title: On shrinkage and model extrapolation in the evaluation of clinical center performance.
abstract: We consider statistical methods for benchmarking clinical centers based on a dichotomous outcome indicator. Borrowing ideas from the causal inference literature, we aim to reveal how the entire study population would have fared under the current care level of each center. To this end, we evaluate direct standardization based on fixed versus random center effects outcome models that incorporate patient-specific baseline covariates to adjust for differential case-mix. We explore fixed effects (FE) regression with Firth correction and normal mixed effects (ME) regression to maintain convergence in the presence of very small centers. Moreover, we study doubly robust FE regression to avoid outcome model extrapolation. Simulation studies show that shrinkage following standard ME modeling can result in substantial power loss relative to the considered alternatives, especially for small centers. Results are consistent with findings in the analysis of 30-day mortality risk following acute stroke across 90 centers in the Swedish Stroke Register.
authors.affiliation: Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium machteld.varewyck@ugent.be.
authors.forename: Machteld
authors.initials: M
authors.lastname: Varewyck
authors.affiliation: Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium.
authors.forename: Els
authors.initials: E
authors.lastname: Goetghebeur
authors.affiliation: Department of Statistics, Umeå University, 901 87 Umeå, Sweden.
authors.forename: Marie
authors.initials: M
authors.lastname: Eriksson
authors.affiliation: Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium.
authors.forename: Stijn
authors.initials: S
authors.lastname: Vansteelandt
mesh_terms: D001530:Belgium / Q000453:epidemiology; D006268:Health Facilities / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D010043:Outcome and Process Assessment, Health Care / Q000706:statistics & numerical data*; D057216:Propensity Score; D011785:Quality Assurance, Health Care / Q000706:statistics & numerical data; D011787:Quality of Health Care / Q000706:statistics & numerical data*; D012044:Regression Analysis; D018570:Risk Assessment
keywords: Causal inference; Double robustness; Firth correction; Profiling center performance; Propensity score; Quality of care; Random and fixed effects