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A Bayesian mixture model for changepoint estimation using ordinal predictors
Journal article   Open access   Peer reviewed

A Bayesian mixture model for changepoint estimation using ordinal predictors

Emily Roberts and Lili Zhao
The international journal of biostatistics, Vol.18(1), pp.57-72
04/06/2021
DOI: 10.1515/ijb-2020-0151
PMCID: PMC9156335
PMID: 33823087
url
https://doi.org/10.1515/ijb-2020-0151View
Published (Version of record) Open Access

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.
 Bayesian methods changepoints mixture model ordinal predictors regression model

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