All entries for Wednesday 09 December 2015
December 09, 2015
A thing I've heard several times is that Bayesian methods might be advantageous for Phase 2 trials but not for Phase 3. I've struggled to understand why people would think that. To me, the advantage of Bayesian methods comes in the fact that the methods make sense, answer relevant questions and give understandable answers, which seem just as important in Phase 3 trials as in Phase 2.
One of my colleagues gave me his explanation, which I will paraphrase. He made two points:
1. Decision-making processes are different after Phase 2 and Phase 3 trials; folowing Phase 2 decisions about whether to proceed further are made by researchers or research funders, but after Phase 3 decisons (about use of therapies presumably) are taken by "society" in the form of regulators or healthcare providers. This makes the Bayesian approach harder as it is harder to formulate a sensible prior (for Phase 3 I think he means).
2. In Phase 3 trials sample sizes are larger so the prior is almost always swamped by the data, so Bayesian methods don't add anything.
My answer to point 1: Bayesian methods are about more than priors. I think this criticism comes from the (limited and in my view somewhat misguided) view of priors as a personal belief. That is one way of specifying them but not the most useful way. As Andrew Gelman has said, prior INFORMATION not prior BELIEF. And you can probably specify information in pretty much the same way for both Phase 2 and Phase 3 trials.
My answer to point 2: Bayesian methods aren't just about including prior information in the analysis (though they are great for doing that if you want to). I'll reiterate my reasons for preferring them that I gave earlier - the methods make sense, answer relevant questions and give understandable answers. Why would you want to use a method that doesn't answer the question and nobody understands? Also, If you DO have good prior information, you can reach an answer more quickly by incorporating that in the analysis - which we kind of do by doing trials and then combining them with others in meta-analyses; but doing it the Bayesian way would be neater and more efficient.