Document Type
Article
Publication Date
2019
DOI
10.1002/pst.1948
Publication Title
Pharmaceutical Statistics
Volume
18
Issue
5
Pages
568-582
Abstract
In the medical literature, there has been an increased interest in evaluating association between exposure and outcomes using nonrandomized observational studies. However, because assignments to exposure are not random in observational studies, comparisons of outcomes between exposed and nonexposed subjects must account for the effect of confounders. Propensity score methods have been widely used to control for confounding, when estimating exposure effect. Previous studies have shown that conditioning on the propensity score results in biased estimation of conditional odds ratio and hazard ratio. However, research is lacking on the performance of propensity score methods for covariate adjustment when estimating the area under the ROC curve (AUC). In this paper, AUC is proposed as measure of effect when outcomes are continuous. The AUC is interpreted as the probability that a randomly selected nonexposed subject has a better response than a randomly selected exposed subject. A series of simulations has been conducted to examine the performance of propensity score methods when association between exposure and outcomes is quantified by AUC; this includes determining the optimal choice of variables for the propensity score models. Additionally, the propensity score approach is compared with that of the conventional regression approach to adjust for covariates with the AUC. The choice of the best estimator depends on bias, relative bias, and root mean squared error. Finally, an example looking at the relationship of depression/anxiety and pain intensity in people with sickle cell disease is used to illustrate the estimation of the adjusted AUC using the proposed approaches.
Original Publication Citation
Galadima, HI, McClish, DK. Controlling for confounding via propensity score methods can result in biased estimation of the conditional AUC: A simulation study. Pharmaceutical Statistics. 2019; 18: 568– 582. https://doi.org/10.1002/pst.1948
ORCID
0000-0003-1588-3929 (Galadima)
Repository Citation
Galadima, Hadiza I. and McClish, Donna K., "Controlling for Confounding Via Propensity Score Methods Can Result in Biased Estimation of the Conditional AUC: A Simulation Study" (2019). Community & Environmental Health Faculty Publications. 98.
https://digitalcommons.odu.edu/commhealth_fac_pubs/98
Included in
Applied Statistics Commons, Biostatistics Commons, Statistical Methodology Commons, Statistical Models Commons, Statistical Theory Commons