Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance

Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance (1995)

Cached

Download Links

by Adrian Raftery , David Madigan , Chris T. Volinsky

Venue:	In Bayesian Statistics 5
Citations:	37 - 12 self

Versions

Version History

Metadata Version 3

User correction supplied by SystemCorrections

Datum	Value	Source
TITLE	Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance	user correction - Legacy Corrections
AUTHOR NAME	Adrian Raftery	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Statistics, GN-22; University of Washington	user correction - Legacy Corrections
AUTHOR ADDR	Seattle, WA 98195, USA.	user correction - Legacy Corrections
AUTHOR NAME	David Madigan	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Statistics, GN-22; University of Washington	user correction - Legacy Corrections
AUTHOR ADDR	Seattle, WA 98195, USA.	user correction - Legacy Corrections
AUTHOR NAME	Chris T. Volinsky	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Statistics, GN-22; University of Washington	user correction - Legacy Corrections
AUTHOR ADDR	Seattle, WA 98195, USA.	user correction - Legacy Corrections
ABSTRACT	Survival analysis is concerned with finding models to predict the survival of patients or to assess the efficacy 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 significance tests to select a single model, and then to make inference conditionally on the selected model. However, this ignores model uncertainty, which can be substantial. We review the standard Bayesian model averaging solution to this problem and extend it to survival analysis, introducing partial Bayes factors to do so for the Cox proportional hazards model. In two examples, taking account of model uncertainty enhances predictive performance, to an extent that could be clinically useful. 1 Introduction From 1974 to 1984 the Mayo Clinic conducted a double-blinded randomized clinical trial involving 312 patients to compare the drug DPCA with a placebo in the treatment of primary biliary cirrhosis...	user correction - Legacy Corrections
YEAR	1995	user correction - Legacy Corrections
VENUE	In Bayesian Statistics 5	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	323--349	INFERENCE
CITATIONS	60 found	ParsCit 1.0