Moving towards multiple site outcomes in spinal cord injury pain clinical trials: An issue of clustered observations in trial design and analysis

Academic Article


  • Introduction: Pain remains a problem for many with spinal cord injury (SCI), and there is a need for sound, randomized clinical trials examining the efficacy of existing and novel therapeutics. SCI-related pain is complex, as more than one type of pain is often experienced. The purpose of this report is to (i) demonstrate how to design and power calculation of a clinical trial of SCI pain using multiple pain sites per individual; (ii) discuss consequences of failing to adjust for this; and (iii) provide intraclass correlation (ICC) estimates for common pain outcome measures that may be used to power future clinical trials in SCI pain. Method: Using an existing dataset from a past SCI pain clinical trial, the ICC was calculated for common pain outcome measures to illustrate appropriate corrections for powering, analyzing and interpreting results from multiple pain sites per individual. The problem associated with not accounting for multiple pain sites per individual and the effect on the Type I error rate is also shown. Results and Discussion: Not accounting for the ICC can lead to (1) incorrect power estimates in the design of a trial, and (2) an inflated Type I error rate with a higher likelihood of misinterpretation of outcomes. Conclusions: Powering for future SCI pain trials and statistical analysis of trial outcomes may be substantially compromised if methods do not account for the intra-individual associations between pain sites, ultimately affecting study interpretations and evidence-based practice. We present ICC estimates based on SCI pain data for purposes of estimating power for future trials. © The Academy of Spinal Cord Injury Professionals, Inc. 2014.
  • Authors

    Published In

    Digital Object Identifier (doi)

    Author List

  • Richardson E; Redden DT
  • Start Page

  • 278
  • End Page

  • 287
  • Volume

  • 37
  • Issue

  • 3