title: Propensity score model overfitting led to inflated variance of estimated odds ratios.
abstract: Simulation studies suggest that the ratio of the number of events to the number of estimated parameters in a logistic regression model should be not less than 10 or 20 to 1 to achieve reliable effect estimates. Applications of propensity score approaches for confounding control in practice, however, do often not consider these recommendations.
  
  
  
  We conducted extensive Monte Carlo and plasmode simulation studies to investigate the impact of propensity score model overfitting on the performance in estimating conditional and marginal odds ratios using different established propensity score inference approaches. We assessed estimate accuracy and precision as well as associated type I error and type II error rates in testing the null hypothesis of no exposure effect.
  
  
  
  For all inference approaches considered, our simulation study revealed considerably inflated standard errors of effect estimates when using overfitted propensity score models. Overfitting did not considerably affect type I error rates for most inference approaches. However, because of residual confounding, estimation performance and type I error probabilities were unsatisfactory when using propensity score quintile adjustment.
  
  
  
  Overfitting of propensity score models should be avoided to obtain reliable estimates of treatment or exposure effects in individual studies.
authors: Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, 3755 Chemin de la Côte-Sainte-Catherine, Montréal, Québec H3T 1E2, Canada; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Purvis Hall, 1020 Pine Avenue West, Montréal, Québec H3A 1A2, Canada; Clinical Epidemiology and Biostatistics Unit and the Melbourne Children's Trial Centre, Murdoch Childrens Research Institute, Royal Children's Hospital, 50 Flemington Road, Parkville, Victoria 3052, Australia; Department of Paediatrics, University of Melbourne, Melbourne, Victoria 3010, Australia. Electronic address: Tibor.Schuster@mcri.edu.au.
authors: Tibor
authors: T
authors: Schuster
authors: Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, 3755 Chemin de la Côte-Sainte-Catherine, Montréal, Québec H3T 1E2, Canada; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Purvis Hall, 1020 Pine Avenue West, Montréal, Québec H3A 1A2, Canada; UFR de Mathématique et d'Informatique, Université de Strasbourg, 7 Rue René Descartes, 67084 Strasbourg, France.
authors: Wilfrid Kouokam
authors: WK
authors: Lowe
authors: Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Purvis Hall, 1020 Pine Avenue West, Montréal, Québec H3A 1A2, Canada; Department of Pediatrics, McGill University, Montreal Children's Hospital, 1001 Décarie Boulevard, Montreal, Québec H4A 3J1, Canada.
authors: Robert W
authors: RW
authors: Platt
mesh_terms: D015340:Epidemiologic Research Design; D006801:Humans; D016017:Odds Ratio; D057216:Propensity Score; D015203:Reproducibility of Results
keywords: Confounder adjustment; Inverse probability weighting; Logistic regression; Odds ratio; Overfitting; Propensity score