Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Maurizio Manuguerra, Gillian Z. Heller, Jun Ma
Title: Continuous Ordinal Regression for Analysis of Visual Analogue Scales: The R Package ordinalCont
Abstract: This paper introduces the R package ordinalCont, which implements an ordinal regression framework for response variables which are recorded on a visual analogue scale (VAS). This scale is used when recording subjects' perception of an intangible quantity such as pain, anxiety or quality of life, and consists of a mark made on a linear scale. We implement continuous ordinal regression models for VAS as the appropriate method of analysis for such responses, and introduce smoothing terms and random effects in the linear predictor. The model parameters are estimated using constrained optimization of the penalized likelihood and the penalty parameters are automatically selected via maximization of their marginal likelihood. The estimation algorithm is shown to perform well, in a simulation study. Two examples of application are given: the first involves the analysis of pain outcomes in a clinical trial for laser treatment for chronic neck pain; the second is an analysis of quality of life outcomes in a clinical trial for chemotherapy for the treatment of breast cancer.

Page views:: 929. Submitted: 2017-11-30. Published: 2020-12-05.
Paper: Continuous Ordinal Regression for Analysis of Visual Analogue Scales: The R Package ordinalCont     Download PDF (Downloads: 300)
ordinalCont_2.0.2.tar.gz: R source package Download (Downloads: 18; 61KB)
v96i08.R: R replication code Download (Downloads: 19; 6KB)
v96i08-simulation.R: R simulation code Download (Downloads: 17; 13KB)
sim_res_1000_seed_3.rda: Supplementary data (R binary format) Download (Downloads: 11; 59MB)

DOI: 10.18637/jss.v096.i08

This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.