Extensions of the Cox proportional hazards model for survival data are studied where allowance is made for unobserved heterogeneity and for correlation between the life times of several individuals. The extendedmodels are frailty models inspired by Yashin et al. (1995). Estimation is carried out using the EM algorithm. Inference is discussed and potential applications are outlined, in particula...

Compared to the proportional hazards model and accelerated failure time model, the accelerated hazards model has a unique property in its application, in that it can allow gradual effects of the treatment. However, its application is still very limited, partly due to the complexity of existing semiparametric estimation methods. We propose a new semiparametric estimation method based on the indu...

Survival analysis is concerned with nding models to predict the survival of patients or to assess the eecacy of a clinical treatment. A key part of the model-building process is the selection of the predictor variables. It is standard to use a stepwise procedure guided by a series of signiicance tests to select a single model, and then to make inference conditionally on the selected model. Howe...

We propose a new parameterisation for standard survival models which allow for the effect of a covariate with extreme value observations. We show that the robust model can offer an improved fit to both the parametric exponential model and the Cox proportional hazards model. Furthermore, we demonstrate the application of this approach using data from a pancreatic cancer dataset. Under traditiona...