Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
Epidemiology. 2013 Mar;24(2):215-23. doi: 10.1097/EDE.0b013e31827f4fa9.
Researchers often recruit proxy respondents, such as relatives or caregivers, for epidemiologic studies of older adults when study participants are unable to provide self-reports (eg, because of illness or cognitive impairment). In most studies involving proxy-reported outcomes, proxies are recruited only to report on behalf of participants who have missing self-reported outcomes; thus, either a proxy report or participant self-report, but not both, is available for each participant. When outcomes are binary and investigators conceptualize participant self-reports as gold standard measures, substituting proxy reports in place of missing participant self-reports in statistical analysis can introduce misclassification error and lead to biased parameter estimates. However, excluding observations from participants with missing self-reported outcomes may also lead to bias. We propose a pattern-mixture model that uses error-prone proxy reports to reduce selection bias from missing outcomes, and we describe a sensitivity analysis to address bias from differential outcome misclassification. We perform model estimation with high-dimensional (eg, continuous) covariates using propensity-score stratification and multiple imputation. We apply the methods to the Second Cohort of the Baltimore Hip Studies, a study of elderly hip fracture patients, to assess the relation between type of surgical treatment and perceived physical recovery. Simulation studies show that the proposed methods perform well. We provide SAS programs in the eAppendix (http://links.lww.com/EDE/A646) to enhance the methods' accessibility.
研究人员在对老年人进行流行病学研究时，经常会招募代理受访者（例如亲属或护理人员），以代替那些无法提供自我报告的研究参与者（例如，由于疾病或认知障碍）。在大多数涉及代理报告结果的研究中，代理受访者仅被招募来代表那些缺失自我报告结果的参与者进行报告；因此，对于每个参与者，要么有代理报告，要么有参与者的自我报告，但两者都没有。当结果为二分类且研究人员将参与者的自我报告视为金标准测量时，在统计分析中用代理报告代替缺失的参与者自我报告可能会引入分类错误，并导致参数估计偏倚。但是，排除缺失自我报告结果的参与者的观察结果也可能会导致偏倚。我们提出了一种模式混合模型，该模型使用容易出错的代理报告来减少缺失结果导致的选择偏倚，并描述了一种敏感性分析来解决因结果分类错误而导致的偏倚。我们使用倾向评分分层和多重插补对具有高维（例如，连续）协变量的模型进行估计。我们将这些方法应用于巴尔的摩髋部研究的第二队列，该研究是对老年髋部骨折患者的研究，以评估手术治疗类型与感知身体恢复之间的关系。模拟研究表明，所提出的方法表现良好。我们在电子附录（http://links.lww.com/EDE/A646）中提供了 SAS 程序，以提高方法的可及性。
