A one-day journey from Bayes' Theorem to the FDA's January-2026 Bayesian draft guidance — built for statisticians and clinical-trial professionals who want to understand what Bayesian inference actually does for drug development, not just memorize how to run it.
Motivated by the recent release of FDA's draft guidance on the Use of Bayesian Methodology in Clinical Trials of Drug and Biological Products (12 January 2026), this short course gives a concise yet rigorous introduction to Bayesian statistics — what we call Bayesics — covering its history, its rationale, and its current and future role in clinical trials.
Bayesian statistics rest on two probability statements — one for the data (the likelihood, shared with frequentist inference) and one for the parameter (the prior, unique to Bayesian). Through Bayes' Theorem we get the posterior — a direct probability statement on the question a clinician or regulator actually wants answered: given what we observed, how likely is it that this drug helps?
This short course consists of three parts: (1) Bayesian thinking — the philosophy and human-logic intuition behind Bayes' Theorem; (2) Bayesian modeling and computation — priors, likelihoods, posteriors, MCMC, and what to do when models outgrow conjugacy; and (3) Bayesian clinical trials — early-phase dose finding and optimization, late-phase sample sizing and decision making, and the practical sponsor workflow under the new FDA draft guidance.
Examples include questions every working biostatistician
encounters: what is the difference between type I error and
type I error rate? How does Bayes quantify the chance of a wrong
decision? When does c = 0.975 not mean what
you think it means? The emphasis is on "why"
rather than "how" — attendees should leave able to
defend or critique a Bayesian SAP in a sponsor or regulatory
meeting.
The FDA released its first comprehensive Bayesian draft guidance for drugs and biologics on 12 January 2026. Sponsors who can write a regulator-ready Bayesian SAP — pre-specified priors, robust safeguards, operating characteristics by simulation, computational reproducibility — will be the ones moving programs fastest in 2026 and beyond. Project Optimus, C3TI, and the rapid growth of hybrid and platform designs make Bayesian fluency a practical advantage, not a theoretical curiosity.
Pharma / biotech biostatisticians, clinical pharmacologists, clinical scientists, regulatory affairs, and medical writers working on adaptive trials, dose-optimization studies, hybrid / platform designs, or sponsor briefings to FDA, NMPA, or EMA. Calibrated to give a senior biostatistician something new while remaining accessible to a first-year industry hire.
Graduate students (MS / PhD) in statistics, biostatistics, pharmacometrics, and clinical-trials methodology, plus faculty who want a current view of regulator-aware Bayesian practice. Background in graduate-level probability and inference is assumed; no prior clinical-trials experience is required.
The day is divided into four 90-minute sessions, mapped to the three-part structure above. Each session opens with a short compass slide (what we're solving and why), uses one real case study, and closes with a concrete take-away.
Why we are all Bayesians (whether we admit it or not), why classroom statistics lacks the probability the clinician actually needs, and how Bayes' Theorem closes the gap.
From priors and likelihoods to posteriors that scale. Conjugate models for intuition, then MCMC for everything else, and what to check before you trust the output.
Phase I has shifted from “find an MTD” to “find the optimal dose with desirable efficacy and safety.” We walk through the modern toolset and where regulators stand today.
How to size a Bayesian study, how to read a subgroup analysis, and how the “Bayesics-first, frequentist-translation” workflow survives a regulatory review.
A short open Q&A on submission, software, and where to take this material next. Course materials and code are shared via a public repository after the course.
| Time (ET) | Block | Topic |
|---|---|---|
| 09:00 – 10:30 | Session 1 | Part 1 — Bayesian thinking, Bayes' Theorem, posterior inference |
| 10:30 – 10:45 | Break | Coffee & Q&A |
| 10:45 – 12:15 | Session 2 | Part 2 — Bayesian modeling, MCMC, hierarchical borrowing |
| 12:15 – 13:30 | Lunch | (75-minute break) |
| 13:30 – 15:00 | Session 3 | Part 3a — Early-phase Bayesian designs & Project Optimus |
| 15:00 – 15:15 | Break | Coffee & Q&A |
| 15:15 – 16:45 | Session 4 | Part 3b — BESS, BSA, posterior calibration, sponsor workflow |
| 16:45 – 17:00 | Closing | Open Q&A and follow-up roadmap |
Professor of Biostatistics · The University of Chicago
Chair, Biopharmaceutical Section, International Society for Bayesian
Analysis (ISBA)
Dr. Ji's research focuses on innovative Bayesian methods for translational cancer research and clinical-trial design. He is the author of more than 170 peer-reviewed papers across medical and statistical journals, and the inventor of widely adopted Bayesian adaptive designs including mTPI, i3+3, BESS, ADOPT, and PEDOOP, used in dose-finding and dose-optimization trials worldwide.
His Phase III Bayesian work has been accepted by the FDA, NMPA, and EMA across oncology, infectious disease, and rare-disease programs. He received the Mitchell Prize in 2015 from the International Society for Bayesian Analysis and is an elected Fellow of the American Statistical Association.
None are prerequisite — they are listed so attendees can go deeper after the course.