title: A new weighted balance measure helped to select the variables to be included in a propensity score model.
abstract: OBJECTIVE
  The propensity score (PS) is a balancing score. Following PS matching, balance checking usually relies on estimating separately the standardized absolute mean difference for each baseline characteristic. The average standardized absolute mean difference and the Mahalanobis distances have been proposed to summarize the information across the covariates. However, they might be minimized when nondesirable variables such as instrumental variables (IV) are included in the PS model. We propose a new weighted summary balance measure that takes into account, for each covariate, its strength of association with the outcome.
  
  
  METHODS
  This new measure was evaluated using a simulation study to assess whether minimization of the measure coincided with minimally biased estimates. All measures were then applied to a real data set from an observational cohort study.
  
  
  RESULTS
  Contrarily to the other measures, our proposal was minimized when including the confounders, which coincided with minimal bias and mean squared error, but increased when including an IV in the PS model. Similar findings were observed in the real data set.
  
  
  CONCLUSIONS
  A balance measure taking into account the strength of association between the covariates and the outcome may be helpful to identify the most parsimonious PS model.
authors.affiliation: Service de Biostatistique et Information Médicale, Hôpital Saint-Louis, AP-HP, Inserm 1153 ECSTRA Team, Université Paris 7 Diderot, 1 rue Claude Vellefaux, Paris 75010, France.
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Caruana
authors.affiliation: Service de Biostatistique et Information Médicale, Hôpital Saint-Louis, AP-HP, Inserm 1153 ECSTRA Team, Université Paris 7 Diderot, 1 rue Claude Vellefaux, Paris 75010, France.
authors.forename: Sylvie
authors.initials: S
authors.lastname: Chevret
authors.affiliation: Service de Biostatistique et Information Médicale, Hôpital Saint-Louis, AP-HP, Inserm 1153 ECSTRA Team, Université Paris 7 Diderot, 1 rue Claude Vellefaux, Paris 75010, France.
authors.forename: Matthieu
authors.initials: M
authors.lastname: Resche-Rigon
authors.affiliation: Service de Biostatistique et Information Médicale, Hôpital Saint-Louis, AP-HP, Inserm 1153 ECSTRA Team, Université Paris 7 Diderot, 1 rue Claude Vellefaux, Paris 75010, France; Service d'Anesthésie-Réanimation, Hôpital Européen Georges Pompidou, Université Paris V Descartes-Sorbonne Paris Cité, 20 rue Leblanc, Paris 75015, France. Electronic address: romainpirracchio@yahoo.fr.
authors.forename: Romain
authors.initials: R
authors.lastname: Pirracchio
mesh_terms: D000208:Acute Disease; D001120:Arginine / Q000031:analogs & derivatives; D015982:Bias; D015331:Cohort Studies; D003198:Computer Simulation; D045422:Continuous Positive Airway Pressure / Q000706:statistics & numerical data*; D003625:Data Collection / Q000706:statistics & numerical data*; D005602:France; D006333:Heart Failure / Q000401:mortality* / Q000628:therapy*; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data; D063847:Mean Platelet Volume; D015233:Models, Statistical; D009010:Monte Carlo Method; D064887:Observational Studies as Topic / Q000706:statistics & numerical data; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data; D010875:Pipecolic Acids; D057216:Propensity Score; D013449:Sulfonamides
chemical_list: D010875:Pipecolic Acids; D013449:Sulfonamides; D001120:Arginine; C031942:argatroban
keywords: Balance; Causal inference; Instrumental variables; Propensity score; Propensity score matching; Standardized mean difference