title: Utility of time-dependent inverse-probability-of-treatment weights to analyze observational cohorts in the intensive care unit.
abstract: OBJECTIVE
  When analyzing observational databases, marginal structural models (MSMs) may offer an appealing approach to estimate causal effects. We aimed at evaluating MSMs, in accounting for confounding when assessing the benefit of intensive care unit (ICU) admission and on its interaction with patient age, as compared with propensity score (PS) matching.
  
  
  METHODS
  PS and inverse-probability-of-treatment weights for MSMs were derived from an observational study designed to evaluate the benefit of ICU admission on in-hospital mortality. Only first ICU triages (time-fixed weights) or whole triage history (time-dependent weights) were considered. Weights were stabilized by either the prevalence of the actual treatment or the probability of the actual treatment given baseline covariates. Risk difference (RD) was the main outcome measure.
  
  
  RESULTS
  MSMs with time-dependent weights offered the best reduction in the baseline imbalances as compared with PS matching. No effect of ICU admission on in-hospital mortality was found (RD=0.010; 95% confidence interval=-0.038, 0.052) with no interaction between age and treatment.
  
  
  CONCLUSIONS
  MSMs appear interesting to handle selection bias in observational studies. When confounding evolves over time, the use of time-dependent weights should be stressed out.
authors.affiliation: Department of Biostatistics, Hôpital Saint Louis, Université Paris 7 Diderot, Paris, France. romainpirracchio@yahoo.fr
authors.forename: Romain
authors.initials: R
authors.lastname: Pirracchio
authors.forename: Charles L
authors.initials: CL
authors.lastname: Sprung
authors.forename: Didier
authors.initials: D
authors.lastname: Payen
authors.forename: Sylvie
authors.initials: S
authors.lastname: Chevret
mesh_terms: D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D016001:Confidence Intervals; D015986:Confounding Factors, Epidemiologic; D005602:France / Q000453:epidemiology; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units; D008875:Middle Aged; D010343:Patient Admission / Q000706:statistics & numerical data*; D057216:Propensity Score; D012307:Risk Factors; D015983:Selection Bias; D014218:Triage