Bayesian Threshold Regression Model for Current Status Data with Informative Censoring

• Activity Number: 664
• Type: Contributed
• Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Tao Xiao*+ and Michael Lindsey Pennell
• Companies: The Ohio State University and The Ohio State University College of Public Health
• Keywords: Bayesian ; current status data ; informative censoring ; survival analysis ; data augmentation ; threshold regression

Abstract

In some biomedical applications, there is interest in making inferences about a time to event distribution but the exact time of the event is unknown. For instance in animal carcinogenicity studies, tumors are not discovered until the time of examination and hence time to tumor is interval censored; this is known as current status data. Sometimes, the examination time is not independent of the event time; e.g., an exam may have occurred because the animal died to a cause related to tumor development. In this case, survival analysis methods assuming independent censoring would result in biased inferences. To address this issue, we propose a Bayesian approach which jointly models time to tumor and time to death using Wiener processes which fail once they hit a boundary value. Data augmentation approach is used to sample the unobserved time to tumor. To account for informative censoring, our model allows the drift of the death process to change following time to tumor. In addition to being a conceptually appealing model, our model does not require the assumption of proportional hazards of some standard methods. We demonstrate our method using time to lung tumor data from an NTP study.