Clinical trials and statistical verdicts: probable grounds for appeal.
Conventional interpretation of clinical trials relies heavily on the classic p value. The p value, however, represents only a false-positive rate, and does not tell the probability that the investigator's hypothesis is correct, given his observations. This more relevant posterior probability can be quantified by an extension of Bayes' theorem to the analysis of statistical tests, in a manner similar to that already widely used for diagnostic tests. Reanalysis of several published clinical trials according to Bayes' theorem shows several important limitations of classic statistical analysis. Classic analysis is most misleading when the hypothesis in question is already unlikely to be true, when the baseline event rate is low, or when the observed differences are small. In such cases, false-positive and false-negative conclusions occur frequently, even when the study is large, when interpretation is based solely on the p value. These errors can be minimized if revised policies for analysis and reporting of clinical trials are adopted that overcome the known limitations of classic statistical theory with applicable bayesian conventions.
D001241:Aspirin / Q000627:therapeutic use; D002986:Clinical Trials as Topic; D003327:Coronary Disease / Q000209:etiology; D003920:Diabetes Mellitus / Q000209:etiology; D006801:Humans; D007603:Jurisprudence; D008175:Lung Neoplasms / Q000209:etiology / Q000401:mortality; D009203:Myocardial Infarction / Q000517:prevention & control; D011336:Probability; D012306:Risk; D012494:Sampling Studies; D013223:Statistics as Topic; D014028:Tobacco Smoke Pollution
D014028:Tobacco Smoke Pollution; D001241:Aspirin