title: Evaluating uses of data mining techniques in propensity score estimation: a simulation study.
abstract: BACKGROUND
  In propensity score modeling, it is a standard practice to optimize the prediction of exposure status based on the covariate information. In a simulation study, we examined in what situations analyses based on various types of exposure propensity score (EPS) models using data mining techniques such as recursive partitioning (RP) and neural networks (NN) produce unbiased and/or efficient results.
  
  
  METHODS
  We simulated data for a hypothetical cohort study (n = 2000) with a binary exposure/outcome and 10 binary/continuous covariates with seven scenarios differing by non-linear and/or non-additive associations between exposure and covariates. EPS models used logistic regression (LR) (all possible main effects), RP1 (without pruning), RP2 (with pruning), and NN. We calculated c-statistics (C), standard errors (SE), and bias of exposure-effect estimates from outcome models for the PS-matched dataset.
  
  
  RESULTS
  Data mining techniques yielded higher C than LR (mean: NN, 0.86; RPI, 0.79; RP2, 0.72; and LR, 0.76). SE tended to be greater in models with higher C. Overall bias was small for each strategy, although NN estimates tended to be the least biased. C was not correlated with the magnitude of bias (correlation coefficient [COR] = -0.3, p = 0.1) but increased SE (COR = 0.7, p < 0.001).
  
  
  CONCLUSIONS
  Effect estimates from EPS models by simple LR were generally robust. NN models generally provided the least numerically biased estimates. C was not associated with the magnitude of bias but was with the increased SE.
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02130, USA. ssetoguchi@partners.org
authors.forename: Soko
authors.initials: S
authors.lastname: Setoguchi
authors.forename: Sebastian
authors.initials: S
authors.lastname: Schneeweiss
authors.forename: M Alan
authors.initials: MA
authors.lastname: Brookhart
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Glynn
authors.forename: E Francis
authors.initials: EF
authors.lastname: Cook
mesh_terms: D015982:Bias; D015331:Cohort Studies; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D006801:Humans; D016015:Logistic Models; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D017891:Pharmacoepidemiology / Q000379:methods*