  Statistical methodology plays a crucial role in drug regulation. Decisions by
the FDA or EMA are typically made based on multiple primary studies testing the
same medical product, where the two-trials rule is the standard requirement,
despite a number of shortcomings. A new approach is proposed for this task
based on the (weighted) harmonic mean of the squared study-specific test
statistics. Appropriate scaling ensures that, for any number of independent
studies, the null distribution is a $\chi^2$-distribution with one degree of
freedom. This gives rise to a new method for combining one-sided $p$-values and
calculating confidence intervals for the overall treatment effect. Further
properties are discussed and a comparison with the two-trials rule is made, as
well as with alternative research synthesis methods. An attractive feature of
the new approach is that a claim of success requires each study to be
convincing on its own to a certain degree depending on the overall significance
level and the number of studies. A real example with 5 clinical trials
investigating the effect of Carvedilol for the treatment of patients with
moderate to severe heart failure patients is used to illustrate the
methodology.
