Free Statistical software: Shakespeare's Confidence Calculator v1.0
The following free statistical software consists of an Excel spreadsheet, and is downloadable free of charge. Instructions for and conditions of use are described on the "INSTRUCTIONS" sheet inside. The spreadsheet calulates confidence levels, confidence intervals, clinical significance curves and risk-benefit contours.
Conditions of use: you must agree to the following before you proceed!
By using this free ststistical software you agree to reference the use of Shakespeare's confidence levels, clinical significance curves, and risk-benefit contours in any manuscript submitted for publication, by referencing our two original publications which describes these decision-making tools:
Shakespeare TP, Gebski VJ, Veness MJ, Simes J. Improving the interpretation of clinical studies by use of confidence levels, clinical significance curves, and risk-benefit contours. Lancet 2001; 357: 1349-1353.
Shakespeare TP, Gebski V, Tang J, Lim K, Lu JJ, Zhang X, Jiang G. Influence of the way results are presented on research interpretation and medical decision making: the PRIMER collaboration randomized studies. Med Decis Making. 2008; 28(1): 127-37.
You also agree that the authors do not guarantee the accuracy of the numbers or figures obtained from this calculator and that you will not hold us liable for any losses (monetary, psychological, professional or otherwise) or grievances arising from the use of this calculator. All data that you obtain should be checked by an appropriately qualified statistician with reference to our article above. For further information and provision of feedback, please contact Dr. Shakespeare. This free statistical software is for personal use only and is not to be sold, lent, distributed, copied or modified in any way.
Whether doctor or patient, deciding on what constitutes the best treatment for a particular disease is often based on the published medical literature. How medical studies are reported, and the methods used to interpret results, all play vital roles in medical decision-making.
In the 1990's, the medical literature was flooded with editorials and articles claiming the superiority of 95% confidence intervals over p values when reporting research results. However this conjecture was based entirely on personal opinion, the lowest level of evidence available. The lack of evidence has been noted by others (eg: S.D. Walter, Methods of reporting statistical results from medical research studies, Am J Epidemiol 141 (1995) 896-906).
In the spirit of exisiting low level evidence, we have our own opinion. One way that we believe results in improving the reporting of results, leading to less chance of misinterpretation and more informed decisions, is by use of confidence levels, clinical significance curves, and confidence contours. These methods have been published previously, and we cite examples where results that have been misinterpreted by both eminent authors (eg WHO collaborators) and journal readers, might have been correctly interpreted using our methods:
Shakespeare TP, Gebski VJ, Veness MJ, Simes J. Improving interpretation of clinical studies by use of confidence levels, clinical significance curves, and risk-benefit contours. Lancet. 2001: 357: 1349-53.
In brief, our methods allow the reader of a study (whether doctor or patient), to know the confidence (or probability) that one treatment is better than another, and by how much. They also can show the confidence that a treatment does not have excessive side-effects. To find out more please read the Lancet article above. A discussion of the problems with confidence intervals, and the benefits of confidence levels can also be found my Statistics Supercourse lecture hosted by the University of Pittsburgh. A discussion of confidence intervals was also presented at ASTRO (American Society for Therapeutic Radiology and Oncology) 2001: the talk's powerpoint slides are available here. An additional discussion of the use of confidence levels in patient decision-making and decision aids can be found in this powerpoint presentation, which I gave as an invited speaker at ASTRO 2003
Not satisfied with the available level of evidence, we (The PRIMER Collaboration) conducted two randomized studies to determine the best methods of reporting research results: p values, 95% confidence intervals, confidence levels, or a combination. Results showed that confidence levels are superior to p values and 95% confidence intervals. We recommend that all researchers use confidence levels when publishing their findings. The abstract is available here. The work is published as follows: Shakespeare et al. Med Decis Making. 2008 Jan-Feb;28(1):127-37.
A/Prof. Thomas Philip Shakespeare