A constrained regression model for an ordinal response with ordinal predictors

04/23/2018
by   Javier Espinosa, et al.
0

A regression model is proposed for the analysis of an ordinal response variable depending on a set of multiple covariates containing ordinal and potentially other variables. The proportional odds model (McCullagh (1980)) is used for the ordinal response, and constrained maximum likelihood estimation is used to account for the ordinality of covariates. Ordinal predictors are coded by dummy variables. The parameters associated to the categories of the ordinal predictor(s) are constrained, enforcing them to be monotonic (isotonic or antitonic). A decision rule is introduced for classifying the ordinal predictors' monotonicity directions, also providing information whether observations are compatible with both or no monotonicity direction. In addition, a monotonicity test for the parameters of any ordinal predictor is proposed. The monotonicity constrained model is proposed together with three estimation methods and compared to the unconstrained one based on simulations. The model is applied to real data explaining a 10-Points Likert scale quality of life self-assessment variable from ordinal and other predictors.

READ FULL TEXT
research
07/11/2021

Inference for the proportional odds cumulative logit model with monotonicity constraints for ordinal predictors and ordinal response

The proportional odds cumulative logit model (POCLM) is a standard regre...
research
07/18/2022

Analyzing Clustered Continuous Response Variables with Ordinal Regression Models

Continuous response variables often need to be transformed to meet regre...
research
03/31/2023

An interpretable neural network-based non-proportional odds model for ordinal regression with continuous response

This study proposes an interpretable neural network-based non-proportion...
research
02/03/2021

Statistical Inference for Ordinal Predictors in Generalized Linear and Additive Models with Application to Bronchopulmonary Dysplasia

Discrete but ordered covariates are quite common in applied statistics, ...
research
08/14/2021

A Bayesian group sequential schema for ordinal endpoints

The ordinal endpoint is prevalent in clinical studies. For example, for ...
research
12/18/2018

cgam: An R Package for the Constrained Generalized Additive Model

The cgam package contains routines to fit the generalized additive model...
research
06/23/2023

A primer on computational statistics for ordinal models with applications to survey data

The analysis of survey data is a frequently arising issue in clinical tr...

Please sign up or login with your details

Forgot password? Click here to reset