title: Comparison of Cox and Gray's survival models in severe sepsis.
abstract: BACKGROUND
  Although survival is traditionally modeled using Cox proportional hazards modeling, this approach may be inappropriate in sepsis, in which the proportional hazards assumption does not hold. Newer, more flexible models, such as Gray's model, may be more appropriate.
  
  
  OBJECTIVE
  To construct and compare Gray's model and two different Cox models in a large sepsis cohort. To determine whether hazards for death after sepsis were nonproportional. To explore how well the different survival modeling approaches describe these data.
  
  
  METHODS
  Analysis of combined data from the treatment and placebo arms of a large, negative, sepsis trial.
  
  
  METHODS
  Intensive care units at 136 U.S. medical centers.
  
  
  METHODS
  A total of 1090 adults aged 18 yrs or older with signs and symptoms of severe sepsis and documented or probable Gram-negative infection.
  
  
  METHODS
  We considered 27 potential baseline risk factors and modeled survival over the 28 days after the onset of sepsis. We tested proportionality in single-variable Cox analysis using Schoenfeld residuals and log-log plots. We constructed a traditional multivariable Cox model, a multivariable Cox model with time-varying covariates, and a multivariable Gray's model.
  
  
  RESULTS
  In single-variable analyses, 20 of the 27 potential factors were significantly associated with mortality, and 10 of 20 had nonproportional hazards. In multivariate analysis, all three models retained a very similar set of significant covariates (two models retained the identical set of nine variables, and the third differed only in that it retained the same nine plus a tenth variable). Four of the nine common covariates had nonproportional hazards. Of the three models, Gray's model best captured these changing hazard ratios over time.
  
  
  CONCLUSIONS
  We confirm that many of the important predictors of mortality in severe sepsis are nonproportional and find that Gray's model seems best suited for modeling survival in this condition.
authors.affiliation: CRISMA Laboratory (Clinical Research, Investigation, and Systems Modeling of Acute Illness), Department of Critical Care Medicine, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Jan
authors.initials: J
authors.lastname: Kasal
authors.forename: Zorana
authors.initials: Z
authors.lastname: Jovanovic
authors.forename: Gilles
authors.initials: G
authors.lastname: Clermont
authors.forename: Lisa A
authors.initials: LA
authors.lastname: Weissfeld
authors.forename: Vladimir
authors.initials: V
authors.lastname: Kaplan
authors.forename: R Scott
authors.initials: RS
authors.lastname: Watson
authors.forename: Derek C
authors.initials: DC
authors.lastname: Angus
mesh_terms: D000328:Adult; D000368:Aged; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015999:Multivariate Analysis; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D012307:Risk Factors; D018805:Sepsis / Q000175:diagnosis / Q000401:mortality*; D016019:Survival Analysis; D013997:Time Factors; D016896:Treatment Outcome; D014481:United States / Q000453:epidemiology