Weinberg C R, Wacholder S
Division of Biometry and Risk Assessment, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709.
Biometrics. 1990 Dec;46(4):963-75.
A design is proposed for case-control studies in which selection of subjects for full variable ascertainment is based jointly on disease status and on easily obtained "screening" variables that may be related to the disease. Recruitment of subjects follows an independent Bernoulli sampling scheme, with recruitment probabilities set by the investigator in advance. In particular, the sampling can be set up to achieve, on average, frequency matching, provided prior estimates of the disease rates or odds ratios associated with screening variables such as age and sex are available. Alternatively--for example, when studying a rare exposure--one can enrich the sample with certain categories of subject. Following such a design, there are two valid approaches to logistic regression analysis, both of which allow for efficient estimation of effects associated with the screening variables that were allowed to bias the recruitment. The statistical properties of the estimators are compared, both for large samples, based on asymptotics, and for small samples, based on simulations.
本文提出了一种病例对照研究的设计方法，其中，为全面确定变量而选择研究对象时，是基于疾病状态以及易于获取的、可能与疾病相关的“筛查”变量共同进行的。研究对象的招募遵循独立伯努利抽样方案，招募概率由研究者预先设定。具体而言，如果能够获得与年龄和性别等筛查变量相关的疾病发生率或比值比的先验估计值，那么抽样可以设定为平均实现频率匹配。或者，例如在研究罕见暴露时，可以用特定类别的研究对象丰富样本。按照这种设计，有两种有效的逻辑回归分析方法，这两种方法都能有效估计与那些导致招募出现偏差的筛查变量相关的效应。基于渐近理论对大样本以及基于模拟对小样本的估计量的统计特性进行了比较。
