Survival Model Selection with Missing Data and Correlated Covariates

Host

Data Science

Speaker

Sydeaka Watson
Research Associate (Assistant Professor), Department of Public Health Sciences, University of Chicago
http://health.bsd.uchicago.edu/People/Watson-Sydeaka

Description

A novel combination of existing methods was used to develop a survival prediction equation for pulmonary arterial hypertension patients awaiting lung transplantation. The Scientific Registry of Transplant Recipients (SRTR) dataset featured censored survival times, missing covariate data, and a large number of highly correlated candidate predictor variables. Penalized weighted least squares regression was repeatedly applied to bootstrap resamples of multiply imputed data, yielding a parsimonious model that satisfied internal validation criteria of clinical interest. Simulation studies under various degrees of predictor variable missing-ness, survival time censoring, effect size, and proportion of variables unrelated to survival have shown that this method tends to accurately recover the true list of Cox regression predictor variables.