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Likert pain score modeling: a Markov integer model and an autoregressive continuous model.

E L Plan1, J-P Elshoff, A Stockis

  • 1Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. elodie.plan@farmbio.uu.se

Clinical Pharmacology and Therapeutics
|March 22, 2012
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Summary

This study developed statistical models for analyzing 11-point Likert scale pain scores in neuropathic pain patients. Both developed models effectively captured data features, aiding in drug effect detection.

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Area of Science:

  • Pharmacometrics
  • Biostatistics
  • Clinical Trial Analysis

Background:

  • Pain intensity is commonly measured using rating scales like the 11-point Likert scale.
  • Frequent pain assessments often exhibit serial correlation and underdispersion, posing analytical challenges.
  • Developing robust population models is crucial for accurate interpretation of pain data in clinical trials.

Purpose of the Study:

  • To develop and compare population models suitable for analyzing 11-point Likert scale pain data.
  • To assess the performance of an integer-based model versus a continuous model for pain intensity data.
  • To establish platform models for reliable drug effect detection in neuropathic pain studies.

Main Methods:

  • Utilized daily Likert pain scores from 231 neuropathic pain patients in a placebo group over 18 weeks.
  • Implemented an integer model using a truncated generalized Poisson distribution with Markovian transition probability inflation in NONMEM 7.1.0.
  • Compared the integer model against a logit-transformed autoregressive continuous model with correlated residual errors.

Main Results:

  • Both the integer and continuous models estimated a baseline pain score of 6.2.
  • A placebo effect of 19% was consistently estimated across both developed models.
  • The models successfully identified underlying data characteristics, validating their utility for drug effect analysis.

Conclusions:

  • Developed population models accurately represent features of 11-point Likert pain scale data.
  • The integer model offers flexibility at the cost of complexity, while the continuous model is simpler but requires longer computation.
  • Both models serve as valuable platforms for detecting drug effects in clinical trial settings.