Estimating cumulative treatment effects in the presence of nonproportional hazards.
Often in medical studies of time to an event, the treatment effect is not constant over time. In the context of Cox regression modeling, the most frequent solution is to apply a model that assumes the treatment effect is either piecewise constant or varies smoothly over time, i.e., the Cox nonproportional hazards model. This approach has at least two major limitations. First, it is generally difficult to assess whether the parametric form chosen for the treatment effect is correct. Second, in the presence of nonproportional hazards, investigators are usually more interested in the cumulative than the instantaneous treatment effect (e.g., determining if and when the survival functions cross). Therefore, we propose an estimator for the aggregate treatment effect in the presence of nonproportional hazards. Our estimator is based on the treatment-specific baseline cumulative hazards estimated under a stratified Cox model. No functional form for the nonproportionality need be assumed. Asymptotic properties of the proposed estimators are derived, and the finite-sample properties are assessed in simulation studies. Pointwise and simultaneous confidence bands of the estimator can be computed. The proposed method is applied to data from a national organ failure registry.
Wei
Guanghui
G
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109-2029, U.S.A.
Schaubel
Douglas E
DE
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109-2029, U.S.A.
D001699:Biometry / Q000379:methods*; D016001:Confidence Intervals; D006801:Humans; D007676:Kidney Failure, Chronic / Q000401:mortality / Q000628:therapy; D010530:Peritoneal Dialysis / Q000706:statistics & numerical data; D016016:Proportional Hazards Models; D012042:Registries / Q000706:statistics & numerical data; D006435:Renal Dialysis / Q000706:statistics & numerical data; D016019:Survival Analysis; D016896:Treatment Outcome