title: The estimation of average hazard ratios by weighted Cox regression.
abstract: Often the effect of at least one of the prognostic factors in a Cox regression model changes over time, which violates the proportional hazards assumption of this model. As a consequence, the average hazard ratio for such a prognostic factor is under- or overestimated. While there are several methods to appropriately cope with non-proportional hazards, in particular by including parameters for time-dependent effects, weighted estimation in Cox regression is a parsimonious alternative without additional parameters. The methodology, which extends the weighted k-sample logrank tests of the Tarone-Ware scheme to models with multiple, binary and continuous covariates, has been introduced in the nineties of the last century and is further developed and re-evaluated in this contribution. The notion of an average hazard ratio is defined and its connection to the effect size measure P(X<Y) is emphasized. The suggested approach accomplishes estimation of intuitively interpretable average hazard ratios and provides tools for inference. A Monte Carlo study confirms the satisfactory performance. Advantages of the approach are exemplified by comparing standard and weighted analyses of an international lung cancer study. SAS and R programs facilitate application.
authors.affiliation: Section of Clinical Biometrics, Department for Medical Statistics and Informatics, Medical University of Vienna, Spitalgasse 23, A-1090 Vienna, Austria. michael.schemper@meduniwien.ac.at
authors.forename: Michael
authors.initials: M
authors.lastname: Schemper
authors.forename: Samo
authors.initials: S
authors.lastname: Wakounig
authors.forename: Georg
authors.initials: G
authors.lastname: Heinze
mesh_terms: D001699:Biometry; D006801:Humans; D053208:Kaplan-Meier Estimate; D008175:Lung Neoplasms / Q000401:mortality / Q000628:therapy; D009010:Monte Carlo Method; D011379:Prognosis; D016016:Proportional Hazards Models; D016019:Survival Analysis