the risk of osteoporosis and on decline in cognitive function using data from the Nurse's Health Study case-control study of lead and hypertension.