1 Regulatory Context

The FDA Draft Guidance on Bayesian Statistical Methods for Drug and Biological Products (2026) and the EMA Reflection Paper on Bayesian Statistics both require that Bayesian prior distributions used in confirmatory clinical trials be:

1. Explicitly specified — family, hyperparameters, and elicitation method
2. Justified — documented basis (expert opinion, historical data, or both)
3. Conflict-checked — evidence that prior and data are compatible
4. Sensitivity-tested — conclusions hold under plausible alternative priors
5. Pre-specified — agreed with regulators before unblinding

prior_report() generates a self-contained document addressing all five requirements.

2 The Prior Justification Report

3 Generating a Report

# 1. Elicit the prior
prior <- elicit_beta(
  mean      = 0.30,
  sd        = 0.10,
  method    = "moments",
  expert_id = "Expert_1",
  label     = "Objective response rate"
)

# 2. Run conflict diagnostics
cd <- prior_conflict(
  prior        = prior,
  data_summary = list(type = "binary", x = 18, n = 40),
  alpha        = 0.05
)

# 3. Run sensitivity analysis
sa <- sensitivity_grid(
  prior        = prior,
  data_summary = list(type = "binary", x = 14, n = 40),
  param_grid   = list(alpha = seq(1, 8, 0.5), beta = seq(2, 20, 1)),
  target       = c("posterior_mean", "prob_efficacy"),
  threshold    = 0.30
)

# 4. Build robust and sceptical priors (optional — appear in report if supplied)
rob  <- robust_prior(prior, vague_weight = 0.20)
scep <- sceptical_prior(null_value = 0.20, family = "beta", strength = "moderate")

# 5. Generate the report
# NOTE: requires devtools::install(), not just devtools::load_all()
# Quarto spawns a fresh R session that needs the installed package.
prior_report(
  prior           = prior,
  conflict        = cd,
  sensitivity     = sa,
  robust_prior    = rob,      # included in "Robust and Sensitivity Priors" section
  sceptical_prior = scep,     # ditto
  output_format   = "pdf",
  output_file     = "prior_justification_report",
  trial_name      = "TRIAL-001",
  sponsor         = "BioPharma Ltd",
  author          = "J. Smith, Principal Biostatistician",
  notes         = paste0(
    "Prior elicited from two independent oncologists (Expert_1, Expert_2) ",
    "using the SHELF structured elicitation protocol. Experts were blinded ",
    "to interim data at the time of elicitation. Prior was pre-specified in ",
    "the Statistical Analysis Plan dated 2025-09-01."
  )
)

4 The Notes Field

The notes argument is the statistician’s narrative — the most important section for regulatory reviewers because it provides the scientific rationale that no automated output can supply. It should address:

1. Basis for the prior

"Prior elicited from three independent clinical experts in non-small cell
lung cancer using SHELF quantile matching (O'Hagan et al., 2006). Experts
were asked to specify the 10th, 50th, and 90th percentiles of the ORR
distribution based on knowledge of similar agents in this indication."

2. Expert independence and blinding

"All experts were blinded to any interim results at the time of elicitation.
No expert had a financial interest in the trial outcome."

3. Pre-specification

"The prior distribution and elicitation protocol were pre-specified in the
Bayesian Statistical Analysis Plan (BSAP), version 2.1, submitted to FDA
on 2025-09-01 as part of the IND amendment."

4. Conflict action plan

"In the event of mild conflict (Box p-value 0.01-0.05), a sensitivity
analysis using the robust mixture prior (vague weight 0.30) will be added.
In the event of severe conflict (Box p-value < 0.01), the primary analysis
will be repeated using the sceptical prior as a co-primary analysis."

5 The FDA/EMA Compliance Checklist

The report automatically generates a compliance checklist that maps each section to specific regulatory requirements:

library(knitr)
kable(data.frame(
  Requirement = c(
    "Prior elicitation method documented",
    "Distribution family and parameters specified",
    "Expert / source of prior identified",
    "Prior density plot provided",
    "Prior-data conflict assessed",
    "Conflict diagnostic statistics reported",
    "Sensitivity analysis performed",
    "Sensitivity visualisations provided",
    "Alternative priors considered",
    "Robust / sceptical prior computed",
    "Regulatory report generated"
  ),
  `FDA Guidance Section` = c(
    "Section IV.B", "Section IV.B", "Section IV.B",
    "Section IV.B", "Section IV.C", "Section IV.C",
    "Section IV.D", "Section IV.D", "Section IV.D",
    "Section IV.D", "Section IV.E"
  ),
  check.names = FALSE
), align = "ll")

6 Session Reproducibility

The Session Information section of every report records the full computational environment:

For fully reproducible regulatory submissions, pin the bayprior version in your renv.lock file and archive the complete renv snapshot alongside the submission documents.

7 Working with the Shiny App

The run_app() function launches the full interactive Shiny interface. Every analysis conducted in the app — elicitation, pooling, conflict diagnostics, sensitivity, and robust/sceptical/power priors — is preserved in the session state and flows directly into the report when the Download button is clicked.

Figures in the report reflect exactly what the analyst saw in the app at the time of download. Specifically:

• The prior density plot comes from the fitted prior in the Elicitation panel
• The conflict overlay comes from the Conflict Diagnostics panel
• The tornado and heatmap come from the Sensitivity Analysis panel
• The robust, sceptical, and power prior plots come from the Robust Priors panel — these appear in the report only if those analyses were run in the session

The app workflow mirrors the analytical steps:

Welcome → Prior Elicitation → Expert Pooling → Conflict Diagnostics →
Sensitivity Analysis → Robust Priors → Export Report

8 Pre-Submission Checklist

Before submitting a bayprior report to a regulatory agency:

• Prior was pre-specified in the Statistical Analysis Plan
• Expert elicitation was conducted blind to interim data
• Expert identities and conflicts of interest are documented
• Conflict action plan is pre-specified (what happens at each severity)
• Sensitivity range was agreed with regulators a priori
• At least one alternative prior is reported (robust or sceptical)
• bayprior version is pinned in the renv.lock
• Report generated from installed package (not load_all())
• PDF and HTML versions both generated and cross-checked
• Notes field contains full scientific rationale

9 References

FDA (2026). Draft Guidance for Industry: Bayesian Statistical Methods for Drug and Biological Products.

EMA (2023). Reflection Paper on the Use of Bayesian Statistics in the Evaluation of Medicinal Products.

O’Hagan, A. et al. (2006). Uncertain Judgements: Eliciting Experts’ Probabilities. Wiley.

Spiegelhalter, D. J. & Freedman, L. S. (1994). Bayesian approaches to clinical trials. JRSS-A, 157, 357–416.