Analyzing multiple endpoints in clinical trials of pain treatments: IMMPACT recommendations. Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials

Pain. 2008 Oct 31;139(3):485-493. doi: 10.1016/j.pain.2008.06.025. Epub 2008 Aug 15.


The increasing complexity of randomized clinical trials and the practice of obtaining a wide variety of measurements from study participants have made the consideration of multiple endpoints a critically important issue in the design, analysis, and interpretation of clinical trials. Failure to consider important outcomes can limit the validity and utility of clinical trials; specifying multiple endpoints for the evaluation of treatment efficacy, however, can increase the rate of false positive conclusions about the efficacy of a treatment. We describe the use of multiple endpoints in the design, analysis, and interpretation of pain clinical trials, and review available strategies and methods for addressing multiplicity. To decrease the probability of a Type I error (i.e., the likelihood of obtaining statistically significant results by chance) in pain clinical trials, the use of gatekeeping procedures and other methods that correct for multiple analyses is recommended when a single primary endpoint does not adequately reflect the overall benefits of treatment. We emphasize the importance of specifying in advance the outcomes and clinical decision rule that will serve as the basis for determining that a treatment is efficacious and the methods that will be used to control the overall Type I error rate.

Publication types

  • Consensus Development Conference
  • Guideline
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Review

MeSH terms

  • Clinical Trials as Topic / statistics & numerical data*
  • Confounding Factors, Epidemiologic
  • Endpoint Determination / statistics & numerical data*
  • Humans
  • Least-Squares Analysis
  • Multivariate Analysis
  • Pain Management*
  • Probability Theory
  • Research Design / statistics & numerical data