I’ve been trying to understand the Bayesian approach to multiple comparison adjustments, and I’ve made a mess of it.
I wanted to please ask for your perception of this. For example, I have recently been reading the results of the trial KEYNOTE-181, published in abstract form at ASCO 2019.
This study had three coprimary endpoints. According to the authors, the P-value required for each contrast was P<0.0075 for ITT, P<0.0075 for the subgroup with squamous tumors, and P<0.0084 for tumors with elevated biomarker level (CPS>10). According to this approach, the study is considered negative for ITT, with P=0.056, also negative for squamous with P=0.0095, and positive for the selected population with biomarker CPS >10 with P=0.0074. This is complicated to interpret, given that there are non-squamous tumors in which the effect seems minor, but which in turn have a high biomarker. Therefore, a Bayesian perspective might help to understand the results.
However, I don’t know how to approach the issue of triple comparison from the Bayesian approach. Please any insights that a clinician like me could understand?