Temporal Prediction of Future State Occupation in a Multistate Model from High-Dimensional Baseline Covariates via Pseudo-Value Regression.

ABSTRACT: In many complex diseases such as cancer, a patient undergoes various disease stages before reaching a terminal state (say disease free or death). This fits a multistate model framework where a prognosis may be equivalent to predicting the state occupation at a future time t. With the advent of high throughput genomic and proteomic assays, a clinician may intent to use such high dimensional covariates in making better prediction of state occupation. In this article, we offer a practical solution to this problem by combining a useful technique, called pseudo value regression, with a latent factor or a penalized regression method such as the partial least squares (PLS) or the least absolute shrinkage and selection operator (LASSO), or their variants. We explore the predictive performan

SUBMITTER: Dutta S 

PROVIDER: S-EPMC5714309 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature