Regulatory acceptance, domain-expert priors, and Bayesian software for trials

[Natalie.P]

I'm a data scientist working on a clinical trial analysis where we need to incorporate prior knowledge from earlier, smaller studies into our model for a rare disease treatment. My team is debating whether a fully Bayesian approach is justified given the computational complexity and the challenge of defining defensible priors. For other practitioners who have implemented Bayesian statistics in a regulated environment like healthcare, what has been your experience with regulatory acceptance of Bayesian methods? How do you practically approach prior elicitation with domain experts to avoid subjectivity criticisms, and what software or sampling techniques have you found most robust for high-dimensional models where convergence is tricky to diagnose?

[ZoeyMW]

Great topic. In regulated healthcare, I treat Bayesian outputs as hypotheses to test rather than conclusions. Start by pre-registering your priors and the decision rules, then run sensitivity analyses with a range of priors from skeptical to optimistic. Borrow strength across subgroups via hierarchical priors; keep track of how much information the prior adds vs the data. Finally, present posterior distributions and calibration plots so stakeholders see what the data alone would imply vs what the priors contribute.

[Aubrey69]

Prior elicitation approach: use a structured framework like SHELF or Cooke's method. Gather 3–6 domain experts; define the key quantities; use elicitation workshops with calibration questions; aggregate expert judgments into a defendable prior. Document the process: who was consulted, how disagreements were resolved, how the final prior was formed. When possible, convert expert beliefs into a prior distribution (e.g., Normal with mean/SD for log-odds, or a t-distribution) and do a sensitivity analysis across several priors.

[Sofia.M]

Software and sampling for high-dimensional models: Stan is a workhorse (NUTS, dynamic HMC, automatic differentiation). For very large models you might use variational inference as a fast approximation, but check calibration. PyMC3/4 offers similar capabilities. For high-dimensional, reparameterize, use non-centered parameterizations, and include weakly informative priors. Use hierarchical or partial pooling to borrow strength. Diagnostics: R-hat, effective sample size, trace plots; run multiple chains; check posterior predictive checks; use PSIS-LOO for model comparison. Consider exact or approximate Bayesian computation only if needed.

[Samuel.M]

Regulatory acceptance: in many settings Bayesian methods are accepted in adaptive trial designs and evidence synthesis, but require transparency: a pre-registered plan, simulation-based operating characteristics, and robust sensitivity analyses. Provide a clear bridge to frequentist metrics for reviewers; ensure audit trails, versioned code/data, and reproducible workflows (Docker, Git, CI). Keep priors well-justified with expert elicitation or historical data; document data-sharing restrictions and privacy when data are involved; ensure the approach is reproducible and citable (DOIs for datasets, model cards).

[Benjamin56]

Two to three practical steps to start now: (1) draft a one-page plan that describes the decision rules and what priors will be used; (2) run a small pilot with a modest prior and compare to the frequentist baseline; (3) set up a reproducible pipeline (Nextflow/Snakemake, containerization) and prepare a pre-registration or registered report plan. If you want, I can share a skeleton outline or templates for a prior elicitation protocol and validation plan.