Survival analysis is fundamental to cancer epidemiology. For example, breast cancer studies often use endpoints such as time to relapse or time to death. Yet very few data analytic tools exist for exploring such data. Thus, statistical assessments make recourse to routine use of Cox proportional hazards models, despite the strong assumptions embodied in this approach. Additionally, Cox models are not adept at identifying prognostic subgroups of patients. The purpose of this project is to further develop, and disseminate, methods and software for performing tree structured survival analysis. Tree techniques have appeal in that they are conceptually simple, readily interpretable and, being nonparametric, do not impose strong assumptions. Further, they are designed to identify distinct prognostic subgroups. These sub-groups are characterized by having common values of predictor variables thus making for clinically meaningful divisions. Subject to this constraint of like predictor values, the subgroups extracted possess maximally disparate survival prospects. Tree structured survival analysis has already been applied in a range of settings including evaluation of breast cancer markers. However, wider usage has been hampered by software deficiencies. Presently, only working code that is not user-friendly is available. The intention is to use this kernel as a basis for producing a comprehensive tree structured survival analysis program. This will involve writing refined input, output and help facilities. Algorithm efficiency will also be improved. Methodologic developments including means for handling missing data, time-varying predictors and assessing statistical significance will also be incorporated. Applications to two separate breast cancer survival studies will be pursued. The first involves projects falling under the umbrella of the SPORE in breast cancer awarded to UCSF which includes several marker studies. The second concerns an 18 year study of the prognostic value of breast fluid cytology being conducted by the UCSF Department of Epidemiology and Biostatistics.
|Segal, M R; Neuhaus, J M; James, I R (1997) Dependence estimation for marginal models of multivariate survival data. Lifetime Data Anal 3:251-68|