Improving Interpretation of Clinical Studies by Use of Confidence Levels, Clinical Significance Curves, and Risk-Benefit Contours

Lancet. 2001 Apr 28;357(9265):1349-53. doi: 10.1016/S0140-6736(00)04522-0.

Abstract

The process of interpreting the results of clinical studies and translating them into clinical practice is being debated. Here we examine the role of p values and confidence intervals in clinical decision-making, and draw attention to confusion in their interpretation. To improve result reporting, we propose the use of confidence levels and plotting of clinical significance curves and risk-benefit contours. These curves and contours provide degrees of probability of both the potential benefit of treatment and the detriment due to toxicity. Additionally, they provide clinicians with a mechanism of translating the results of studies into treatment for individual patients, thus improving the clinical decision-making process. We illustrate the application of these curves and contours by reference to published studies. Confidence levels, clinical significance curves, and risk-benefit contours can be easily calculated with a hand calculator or standard statistical packages. We advocate their incorporation into the published results of clinical studies.

Publication types

  • Review

MeSH terms

  • Clinical Trials as Topic / statistics & numerical data*
  • Confidence Intervals*
  • Data Interpretation, Statistical*
  • Decision Making
  • Humans
  • Neoplasms / therapy
  • Risk Assessment