Hi all, I an in search of resources/guidelines for the principled analysis of a four arm clinical trial where the treatments are expected to interact. The four arms are: drug A, drug B, drug A+B, placebo. It is thought that drug A will potentiate B - thus A+B is anticipated to be the most effective treatment.
In my search so far one recommended strategy to analyze such a trial is to perform a series of NHSTs comparing:
- A, B and AB vs placebo;
- A+B vs max(A,B,placebo);
- treatment A vs B
At each step, a Bonferroni p value correction is undertaken to control the overall family-wise error rate (up to alpha / 6).
I am not entirely satisfied with this approach. Rather than measure the evidence against the null hypothesis of zero treatment effect, I would much prefer to estimate a range of treatment effects compatible with the data and interpret these clinically. Applying a Bonferroni p value correction does not help me if I am not aiming to perform NHST. Nor does it have any impact on the estimated treatment effects and compatibility intervals - and these are what I will interpret. Yet I do recognize that some form of multiplicity correction is required - but I hope that there may be a more efficient alternative. Logically specifying skeptical priors may be such an alternative - but I am uncertain if this is recommended practise.
If anyone would be able to provide me any suggestions or resources for such a situation, and any potential alternative strategies, I would be very grateful.