Methods for generalized change‐point models: with applications to human immunodeficiency virus surveillance and diabetes data

• Published in: Statistics in medicine 2020-04, Vol.39 (8), p.1167-1182
• Main Authors: Tapsoba, Jean de Dieu, Wang, Ching‐Yun, Zangeneh, Sahar, Chen, Ying Qing
• Format: Article
• Language: English
• Subjects: Body Mass Index; change‐point; correlated data; Diabetes; Diabetes Mellitus, Type 2 - epidemiology; generalized linear model; Generalized linear models; Health surveillance; HIV; HIV Infections - epidemiology; HIV surveillance; Human immunodeficiency virus; Humans; Linear Models; type 2 diabetes
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.8469

In many epidemiological and biomedical studies, the association between a response variable and some covariates of interest may change at one or several thresholds of the covariates. Change‐point models are suitable for investigating the relationship between the response and covariates in such situations. We present change‐point models, with at least one unknown change‐point occurring with respect to some covariates of a generalized linear model for independent or correlated data. We develop methods for the estimation of the model parameters and investigate their finite‐sample performances in simulations. We apply the proposed methods to examine the trends in the reported estimates of the annual percentage of new human immunodeficiency virus (HIV) diagnoses linked to HIV‐related medical care within 3 months after diagnosis using HIV surveillance data from the HIV prevention trial network 065 study. We also apply our methods to a dataset from the Pima Indian diabetes study to examine the effects of age and body mass index on the risk of being diagnosed with type 2 diabetes.