title: Confounding control in a nonexperimental study of STAR*D data: logistic regression balanced covariates better than boosted CART.
abstract: OBJECTIVE
  Propensity scores (PSs), a powerful bias-reduction tool, can balance treatment groups on measured covariates in nonexperimental studies. We demonstrate the use of multiple PS estimation methods to optimize covariate balance.
  
  
  METHODS
  We used secondary data from 1292 adults with nonpsychotic major depressive disorder in the Sequenced Treatment Alternatives to Relieve Depression trial (2001-2004). After initial citalopram treatment failed, patient preference influenced assignment to medication augmentation (n = 565) or switch (n = 727). To reduce selection bias, we used boosted classification and regression trees (BCART) and logistic regression iteratively to identify two potentially optimal PSs. We assessed and compared covariate balance.
  
  
  RESULTS
  After iterative selection of interaction terms to minimize imbalance, logistic regression yielded better balance than BCART (average standardized absolute mean difference across 47 covariates: 0.03 vs. 0.08, matching; 0.02 vs. 0.05, weighting).
  
  
  CONCLUSIONS
  Comparing multiple PS estimates is a pragmatic way to optimize balance. Logistic regression remains valuable for this purpose. Simulation studies are needed to compare PS models under varying conditions. Such studies should consider more flexible estimation methods, such as logistic models with automated selection of interactions or hybrid models using main effects logistic regression instead of a constant log-odds as the initial model for BCART.
authors: Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7590, USA. are@unc.edu
authors: Alan R
authors: AR
authors: Ellis
authors: Stacie B
authors: SB
authors: Dusetzina
authors: Richard A
authors: RA
authors: Hansen
authors: Bradley N
authors: BN
authors: Gaynes
authors: Joel F
authors: JF
authors: Farley
authors: Til
authors: T
authors: Stürmer
mesh_terms: D000328:Adult; D000368:Aged; D018687:Antidepressive Agents, Second-Generation / Q000627:therapeutic use; D015283:Citalopram / Q000627:therapeutic use; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D003865:Major Depressive Disorder / Q000188:drug therapy; D057915:Drug Substitution / Q000379:methods; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods*; D057216:Propensity Score
chemical_list: D018687:Antidepressive Agents, Second-Generation; D015283:Citalopram