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Activity Number: 622
Type: Invited
Date/Time: Thursday, August 7, 2014 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract #310737 View Presentation
Title: Robust Survival Prediction via Linear Transformation Models
Author(s): David Harrington*+ and Keith Betts
Companies: Harvard and Analysis Group
Keywords: censored data ; linear transformation ; prediction
Abstract:

For censored time to event data, it is important to develop flexible regression models that can be used to accurately predict future risk. In this talk, we examine robust prediction models for event time outcomes by generalizing Cai's estimating equation approach for the linear transformation model, which includes the proportional odds and proportional hazards model. We demonstrate that under mild regularity conditions, the solution of the estimating equations possess a stability property which allows for valid predictive inference under possible model misspecification. The proposed procedures are applied to a multiple myeloma dataset to derive a flexible regression model for predicting patient survival based on traditional clinical factors with and without the addition of genetic information. The finite sample properties of the procedures are evaluated through a simulation study.


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