Clinical significance not statistical significance : a simple Bayesian alternative to p values.
To take the common « Bayesian » interpretation of conventional confidence intervals to its logical conclusion, and hence to derive a simple, intuitive way to interpret the results of public health and clinical studies.
Design and setting-The theoretical basis and practicalities of the approach advocated is at first explained and then its use is illustrated by referring to the interpretation of a real historical cohort study.
The study considered compared survival on haemodialysis (HD) with that on continuous ambulatory peritoneal dialysis (CAPD) in 389 patients dialysed for end stage renal disease in Leicestershire between 1974 and 1985.
Careful interpretation of the study was essential.
This was because although it had relatively low statistical power, it represented all of the data that were available at the time and it had to inform a critical clinical policy decision : whether or not to continue putting the majority of new patients onto CAPD.
Measurements and analysis-Conventional confidence intervals are often interpreted using subjective probability.
For example, 95% confidence intervals are commonly understood to represent a range of values within which one may be 95% certain that the true value of whatever one is estimating really lies.
Such an interpretation is fundamentally incorrect within the framework of conventional, frequency-based, statistics. (...)
Mots-clés Pascal : Analyse statistique, Probabilité, Intervalle confiance, Epidémiologie, Méthodologie
Mots-clés Pascal anglais : Statistical analysis, Probability, Confidence interval, Epidemiology, Methodology
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 98-0271880
Code Inist : 002B30A01A1. Création : 27/11/1998.