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

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