Merits and caveats of propensity scores to adjust for confounding

Edouard L Fu; Rolf H H Groenwold; Carmine Zoccali; Kitty J Jager; Merel van Diepen; Friedo W Dekker

doi:10.1093/ndt/gfy283

Merits and caveats of propensity scores to adjust for confounding

Nephrol Dial Transplant. 2019 Oct 1;34(10):1629-1635. doi: 10.1093/ndt/gfy283.

Authors

Edouard L Fu¹, Rolf H H Groenwold¹, Carmine Zoccali², Kitty J Jager³, Merel van Diepen¹, Friedo W Dekker¹

Affiliations

¹ Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
² CNR-IFC, Center of Clinical Physiology, Clinical Epidemiology of Renal Diseases and Hypertension, Reggio Calabria, Italy.
³ Department of Medical Informatics, ERA-EDTA Registry, Amsterdam UMC, Amsterdam Public Health Research institute, University of Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands.

PMID: 30215791
DOI: 10.1093/ndt/gfy283

Abstract

Proper adjustment for confounding is essential when estimating the effects of treatments or risk factors on health outcomes in observational data. To this end, various statistical methods have been developed. In the past couple of years, the use of propensity scores (PSs) to control for confounding has increased. Proper understanding of this method is necessary to critically appraise research in which it is applied. In this article, we provide an overview of PS methods, explaining their concept, advantages and possible disadvantages. Furthermore, the use of PS matching, PS adjustment and PS weighting is illustrated using data from the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD) cohort of dialysis patients.

Keywords: cohort studies; confounding; dialysis; observational research; propensity score.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Cohort Studies
Confounding Factors, Epidemiologic*
Humans
Models, Statistical*
Netherlands / epidemiology
Propensity Score*
Renal Dialysis*
Research Design*
Risk Factors