A chi-square goodness-of-fit test for autoregressive logistic regression models with applications to patient screening

Anne M Hansen; Daniel Jeske; Wolff Kirsch

doi:10.1080/10543406.2014.919938

A chi-square goodness-of-fit test for autoregressive logistic regression models with applications to patient screening

J Biopharm Stat. 2015;25(1):89-108. doi: 10.1080/10543406.2014.919938.

Authors

Anne M Hansen¹, Daniel Jeske, Wolff Kirsch

Affiliation

¹ a Department of Statistics , University of California , Riverside , California , USA.

PMID: 24854578
DOI: 10.1080/10543406.2014.919938

Abstract

We propose a chi-square goodness-of-fit test for autoregressive logistic regression models. General guidelines for a two-dimensional binning strategy are provided, which make use of two types of maximum likelihood parameter estimates. For smaller sample sizes, a bootstrap p-value procedure is discussed. Simulation studies indicate that the test procedure satisfactorily approximates the correct size and has good power for detecting model misspecification. In particular, the test is very good at detecting the need for an additional lag. An application to a dataset relating to screening patients for late-onset Alzheimer's disease is provided.

Keywords: Autoregressive logistic regression; Bootstrap; Chi-square; Goodness-of-fit; Pearson; Simulation study.

MeSH terms

Alzheimer Disease / diagnosis
Chi-Square Distribution
Computer Simulation
Humans
Likelihood Functions
Logistic Models
Magnetic Resonance Imaging
Models, Statistical*
Patient Selection*
Predictive Value of Tests
Sample Size*
Severity of Illness Index
Time Factors