A chi-square goodness-of-fit test for autoregressive logistic regression models with applications to patient screening.
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.
a Department of Statistics , University of California , Riverside , California , USA.
Anne M
AM
Hansen
Daniel
D
Jeske
Wolff
W
Kirsch
D000544:Alzheimer Disease / Q000175:diagnosis; D016009:Chi-Square Distribution; D003198:Computer Simulation; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D008279:Magnetic Resonance Imaging; D015233:Models, Statistical; D018579:Patient Selection; D011237:Predictive Value of Tests; D018401:Sample Size; D012720:Severity of Illness Index; D013997:Time Factors
Autoregressive logistic regression; Bootstrap; Chi-square; Goodness-of-fit; Pearson; Simulation study