TY - JOUR
T1 - Comparing the performance of propensity score methods in healthcare database studies with rare outcomes
AU - Franklin, Jessica M.
AU - Eddings, Wesley
AU - Austin, Peter C.
AU - Stuart, Elizabeth A.
AU - Schneeweiss, Sebastian
N1 - Publisher Copyright:
Copyright © 2017 John Wiley & Sons, Ltd.
PY - 2017/5/30
Y1 - 2017/5/30
N2 - Nonrandomized studies of treatments from electronic healthcare databases are critical for producing the evidence necessary to making informed treatment decisions, but often rely on comparing rates of events observed in a small number of patients. In addition, studies constructed from electronic healthcare databases, for example, administrative claims data, often adjust for many, possibly hundreds, of potential confounders. Despite the importance of maximizing efficiency when there are many confounders and few observed outcome events, there has been relatively little research on the relative performance of different propensity score methods in this context. In this paper, we compare a wide variety of propensity-based estimators of the marginal relative risk. In contrast to prior research that has focused on specific statistical methods in isolation of other analytic choices, we instead consider a method to be defined by the complete multistep process from propensity score modeling to final treatment effect estimation. Propensity score model estimation methods considered include ordinary logistic regression, Bayesian logistic regression, lasso, and boosted regression trees. Methods for utilizing the propensity score include pair matching, full matching, decile strata, fine strata, regression adjustment using one or two nonlinear splines, inverse propensity weighting, and matching weights. We evaluate methods via a ‘plasmode’ simulation study, which creates simulated datasets on the basis of a real cohort study of two treatments constructed from administrative claims data. Our results suggest that regression adjustment and matching weights, regardless of the propensity score model estimation method, provide lower bias and mean squared error in the context of rare binary outcomes.
AB - Nonrandomized studies of treatments from electronic healthcare databases are critical for producing the evidence necessary to making informed treatment decisions, but often rely on comparing rates of events observed in a small number of patients. In addition, studies constructed from electronic healthcare databases, for example, administrative claims data, often adjust for many, possibly hundreds, of potential confounders. Despite the importance of maximizing efficiency when there are many confounders and few observed outcome events, there has been relatively little research on the relative performance of different propensity score methods in this context. In this paper, we compare a wide variety of propensity-based estimators of the marginal relative risk. In contrast to prior research that has focused on specific statistical methods in isolation of other analytic choices, we instead consider a method to be defined by the complete multistep process from propensity score modeling to final treatment effect estimation. Propensity score model estimation methods considered include ordinary logistic regression, Bayesian logistic regression, lasso, and boosted regression trees. Methods for utilizing the propensity score include pair matching, full matching, decile strata, fine strata, regression adjustment using one or two nonlinear splines, inverse propensity weighting, and matching weights. We evaluate methods via a ‘plasmode’ simulation study, which creates simulated datasets on the basis of a real cohort study of two treatments constructed from administrative claims data. Our results suggest that regression adjustment and matching weights, regardless of the propensity score model estimation method, provide lower bias and mean squared error in the context of rare binary outcomes.
KW - epidemiology
KW - healthcare databases
KW - propensity score
KW - risk ratio
KW - simulation
UR - http://www.scopus.com/inward/record.url?scp=85013290379&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85013290379&partnerID=8YFLogxK
U2 - 10.1002/sim.7250
DO - 10.1002/sim.7250
M3 - Article
C2 - 28208229
AN - SCOPUS:85013290379
SN - 0277-6715
VL - 36
SP - 1946
EP - 1963
JO - Statistics in Medicine
JF - Statistics in Medicine
IS - 12
ER -