Proportion of treatment effect (PTE) explained by a surrogate marker.
In time-varying covariate analysis of clinical survival data, it is often of interest to estimate the proportion of treatment effect (PTE), along with its confidence intervals, explained by a surrogate marker. The conventional procedure for such an analysis fits data into two working models separately to estimate the treatment effects before and after adjustment of the covariate. The construction of confidence intervals for the PTE under the conventional procedure lacks support by standard statistical software such as SAS, and could be very computationally demanding even after the support is available in the future. To overcome this problem, we propose a new procedure to simplify the computation. Under the new procedure, the treatment effects before and after adjustment of the covariate are simultaneously estimated from a single model. More important than saving computational effort, the new procedure can also be effectively applied to multiple-covariate models for the decomposition of overall treatment effect and for the comparison of PTE among several surrogate markers. The new procedure is applied to the motivating data example from the LIFE study, and demonstrates flexibility that the conventional procedure currently lacks.
Chen
Cong
C
Merck Research Laboratories, BL X-27, PO Box 4, West Point, PA 19486, USA. cong_chen@merck.com
Wang
Hongwei
H
Snapinn
Steven M
SM
D000959:Antihypertensive Agents / Q000627:therapeutic use; D001262:Atenolol / Q000627:therapeutic use; D015415:Biomarkers; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D006801:Humans; D006973:Hypertension / Q000188:drug therapy; D019808:Losartan / Q000627:therapeutic use; D015233:Models, Statistical; D009010:Monte Carlo Method; D016896:Treatment Outcome; D014527:Uric Acid / Q000097:blood
D000959:Antihypertensive Agents; D015415:Biomarkers; D014527:Uric Acid; D001262:Atenolol; D019808:Losartan