chngpt: threshold regression model estimation and inference

BMC Bioinformatics

Software

Ying Huang¹  Peter B. Gilbert¹  Youyi Fong¹  Sallie R. Permar² 

[1] Department of Biostatistics, Bioinformatics and Epidemiology, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, USA, 1100 Fairview Ave N., Seattle, USA;Human Vaccine Institute, Duke University Medical Center, 2 Genome Court, Durham, USA;

关键词: Segmented regression model;    Regression kink;    Jump-type;    Change point;   

DOI  :  10.1186/s12859-017-1863-x

 received in 2017-01-10, accepted in 2017-10-09,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundThreshold 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.ResultsThe 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.Conclusionchngpt 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.

【 授权许可】

CC BY   
© The Author(s) 2017

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