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  • 标题:Continuous Ordinal Regression for Analysis of Visual Analogue Scales: The R Package ordinalCont
  • 本地全文:下载
  • 作者:Maurizio Manuguerra ; Gillian Z. Heller ; Jun Ma
  • 期刊名称:Journal of Statistical Software
  • 印刷版ISSN:1548-7660
  • 电子版ISSN:1548-7660
  • 出版年度:2020
  • 卷号:96
  • 期号:1
  • 页码:1-25
  • DOI:10.18637/jss.v096.i08
  • 出版社:University of California, Los Angeles
  • 摘要: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.
  • 关键词:continuous ordinal regression;visual analogue scale;maximum penalized likelihood;splines;R.
  • 其他关键词:continuous ordinal regression;visual analogue scale;maximum penalized likelihood;splines;R
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