当前位置: X-MOL 学术Biostatistics › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Estimation and inference for the population attributable risk in the presence of misclassification.
Biostatistics ( IF 2.1 ) Pub Date : 2021-10-13 , DOI: 10.1093/biostatistics/kxz067
Benedict H W Wong 1 , Jooyoung Lee 2 , Donna Spiegelman 3 , Molin Wang 4
Affiliation  

Because it describes the proportion of disease cases that could be prevented if an exposure were entirely eliminated from a target population as a result of an intervention, estimation of the population attributable risk (PAR) has become an important goal of public health research. In epidemiologic studies, categorical covariates are often misclassified. We present methods for obtaining point and interval estimates of the PAR and the partial PAR (pPAR) in the presence of misclassification, filling an important existing gap in public health evaluation methods. We use a likelihood-based approach to estimate parameters in the models for the disease and for the misclassification process, under main study/internal validation study and main study/external validation study designs, and various plausible assumptions about transportability. We assessed the finite sample perf ormance of this method via a simulation study, and used it to obtain corrected point and interval estimates of the pPAR for high red meat intake and alcohol intake in relation to colorectal cancer incidence in the HPFS, where we found that the estimated pPAR for the two risk factors increased by up to 317% after correcting for bias due to misclassification.

中文翻译:

在存在错误分类的情况下估计和推断人群归因风险。

因为它描述了如果由于干预而从目标人群中完全消除暴露,则可以预防的疾病病例的比例,因此估计人群归因风险 (PAR) 已成为公共卫生研究的重要目标。在流行病学研究中,分类协变量经常被错误分类。我们提出了在存在错误分类的情况下获得 PAR 和部分 PAR (pPAR) 的点和区间估计的方法,填补了公共卫生评估方法中现有的一个重要空白。在主要研究/内部验证研究和主要研究/外部验证研究设计以及关于可移植性的各种合理假设下,我们使用基于可能性的方法来估计疾病模型和错误分类过程中的参数。
更新日期:2020-02-29
down
wechat
bug