We propose a new method for predicting censored (and non-censored) clinical outcomes from a highly-complex covariate space. Previously we suggested a unified strategy for predictor construction, selection, and performance assessment. Here we introduce a new algorithm which generates a piecewise constant estimation sieve of candidate predictors based on an intensive and comprehensive search over the entire covariate space. This algorithm allows us to elucidate interactions and correlation patterns in addition to main effects.
Statistical Methodology | Statistical Theory | Survival Analysis
Molinaro, Annette and van der Laan, Mark J., "Deletion/Substitution/Addition Algorithm for Partitioning the Covariate Space in Prediction" (November 2004). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 162.