Frequency of Visiting a Doctor: A right Truncated Count Regression Model with Excess Zeros

Seyed Ehsan Saffari, John Carson Allen, Robiah Adnan, Seng Huat Ong, Shin Zhu Sim and William Greene
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Seyed Ehsan Saffari: Centre for Quantitative Medicine, Duke NUS Medical School, Singapore
Seng Huat Ong: Department of Mathematical Sciences, Universiti Teknologi Malaysia, Malaysia
Shin Zhu Sim: Institute of Mathematical Sciences, University of Malaya, Malaysia
William Greene: Department of Economics, New York University, United States of America

Biostatistics and Biometrics Open Access Journal, 2019, vol. 9, issue 5, 112-122

Abstract: Count response variables are frequently encountered in medical data, which calls for the use of count regression models. In this study, we introduce the hurdle Conway-Maxwell Poisson (HCMP) regression model where the outcome variable is the number of doctor visits, complicated by excess zeros and over-dispersion from troublesome extreme values. A truncation approach is proposed to handle extreme values, leading to the definition of a truncated HCMP (THCMP) model. Parameter estimates are derived using maximum likelihood. Results of a case study on a RWM dataset investigated effects of response truncation at 6.65, 3.08 and 1.75% for the THCMP and truncated hurdle Poisson (THP) models. In a simulation study, responses were generated from a mixture of HCMP (50%) and HP (50%) probability models. THCMP and THP model performance was compared with respect to parameter estimation bias, goodness-of-fit and outcome estimates for truncation levels of 5 and 10%. As measured by AIC, the THCMP model exhibited better goodness-of-fit at all truncation levels compared to the THP model. Estimation bias increased with higher truncation levels for both models, but to a lesser degree for the THCMP model.

Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:9:y:2019:i:5:p:112-122

DOI: 10.19080/BBOAJ.2019.09.555773

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