Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
권호 표지
DOI QR코드

DOI QR Code

Performance study of propensity score methods against regression with covariate adjustment

  • Received : 2014.11.05
  • Accepted : 2014.12.05
  • Published : 2015.01.31
Download PDF
⟨ Previous Next ⟩

Abstract

In observational study, handling confounders is a primary issue in measuring treatment effect of interest. Historically, a regression with covariate adjustment (covariate-adjusted regression) has been the typical approach to estimate treatment effect incorporating potential confounders into model. However, ever since the introduction of the propensity score, covariate-adjusted regression has been gradually replaced in medical literatures with various balancing methods based on propensity score. On the other hand, there is only a paucity of researches assessing propensity score methods compared with the covariate-adjusted regression. This paper examined the performance of propensity score methods in estimating risk difference and compare their performance with the covariate-adjusted regression by a Monte Carlo study. The study demonstrated in general the covariate-adjusted regression with variable selection procedure outperformed propensity-score-based methods in terms both of bias and MSE, suggesting that the classical regression method needs to be considered, rather than the propensity score methods, if a performance is a primary concern.

Keywords

References

  1. Austin, P. C. (2008). The performance of different propensity-score methods for estimating differences in proportions (risk differences or absolute risk reductions) in observational studies. Statistics in Medicine, 29, 2137-2148.
  2. Austin, P. C. (2010). A data-generation process for data with specified risk differences or numbers needed to treat. Communications in Statistics - Simulation and Computation, 39, 563-577. https://doi.org/10.1080/03610910903528301
  3. Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46, 399-424. https://doi.org/10.1080/00273171.2011.568786
  4. Austin, P. C. (2013). The performance of different propensity score methods for estimating marginal hazard ratios. Statistics in Medicine, 32, 2837-2849. https://doi.org/10.1002/sim.5705
  5. Austin, P. C., Grootendorst, P. and Anderson, G. M. (2007). A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: A Monte Carlo study. Statistics in Medicine, 26, 734-753. https://doi.org/10.1002/sim.2580
  6. Bender, R., Augustin, T. and Blettner, M. (2005). Generating survival times to simulate Cox proportional hazards models. Statistics in Medicine, 24, 1713-1723. https://doi.org/10.1002/sim.2059
  7. Ho, D. E., Imai, K., King, G. and Stuart, E. A. (2011). Nonparametric preprocessing for parametric causal inference. Journal of Statistical Software, 42-8, 1-28.
  8. Lunceford, J. K. and Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: A comparative study. Statistics in Medicine, 23, 2937-2960. https://doi.org/10.1002/sim.1903
  9. Rosenbaum, P. R. (1987). Model-based direct adjustment. The Journal of the American Statistician, 82, 387-394.
  10. Rosenbaum, P. R. (2010). Design of observational studies, Springer Series in Statistics, New York.
  11. Rosenbaum, P. R. and Rubin, D.B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41-55. https://doi.org/10.1093/biomet/70.1.41
  12. Shah, B. R., Laupacis, A., Hux, J. E. and Austin, P. C. (2005). Propensity score methods give similar results to traditional regression modeling in observational studies: A systematic review. Journal of Clinical Epidemiology, 58, 550-559. https://doi.org/10.1016/j.jclinepi.2004.10.016
  13. Susanne, S. (2010). Propensity score methods in observational studies- estimating the marginal odds ratio, Ph.D Dissertation, Albert Ludwig University of Freiburg, Germany.
  14. Weitzen, S., Lapane, K. L., Toledano, A. Y., Hume, A. L. and Mor, V. (2004). Principles for modeling propensity scores in medical research: A systematic literature review. Pharmacoepidemiology and Drug Safety, 13, 841-853. https://doi.org/10.1002/pds.969

Cited by

  1. FUZZY matching using propensity score: IBM SPSS 22 Ver. vol.27, pp.1, 2016, https://doi.org/10.7465/jkdi.2016.27.1.91
  2. Clinical data analysis in retrospective study through equality adjustment between groups vol.26, pp.6, 2015, https://doi.org/10.7465/jkdi.2015.26.6.1317