Parsimonious covariate selection for a multicategory ordered response

We propose a flexible continuation ratio (CR) model for an ordinal categorical response with potentially ultrahigh dimensional data that characterizes the unique covariate effects at each response level. The CR model is the logit of the conditional discrete hazard function for each response level gi...

Full description

Saved in:
Bibliographic Details
Published in:Statistical methods in medical research 2017-12, Vol.26 (6), p.2743-2757
Main Authors: Hsu, Wan-Hsiang, DiRienzo, A Gregory
Format: Article
Language:English
Subjects:
Computer simulation
Distribution functions
Economic models
Errors
Goodness of fit
Leukemia
Mathematical models
Numerical methods
Regression coefficients
Robustness (mathematics)
Simulation
Specification
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We propose a flexible continuation ratio (CR) model for an ordinal categorical response with potentially ultrahigh dimensional data that characterizes the unique covariate effects at each response level. The CR model is the logit of the conditional discrete hazard function for each response level given covariates. We propose two modeling strategies, one that keeps the same covariate set for each hazard function but allows regression coefficients to arbitrarily change with response level, and one that allows both the set of covariates and their regression coefficients to arbitrarily change with response. Evaluating a covariate set is accomplished by using the nonparametric bootstrap to estimate prediction error and their robust standard errors that do not rely on proper model specification. To help with interpretation of the selected covariate set, we flexibly estimate the conditional cumulative distribution function given the covariates using the separate hazard function models. The goodness-of-fit of our flexible CR model is assessed with graphical and numerical methods based on the cumulative sum of residuals. Simulation results indicate the methods perform well in finite samples. An application to B-cell acute lymphocytic leukemia data is provided.
ISSN:0962-2802
1477-0334
DOI:10.1177/0962280215608120