Literature DB >> 22902041

Toward a better understanding of when to apply propensity scoring: a comparison with conventional regression in ethnic disparities research.

Yu Ye1, Jason C Bond, Laura A Schmidt, Nina Mulia, Tammy W Tam.   

Abstract

PURPOSE: Despite growing popularity of propensity score (PS) methods used in ethnic disparities studies, many researchers lack clear understanding of when to use PS in place of conventional regression models. One such scenario is presented here: When the relationship between ethnicity and primary care utilization is confounded with and modified by socioeconomic status. Here, standard regression fails to produce an overall disparity estimate, whereas PS methods can through the choice of a reference sample (RS) to which the effect estimate is generalized.
METHODS: Using data from the National Alcohol Surveys, ethnic disparities between White and Hispanics in access to primary care were estimated using PS methods (PS stratification and weighting), standard logistic regression, and the marginal effects from logistic regression models incorporating effect modification.
RESULTS: Whites, Hispanics, and combined White/Hispanic samples were used separately as the RS. Two strategies utilizing PS generated disparities estimates different from those from standard logistic regression, but similar to marginal odd ratios from logistic regression with ethnicity by covariate interactions included in the model.
CONCLUSIONS: When effect modification is present, PS estimates are comparable with marginal estimates from regression models incorporating effect modification. The estimation process requires a priori hypotheses to guide selection of the RS.
Copyright © 2012 Elsevier Inc. All rights reserved.

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Year:  2012        PMID: 22902041      PMCID: PMC3494414          DOI: 10.1016/j.annepidem.2012.07.008

Source DB:  PubMed          Journal:  Ann Epidemiol        ISSN: 1047-2797            Impact factor:   3.797


  19 in total

1.  Marginalia: comparing adjusted effect measures.

Authors:  Jay S Kaufman
Journal:  Epidemiology       Date:  2010-07       Impact factor: 4.822

2.  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

Review 3.  A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods.

Authors:  Til Stürmer; Manisha Joshi; Robert J Glynn; Jerry Avorn; Kenneth J Rothman; Sebastian Schneeweiss
Journal:  J Clin Epidemiol       Date:  2005-10-13       Impact factor: 6.437

4.  The performance of different propensity score methods for estimating marginal odds ratios.

Authors:  Peter C Austin
Journal:  Stat Med       Date:  2007-07-20       Impact factor: 2.373

5.  Ethnic disparities in clinical severity and services for alcohol problems: results from the National Alcohol Survey.

Authors:  Laura A Schmidt; Yu Ye; Thomas K Greenfield; Jason Bond
Journal:  Alcohol Clin Exp Res       Date:  2007-01       Impact factor: 3.455

Review 6.  U.S. disparities in health: descriptions, causes, and mechanisms.

Authors:  Nancy E Adler; David H Rehkopf
Journal:  Annu Rev Public Health       Date:  2008       Impact factor: 21.981

7.  Systematic differences in treatment effect estimates between propensity score methods and logistic regression.

Authors:  Edwin P Martens; Wiebe R Pestman; Anthonius de Boer; Svetlana V Belitser; Olaf H Klungel
Journal:  Int J Epidemiol       Date:  2008-05-03       Impact factor: 7.196

8.  Estimators and confidence intervals for the marginal odds ratio using logistic regression and propensity score stratification.

Authors:  Susanne Stampf; Erika Graf; Claudia Schmoor; Martin Schumacher
Journal:  Stat Med       Date:  2010-03-30       Impact factor: 2.373

9.  Age-period-cohort modelling of alcohol volume and heavy drinking days in the US National Alcohol Surveys: divergence in younger and older adult trends.

Authors:  William C Kerr; Thomas K Greenfield; Jason Bond; Yu Ye; Jürgen Rehm
Journal:  Addiction       Date:  2009-01       Impact factor: 6.526

10.  Different methods of balancing covariates leading to different effect estimates in the presence of effect modification.

Authors:  Mark Lunt; Daniel Solomon; Kenneth Rothman; Robert Glynn; Kimme Hyrich; Deborah P M Symmons; Til Stürmer
Journal:  Am J Epidemiol       Date:  2009-01-19       Impact factor: 4.897

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  4 in total

1.  Addressing geographic confounding through spatial propensity scores: a study of racial disparities in diabetes.

Authors:  Melanie L Davis; Brian Neelon; Paul J Nietert; Kelly J Hunt; Lane F Burgette; Andrew B Lawson; Leonard E Egede
Journal:  Stat Methods Med Res       Date:  2017-11-16       Impact factor: 3.021

2.  Social determinants of disparities in weight among US children and adolescents.

Authors:  Lauren M Rossen; Makram Talih
Journal:  Ann Epidemiol       Date:  2014-08-01       Impact factor: 3.797

3.  Estimating mental health impacts of alcohol's harms from other drinkers: using propensity scoring methods with national cross-sectional data from the United States.

Authors:  Katherine J Karriker-Jaffe; Libo Li; Thomas K Greenfield
Journal:  Addiction       Date:  2018-06-25       Impact factor: 6.526

4.  Racial Disparities in Acute Coronary Syndrome Management Within a Universal Healthcare Context: Insights From the AMI-OPTIMA Trial.

Authors:  Marc-André d'Entremont; Christina C Wee; Michel Nguyen; Étienne L Couture; Samuel Lemaire-Paquette; Simon Kouz; Marc Afilalo; Stéphane Rinfret; Erick Schampaert; Samer Mansour; Martine Montigny; Mark Eisenberg; Claude Lauzon; Jean-Pierre Déry; Philippe L'Allier; Jean-Claude Tardif; Thao Huynh
Journal:  CJC Open       Date:  2021-07-24
  4 in total

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