A Semiparametric Inverse-Gaussian Model and Inference for Survival Data with a Cured Proportion

• Activity Number: 520
• Type: Topic Contributed
• Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Risk Analysis
• Author(s): Sangbum Choi* and Xuelin Huang and Janice Cormier and Kjell Doksum
• Companies: The University of Texas at Houston and MD Anderson Cancer Center and MD Anderson Cancer Center and University of Wisconsin - Madison
• Keywords: Degradation model ; nonparametric likelihood ; threshold regression ; transformation model ; Wiener process

Abstract

In clinical and epidemiological studies, a Wiener process with drift may represent a patient's health status and a clinical endpoint occurs when the process first reaches an adverse threshold state. The first-hitting-time of the health process 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. We develop an asymptotically efficient likelihood-based estimation and inference procedure and derive the large-sample properties of the estimators. Simulation studies demonstrate that the proposed method performs well in finite samples. A case study of stage-III soft tissue sarcoma data is used as an illustration.