Continuous Ordinal Regression for Analysis of Visual Analogue Scales: The R Package ordinalCont

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
M. Manuguerra, G. Heller, Jun Ma
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引用次数: 17

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.
视觉模拟尺度分析的连续序数回归:R包序数控制
本文介绍了R包ordinalCont,它实现了一个对记录在视觉模拟量表(VAS)上的响应变量进行有序回归的框架。该量表用于记录受试者对疼痛、焦虑或生活质量等无形量的感知,并由线性量表上的标记组成。我们为VAS实现连续有序回归模型,作为分析此类响应的适当方法,并在线性预测器中引入平滑项和随机效应。利用惩罚似然的约束优化来估计模型参数,并通过边际似然的最大化来自动选择惩罚参数。仿真研究表明,该估计算法具有良好的效果。给出了两个应用实例:第一个涉及分析慢性颈部疼痛的激光治疗临床试验的疼痛结果;第二个是对乳腺癌化疗临床试验的生活质量结果的分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Statistical Software
Journal of Statistical Software 工程技术-计算机:跨学科应用
CiteScore
10.70
自引率
1.70%
发文量
40
审稿时长
6-12 weeks
期刊介绍: The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.
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