We present a novel and easy-to-use method for calibrating error-rate based confidence intervals to evidence-based support intervals. Support intervals are obtained from inverting Bayes factors based on a parameter estimate and its standard error. A $k$ support interval can be interpreted as "the observed data are at least $k$ times more likely under the included parameter values than under a specified alternative". Support intervals depend on the specification of prior distributions for the parameter under the alternative, and we present several types that allow different forms of external knowledge to be encoded. We also show how prior specification can to some extent be avoided by considering a class of prior distributions and then computing so-called minimum support intervals which, for a given class of priors, have a one-to-one mapping with confidence intervals. We also illustrate how the sample size of a future study can be determined based on the concept of support. Finally, we show how the bound for the type I error rate of Bayes factors leads to a bound for the coverage of support intervals. An application to data from a clinical trial illustrates how support intervals can lead to inferences that are both intuitive and informative.

翻译：我们提出了一种新颖且易于使用的方法，用于将基于错误率的置信区间校准为基于证据的支持区间。支持区间通过基于参数估计及其标准误差的贝叶斯因子反转得到。一个$k$支持区间可解释为“在包含的参数值下观测数据的可能性至少是特定备择假设下的$k$倍”。支持区间依赖于备择假设下参数先验分布的指定，我们提出了几种允许编码不同形式外部知识的类型。我们还展示了如何通过考虑一类先验分布，然后计算所谓的最小支持区间来在一定程度上避免先验指定，该类区间对于给定的一类先验分布与置信区间具有一一对应关系。我们还说明了如何基于支持的概念确定未来研究的样本量。最后，我们展示了贝叶斯因子的第一类错误率上界如何导致支持区间覆盖概率的上界。一项临床试验数据的应用示例说明了支持区间如何导致既直观又信息丰富的推断。