title: [Analysis with the propensity score of the association between likelihood of treatment and event of interest in observational studies. An example with myocardial reperfusion].
abstract: OBJECTIVE
  Analysis of the effect of treatment in observational studies is complex due to differences between treated and nontreated patients. Calculating the probability of receiving treatment conditioned on relevant covariates (propensity score [PS]) has been proposed as a method to control for these differences. We report an application of PS to assess the association between reperfusion treatment and 28-day case fatality in patients with acute myocardial infarction (AMI).
  
  
  METHODS
  We describe the procedure used to calculate PS for receiving reperfusion treatment, and different strategies to analyze the association between PS and case fatality with regression modeling and matching. Data were from a population-based registry of 6307 patients with AMI in Spain during 1997-98.
  
  
  RESULTS
  The PS for reperfusion was calculated in 5622 patients. In the multivariate analysis, reperfusion was associated with lower case fatality (OR = 0.59; 95% confidence interval [95% CI]: 0.46-0.77). When PS was included as a covariate, this association became non- significant (OR = 0.76; 95% CI: 0.57-1.01). In the subgroup of matched patients with a similar PS (n = 3138), treatment was not associated with case fatality (OR = 0.95; 95% CI: 0.72-1.26). When the influence of cases with missing data on PS was controlled for, reperfusion treatment was associated with lower fatality (OR = 0.66; 95% CI: 0.55-0.80).
  
  
  CONCLUSIONS
  Calculating propensity score is a method that controls for differences between treated and nontreated patients. This score has limitations when matching is incomplete and when data are missing. Results of the present example suggest that reperfusion treatment reduces AMI case fatality.
authors.affiliation: Unidad de Lípidos y Epidemiología Cardiovascular, IMIM, Barcelona, España.
authors.forename: Helena
authors.initials: H
authors.lastname: Martí
authors.forename: Jon
authors.initials: J
authors.lastname: Pérez-Bárcena
authors.forename: Miquel
authors.initials: M
authors.lastname: Fiol
authors.forename: Jaume
authors.initials: J
authors.lastname: Marrugat
authors.forename: Carmen
authors.initials: C
authors.lastname: Navarro
authors.forename: Elena
authors.initials: E
authors.lastname: Aldasoro
authors.forename: Adolfo
authors.initials: A
authors.lastname: Cabadés
authors.forename: Antonio
authors.initials: A
authors.lastname: Segura
authors.forename: Rafael
authors.initials: R
authors.lastname: Masiá
authors.forename: Javier
authors.initials: J
authors.lastname: Turumbay
authors.forename: Lluís
authors.initials: L
authors.lastname: Cirera
authors.forename: José M
authors.initials: JM
authors.lastname: Arteagoitia
authors.forename: Carlos A
authors.initials: CA
authors.lastname: Tomás
authors.forename: Gema
authors.initials: G
authors.lastname: Vega
authors.forename: Joan
authors.initials: J
authors.lastname: Sala
authors.forename: Enrique
authors.initials: E
authors.lastname: de Los Arcos
authors.forename: María J
authors.initials: MJ
authors.lastname: Tormo
authors.forename: Iraida
authors.initials: I
authors.lastname: Hurtado-de-Saracho
authors.forename: Mercedes
authors.initials: M
authors.lastname: Francés-Sempere
authors.forename: Roberto
authors.initials: R
authors.lastname: Elosua
mesh_terms: D000328:Adult; D000368:Aged; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009203:Myocardial Infarction / Q000401:mortality / Q000628:therapy*; D015425:Myocardial Reperfusion / Q000706:statistics & numerical data*; D016017:Odds Ratio; D011379:Prognosis; D012042:Registries; D012307:Risk Factors; D013030:Spain / Q000453:epidemiology