Open AccessStandardizing Discrete-Time Hazard Ratios With a Disease Risk Score
Author(s) -
David B. Richardson,
Alexander P. Keil,
Jessie K. Edwards,
Alan C Kinlaw,
Stephen R. Cole
Publication year - 2020
Publication title -
american journal of epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.33
H-Index - 256
eISSN - 1476-6256
pISSN - 0002-9262
DOI - 10.1093/aje/kwaa061
Subject(s) - covariate , hazard ratio , propensity score matching , confounding , hazard , medicine , cohort , statistics , proportional hazards model , standardization , cohort study , estimation , econometrics , confidence interval , computer science , mathematics , engineering , chemistry , organic chemistry , systems engineering , operating system
The disease risk score (DRS) is a summary score that is a function of a potentially large set of covariates. The DRS can be used to control for confounding by the covariates that went into estimation of the DRS and obtain a standardized estimate of an exposure's effect on disease. However, to date, literature on the DRS has not addressed analyses that focus on estimation of survival or hazard functions, which are common in epidemiologic analyses of cohort data. Here, we propose a method for standardization of hazard ratios using the DRS in longitudinal analyses of the association between a binary exposure and an outcome. This approach to handling a potentially large set of covariates through a model-based approach to standardization may provide a useful tool for cohort analyses of hazard ratios and may be particularly well-suited to settings where an exposure propensity score is difficult to model. Simulations are used in this paper to illustrate the approach, and an empirical example is provided.