Survival ensembles by the sum of pairwise differences with application to lung cancer microarray studies

• Type of Material: Article
• Authors: Brent Johnson, Emory University; Qi Long, Emory University
• Language: English
• Date: 2011-06-01
• Publisher: Institute of Mathematical Statistics (IMS)
• Publication Version: Final Published Version
• Copyright Statement: © Institute of Mathematical Statistics, 2011
• Final Published Version (URL): http://dx.doi.org/10.1214%2F10-AOAS426
• Title of Journal or Parent Work: Annals of Applied Statistics
• ISSN: 1932-6157
• Volume: 5
• Issue: 2A
• Start Page: 1081
• End Page: 1101
• Grant/Funding Information: The research of the first author was supported in part by a grant from the National Institutes of Allergies and Infectious Diseases (R03AI068484) and Emory's Center for AIDS Research.
• Keywords: Accelerated failure time; Lasso; Survival analysis; Proportional hazards regression; Boosting
• Research Categories: Biology, Biostatistics

Abstract

Lung cancer is among the most common cancers in the United States, in terms of incidence and mortality. In 2009, it is estimated that more than 150,000 deaths will result from lung cancer alone. Genetic information is an extremely valuable data source in characterizing the personal nature of cancer. Over the past several years, investigators have conducted numerous association studies where intensive genetic data is collected on relatively few patients compared to the numbers of gene predictors, with one scientific goal being to identify genetic features associated with cancer recurrence or survival. In this note, we propose high-dimensional survival analysis through a new application of boosting, a powerful tool in machine learning. Our approach is based on an accelerated lifetime model and minimizing the sum of pairwise differences in residuals. We apply our method to a recent microarray study of lung adenocarcinoma and find that our ensemble is composed of 19 genes while a proportional hazards (PH) ensemble is composed of nine genes, a proper subset of the 19-gene panel. In one of our simulation scenarios, we demonstrate that PH boosting in a misspecified model tends to underfit and ignore moderately-sized covariate effects, on average. Diagnostic analyses suggest that the PH assumption is not satisfied in the microarray data and may explain, in part, the discrepancy in the sets of active coefficients. Our simulation studies and comparative data analyses demonstrate how statistical learning by PH models alone is insufficient.