Improving interpretation of clinical studies by use of confidence levels, clinical significance curves, and risk-benefit contours.
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.
Shakespeare
T P
TP
Radiotherapy Centre, National University Hospital, Singapore, Singapore. ThomasS@nuh.com.sg
Gebski
V J
VJ
Veness
M J
MJ
Simes
J
J
D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D003657:Decision Making; D006801:Humans; D009369:Neoplasms / Q000628:therapy; D018570:Risk Assessment