On Modeling and Estimation for the Relative Risk and Risk Difference

• 成果类型: Article
• 署名作者: Richardson, Thomas S.; Robins, James M.; Wang, Linbo
• 署名单位: University of Washington; University of Washington Seattle; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard T.H. Chan School of Public Health; University of Washington; University of Washington Seattle
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2016.1192546
• 发表日期: 2017
• 页码: 1121-1130
• 关键词: trials

摘要

A common problem in formulating models for the relative risk and risk difference is the variation dependence between these parameters and the baseline risk, which is a nuisance model. We address this problem by proposing the conditional log odds-product as a preferred nuisance model. This novel nuisance model facilitates maximum-likelihood estimation, but also permits doubly-robust estimation for the parameters of interest. Our approach is illustrated via simulations and a data analysis. An R package brm implementing the proposed methods is available on CRAN. Supplementary materials for this article are available online.