Methods for Assessing the Credibility of Clinical Trial Outcomes
Credibility—the believability of new findings in the light of current knowledge—is a key issue in the assessment of clinical trial outcomes. Yet, despite the growth of evidence-based medicine, credibility is usually dealt with in a broad-brush and qualitative fashion. This paper describes how Bayesian methods lead to quantitative credibility assessments that take explicit account of prior insights and experience. A simple technique based on the concept of the critical prior interval (CPI) is presented, which allows rapid credibility assessment of trial outcomes reported in the standard format of odds ratios and 95% confidence intervals. The critical prior interval is easily determined via a graph, and provides clinicians with an explicit and objective baseline on which to base their assessment of credibility. The use of the critical prior interval is demonstrated through several working examples.
Key WordsCredibility Critical prior interval Bayesian methods
Unable to display preview. Download preview PDF.
- 2.Freedman D, Pisani R, Purves R, Statistics. (3rd Ed) New York, NY: Norton; 1998: Chapter 29.Google Scholar
- 3.O’Hagan A. Kendall’s Advanced Theory of Statistics. Vol 2B: Bayesian Inference. London: Arnold; 1994.Google Scholar
- 5.Spiegelhalter DJ, Myles JP, Jones DR, Abrams KR. An introduction to Bayesian methods in health technology assessment. Br Med J. 319:508-512Google Scholar
- 6.Matthews RAJ. Facts versus Factions: the use and abuse of subjectivity in scientific research. Cambridge: European Science and Environment Forum; 1998. Reprinted in Rethinking Risk and the Precautionary Principle. Morris, J, Ed. Oxford: Butter-worth; 2000: 247-282. Available online at: http://ourworld.compuserve.com/homepages/rajm/openesef. htm.Google Scholar
- 12.Roberto Latini R, et al. Clinical effects of early angiotensin-converting enzyme inhibitor treatment for acute myocardial infarction are similar in the presence and absence of aspirin: Systematic over-view of individual data from 96,712 randomized patients. J Am Coll Cardio. 2000;35:1801–1807.CrossRefGoogle Scholar
- 14.Lee PM. Bayesian Statistics: An Introduction. 2nd Ed. London: Arnold; 1997.Google Scholar
- 15.Good U. Probability and the Weighing of Evidence. London: Griffin; 1950.Google Scholar