Invited Commentary: Evidence-based Evaluation of p Values and Bayes Factors

摘要

Despite clear deficiencies of the p value as a summary of statistical evidence, compelling alternatives with strong theoretical justification, such as the Bayes factor and the related likelihood ratio, are rarely presented in epidemi-ologic publications. Comparison of the historical performance of the p value with that of its competitors in the epidemiologic literature may help epidemiologists evaluate whether Bayes factors or likelihood ratios lead to conclusions more quickly and reliably than a p value, given the same data. Empirical evidence presented by loannidis (Am J Epidemiol 2008; 168:374-83) demonstrates that findings with p values near 0.05 tend not to be confirmed in future studies. Similarly, Bayes factors interpret p values near 0.05 as having, at best, promising evidence against the null hypothesis. However, the different types of Bayes factors require empirical evaluation of their performance in practice. P values remain popular because miniscule p values are unlikely to mislead and p values do not require alternative hypotheses. Publishing p values near 0.05 could be considered a low-threshold screen to allow many (possibly null) results to be published for follow-up consideration. Meta-analyses and studies meant to decisively convince skeptics require a stronger standard (p values much below 0.05) and a Bayes factor to interpret the p value and to facilitate incorporation of background expertise necessary for drawing comprehensive conclusions#