title: Adjusted survival curves with inverse probability weights.
abstract: Kaplan-Meier survival curves and the associated nonparametric log rank test statistic are methods of choice for unadjusted survival analyses, while the semiparametric Cox proportional hazards regression model is used ubiquitously as a method for covariate adjustment. The Cox model extends naturally to include covariates, but there is no generally accepted method to graphically depict adjusted survival curves. The authors describe a method and provide a simple worked example using inverse probability weights (IPW) to create adjusted survival curves. When the weights are non-parametrically estimated, this method is equivalent to direct standardization of the survival curves to the combined study population.
authors.affiliation: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street E-7014, Baltimore, MD 21205, USA. scole@jhu.edu
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Cole
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Hernán
mesh_terms: D006801:Humans; D011336:Probability; D016016:Proportional Hazards Models; D016019:Survival Analysis; D014481:United States