Literature DB >> 32347298

Standardizing Discrete-Time Hazard Ratios With a Disease Risk Score.

David B Richardson, Alexander P Keil, Jessie K Edwards, Alan C Kinlaw, Stephen R Cole.   

Abstract

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.
© The Author(s) 2020. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Keywords:  cohort studies; disease risk score; regression analysis; standardization

Mesh:

Year:  2020        PMID: 32347298      PMCID: PMC7666420          DOI: 10.1093/aje/kwaa061

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  20 in total

1.  Marginal structural models as a tool for standardization.

Authors:  Tosiya Sato; Yutaka Matsuyama
Journal:  Epidemiology       Date:  2003-11       Impact factor: 4.822

2.  Propensity score methods for confounding control in nonexperimental research.

Authors:  M Alan Brookhart; Richard Wyss; J Bradley Layton; Til Stürmer
Journal:  Circ Cardiovasc Qual Outcomes       Date:  2013-09-10

3.  Analytic strategies to adjust confounding using exposure propensity scores and disease risk scores: nonsteroidal antiinflammatory drugs and short-term mortality in the elderly.

Authors:  Til Stürmer; Sebastian Schneeweiss; M Alan Brookhart; Kenneth J Rothman; Jerry Avorn; Robert J Glynn
Journal:  Am J Epidemiol       Date:  2005-05-01       Impact factor: 4.897

4.  Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect.

Authors:  Tobias Kurth; Alexander M Walker; Robert J Glynn; K Arnold Chan; J Michael Gaziano; Klaus Berger; James M Robins
Journal:  Am J Epidemiol       Date:  2005-12-21       Impact factor: 4.897

5.  Estimating causal effects from epidemiological data.

Authors:  Miguel A Hernán; James M Robins
Journal:  J Epidemiol Community Health       Date:  2006-07       Impact factor: 3.710

Review 6.  Use of disease risk scores in pharmacoepidemiologic studies.

Authors:  Patrick G Arbogast; Wayne A Ray
Journal:  Stat Methods Med Res       Date:  2008-06-18       Impact factor: 3.021

7.  Marginal Structural Models for Risk or Prevalence Ratios for a Point Exposure Using a Disease Risk Score.

Authors:  David B Richardson; Alexander P Keil; Alan C Kinlaw; Stephen R Cole
Journal:  Am J Epidemiol       Date:  2019-05-01       Impact factor: 4.897

8.  Constructing inverse probability weights for continuous exposures: a comparison of methods.

Authors:  Ashley I Naimi; Erica E M Moodie; Nathalie Auger; Jay S Kaufman
Journal:  Epidemiology       Date:  2014-03       Impact factor: 4.822

9.  Assessing Exposure-Response Trends Using the Disease Risk Score.

Authors:  David B Richardson; Alexander P Keil; Stephen R Cole; Alan C Kinlaw
Journal:  Epidemiology       Date:  2020-03       Impact factor: 4.822

10.  Role of disease risk scores in comparative effectiveness research with emerging therapies.

Authors:  Robert J Glynn; Joshua J Gagne; Sebastian Schneeweiss
Journal:  Pharmacoepidemiol Drug Saf       Date:  2012-05       Impact factor: 2.890

View more
  1 in total

1.  On Variance of the Treatment Effect in the Treated When Estimated by Inverse Probability Weighting.

Authors:  Sarah A Reifeis; Michael G Hudgens
Journal:  Am J Epidemiol       Date:  2022-05-20       Impact factor: 5.363

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.