A Bayesian mixture model for changepoint estimation using ordinal predictors

Roberts Emily () and Zhao Lili ()
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Roberts Emily: Department of Biostatistics, University of Michigan, 1415 Washington Heights, 48109 Ann Arbor, MI, USA
Zhao Lili: Department of Biostatistics, University of Michigan, 1415 Washington Heights, 48109 Ann Arbor, MI, USA

The International Journal of Biostatistics, 2022, vol. 18, issue 1, 57-72

Abstract: In regression models, predictor variables with inherent ordering, such ECOG performance status or novel biomarker expression levels, are commonly seen in medical settings. Statistically, it may be difficult to determine the functional form of an ordinal predictor variable. Often, such a variable is dichotomized based on whether it is above or below a certain cutoff. Other methods conveniently treat the ordinal predictor as a continuous variable and assume a linear relationship with the outcome. However, arbitrarily choosing a method may lead to inaccurate inference and treatment. In this paper, we propose a Bayesian mixture model to consider both dichotomous and linear forms for the variable. This allows for simultaneous assessment of the appropriate form of the predictor in regression models by considering the presence of a changepoint through the lens of a threshold detection problem. This method is applicable to continuous, binary, and survival outcomes, and it is easily amenable to penalized regression. We evaluated the proposed method using simulation studies and apply it to two real datasets. We provide JAGS code for easy implementation.

Keywords: Bayesian methods; changepoints; mixture model; ordinal predictors; regression model (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/ijb-2020-0151

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