A Censored Semicontinuous Regression for Modeling Clustered/Longitudinal Zero-Inflated Rates and Proportions: An Application to Colorectal Cancer

Leili Tapak, Omid Hamidi (), Payam Amini and Hassan Doosti ()
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Leili Tapak: Department of Biostatistics, School of Public Health and Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences
Omid Hamidi: Department of Science, Hamedan University of Technology
Payam Amini: School of Medicine, Keele University
Hassan Doosti: School of Mathematical and Physical Sciences, Macquarie University

A chapter in Flexible Nonparametric Curve Estimation, 2024, pp 153-174 from Springer

Abstract: Abstract Zero-inflated rate/proportions/ratio models are commonly used in biomedical data where the response variable takes its values over the interval (0,1). When proportion data include many zeroes in addition to the values in the interval and are correlated among study units, fitting a marginal model using generalized estimating equations (GEE) that can incorporate subject-to-subject correlations is a natural choice. In the present study a GEE based zero-inflated censored Beta (GEE.ZICBETA) model is proposed to fit clustered rate data with zeroes that allows for some proportions to be left-censored. The model combines elements of logistic regression for the Bernoulli success probability, the Beta distribution for the rate observations, and left censoring. A corresponding sandwich variance estimator as well as a clustered resampling (bootstrap)-based procedure are used to estimate the variance. Using a simulation study, the asymptotical properties of the estimators are shown. The resulting inference procedure is applied to investigate the association between several potential climatic risk factors and colorectal cancer rate in Iran. Several risk factors clinically relevant are identified using the proposed model.

Keywords: Generalized estimating equations (GEE); Colorectal neoplasm; Sunlight; Vitamin D; Beta regression; Bootstrap (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-66501-1_7

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