Estimating risk from underpowered, but statistically significant, studies: Was APPROVe on TARGET?

What is known and objective: The importance of statistical power is widely recognized from a pretrial perspective, and when interpreting results that are not statistically significant. It is less well recognized that poor power can lead to inflated estimates of the effect size when statistically significant results are observed. We use trial simulations to quantify this bias, which we term 'significant-result bias'