A Semiparametric Inverse Gaussian Model and Inference for Survival Data
This work focuses on a semiparametric analysis of survival and cure rate modeling approach based on a latent failure process. In clinical and epidemiological studies, a Wiener process with drift may represent a patient’s health status and a clinical end point occurs when the process first reaches an adverse threshold state. The first hitting time then follows an inverse Gaussian distribution. On the basis of the improper inverse Gaussian distribution, we consider a process-based lifetime model that allows for a positive probability of no event taking place in finite time. Model flexibility is achieved by leaving a transformed time measure for disease progression completely unspecified, and regression structures are incorporated into the model by taking the acceleration factor and the threshold parameter as functions of the covariates. When applied to experiments with a cure fraction, this model is compatible with classical two-mixture or promotion time cure rate models. A case study of stage III soft tissue sarcoma data is used as an illustration.
- Aalen, O., Gjessing, H., & Borgan, Ø. (2008). Survival and event history analysis: A process point of view. Springer: New York.Google Scholar