chngpt: threshold regression model estimation and inference.

Published

Journal Article

BACKGROUND:Threshold regression models are a diverse set of non-regular regression models that all depend on change points or thresholds. They provide a simple but elegant and interpretable way to model certain kinds of nonlinear relationships between the outcome and a predictor. RESULTS:The R package chngpt provides both estimation and hypothesis testing functionalities for four common variants of threshold regression models. All allow for adjustment of additional covariates not subjected to thresholding. We demonstrate the consistency of the estimating procedures and the type 1 error rates of the testing procedures by Monte Carlo studies, and illustrate their practical uses using an example from the study of immune response biomarkers in the context of Mother-To-Child-Transmission of HIV-1 viruses. CONCLUSION:chngpt makes several unique contributions to the software for threshold regression models and will make these models more accessible to practitioners interested in modeling threshold effects.

Full Text

Duke Authors

Cited Authors

  • Fong, Y; Huang, Y; Gilbert, PB; Permar, SR

Published Date

  • October 16, 2017

Published In

Volume / Issue

  • 18 / 1

Start / End Page

  • 454 -

PubMed ID

  • 29037149

Pubmed Central ID

  • 29037149

Electronic International Standard Serial Number (EISSN)

  • 1471-2105

International Standard Serial Number (ISSN)

  • 1471-2105

Digital Object Identifier (DOI)

  • 10.1186/s12859-017-1863-x

Language

  • eng