Measurement error models with zero inflation and multiple sources of zeros, with applications to hard zeros

Anindya Bhadra (), Rubin Wei (), Ruth Keogh (), Victor Kipnis (), Douglas Midthune (), Dennis W. Buckman (), Ya Su (), Ananya Roy Chowdhury () and Raymond J. Carroll ()
Additional contact information
Anindya Bhadra: Purdue University
Rubin Wei: Eli Lilly and Company
Ruth Keogh: Department of Medical Statistics, London School of Hygiene and Tropical Medicine
Victor Kipnis: National Cancer Institute
Douglas Midthune: National Cancer Institute
Dennis W. Buckman: Information Management Services, Inc.
Ya Su: Virginia Commonwealth University
Ananya Roy Chowdhury: Texas A&M University, College Station
Raymond J. Carroll: University of Technology Sydney

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2024, vol. 30, issue 3, No 5, 600-623

Abstract: Abstract We consider measurement error models for two variables observed repeatedly and subject to measurement error. One variable is continuous, while the other variable is a mixture of continuous and zero measurements. This second variable has two sources of zeros. The first source is episodic zeros, wherein some of the measurements for an individual may be zero and others positive. The second source is hard zeros, i.e., some individuals will always report zero. An example is the consumption of alcohol from alcoholic beverages: some individuals consume alcoholic beverages episodically, while others never consume alcoholic beverages. However, with a small number of repeat measurements from individuals, it is not possible to determine those who are episodic zeros and those who are hard zeros. We develop a new measurement error model for this problem, and use Bayesian methods to fit it. Simulations and data analyses are used to illustrate our methods. Extensions to parametric models and survival analysis are discussed briefly.

Keywords: Bayesian methods; Hard zeroes; Latent variables; Measurement error; Mixed models; Nutritional epidemiology; Nutritional surveillance; Zero-inflated data (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10985-024-09627-w

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