Iterative partial least squares with right-censored data analysis: a comparison to other dimension reduction techniques.
In the linear model with right-censored responses and many potential explanatory variables, regression parameter estimates may be unstable or, when the covariates outnumber the uncensored observations, not estimable. We propose an iterative algorithm for partial least squares, based on the Buckley-James estimating equation, to estimate the covariate effect and predict the response for a future subject with a given set of covariates. We use a leave-two-out cross-validation method for empirically selecting the number of components in the partial least-squares fit that approximately minimizes the error in estimating the covariate effect of a future observation. Simulation studies compare the methods discussed here with other dimension reduction techniques. Data from the AIDS Clinical Trials Group protocol 333 are used to motivate the methodology.
Huang
Jie
J
Section of Clinical Science, Brigham and Women's Hospital, and Harvard Medical School, 221 Longwood Avenue, Boston, Massachusetts 02115, USA. jjhuang@post.harvard.edu
Harrington
David
D
D019380:Anti-HIV Agents / Q000008:administration & dosage* / Q000494:pharmacology; D001699:Biometry / Q000379:methods*; D018592:Cross-Over Studies; D024882:Drug Resistance, Viral; D015658:HIV Infections / Q000188:drug therapy; D016333:HIV Protease / Q000235:genetics*; D006801:Humans; D016014:Linear Models; D020125:Mutation, Missense; D011237:Predictive Value of Tests; D012367:RNA, Viral / Q000097:blood / Q000187:drug effects; D012044:Regression Analysis; D016896:Treatment Outcome
D019380:Anti-HIV Agents; D012367:RNA, Viral; D016333:HIV Protease