Bayesian Workflow Toolkit (Audit-Ready)
Executive Summary
This appendix provides a Bayesian Workflow Toolkit designed for regulated, high-stakes clinical modeling.
It includes:
- Prior justification templates (priors as scientific claims)
- Sensitivity analysis checklist (fragility discovery, not robustness theater)
- Audit log schema (what must be traceable post-deployment)
- Reproducible report scaffold (one-click regeneration of the full analytic record)
The focus is not Bayesian elegance. The focus is defensible inference under scrutiny, consistent with modern Bayesian workflow principles that emphasize model checking, sensitivity analysis, and transparent reporting (Gelman et al. 2020; Kruschke 2015).
1. Prior Justification Templates
1.1 Why priors must be documented
In Bayesian workflows, priors are not defaults. They encode assumptions about scientific plausibility, scale, and regularization, and they should be justified as part of the modeling workflow (Gelman et al. 2013, 2020).
They encode assumptions about:
- plausible effect sizes
- biological realism
- data quality and noise
- ethical bounds on extrapolation
An undocumented prior is an unreviewable assumption.
1.2 Prior justification table (template)
Use this table verbatim in reports and appendices.
prior_justification <- tibble::tribble(
~parameter, ~prior, ~scale, ~rationale, ~source,
"b_age", "Normal(0, 1)", "log-odds",
"Assumes moderate association; extreme age effects implausible after adjustment",
"Clinical judgment + prior literature",
"b_severity", "Normal(0, 1)", "log-odds",
"Severity expected to dominate but not overwhelm baseline risk",
"Domain expertise",
"Intercept", "Normal(0, 2)", "log-odds",
"Allows wide baseline risk without implausible extremes",
"Prevalence-based reasoning",
"sd_site", "Exponential(1)", "SD",
"Encourages partial pooling while allowing site heterogeneity",
"Hierarchical modeling best practice"
)
prior_justificationAudit prompt: Could an independent reviewer understand and challenge each rationale?
1.3 Prior predictive checks (required)
Every prior must be checked before seeing the data through prior predictive simulation, which is now a standard recommendation in Bayesian applied work (Gelman et al. 2013, 2020).
# brms example
# library(brms)
# prior_only_fit <- brm(
# outcome ~ age + severity + (1 | site),
# data = analysis_df,
# family = bernoulli(),
# prior = priors,
# sample_prior = "only"
# )
# pp_check(prior_only_fit)If prior predictive draws generate implausible outcomes, the prior is wrong.
2. Sensitivity Analysis Checklist (Bayesian)
2.1 Purpose
Sensitivity analysis answers a single question, and in Bayesian work it is a way to examine dependence on assumptions rather than to perform robustness theater (Gelman et al. 2020):
Which conclusions depend on assumptions we cannot verify?
This checklist should be completed before deployment.
2.2 Checklist (complete for each model)
Model structure
Priors
Data handling
Temporal / population stability
Decision sensitivity
If any box is unchecked, deployment must be justified explicitly.
3. Audit Log Schema
3.1 Why an audit log is non-negotiable
Bayesian models evolve. Teams change. Memories fade.
An audit log preserves epistemic continuity.
3.2 Minimal audit log (row-level schema)
Each row corresponds to a model action (fit, recalibration, redeploy).
audit_log_schema <- tibble::tribble(
~field, ~description, ~example,
"timestamp", "When the action occurred", "2023-12-15 14:32:00",
"analyst", "Responsible individual", "JD Stallings",
"model_id", "Unique model identifier", "bayes_bleed_v3",
"model_hash", "Code hash or version tag", "a94c1f7",
"data_version", "Input data fingerprint", "dodtr_2023Q3_md5",
"action", "fit / recalibrate / suspend / deploy", "recalibrate",
"prior_set", "Named prior regime", "baseline_priors",
"diagnostics_passed", "Yes/No with notes", "Yes",
"calibration_status", "In control / warning / action", "Warning",
"decision", "Action taken", "Intercept recalibration",
"justification", "Why this action was taken", "EWMA OOC 2 periods",
"reviewer", "Independent reviewer (if any)", "Clinical SME",
"notes", "Free text", "No patient safety concerns identified"
)
audit_log_schema3.3 Governance rule
If a model decision cannot be reconstructed from the audit log, it did not happen.
4. Reproducible Report Scaffold
4.1 Purpose
This scaffold defines the single authoritative report that can be regenerated at any time.
It should be rendered:
- before deployment
- after recalibration
- during audits
- during post-hoc review
4.2 Directory structure (recommended)
# One-time setup
dir.create("R", showWarnings = FALSE)
dir.create("data_raw", showWarnings = FALSE)
dir.create("data_processed", showWarnings = FALSE)
dir.create("models", showWarnings = FALSE)
dir.create("reports", showWarnings = FALSE)
dir.create("logs", showWarnings = FALSE)4.3 Master report template (analysis_report.Rmd)
---
title: "Bayesian Model Analysis Report"
author: "Jonathan D. Stallings, PhD, MS"
date: "`r format(Sys.Date(), '%Y-%m-%d')`"
output:
html_document:
toc: true
theme: readable
fontsize: 11pt
---
## 1. Executive Summary
- Intended use
- Decision context
- Key risks
## 2. Data Provenance
- Sources
- Inclusion/exclusion
- Missingness profile
## 3. Model Specification
- Likelihood
- Priors (table included)
- Hierarchy
## 4. Diagnostics
- Convergence
- Posterior predictive checks
- LOO / fit statistics
## 5. Sensitivity Analyses
- Prior regimes
- Data assumptions
- Thresholds
## 6. Calibration & Drift Monitoring
- Current status
- Historical trends
- Action thresholds
## 7. Results for Decision-Making
- Risk distributions
- Uncertainty
- Safe operating regions
## 8. Limitations
- Known failure modes
- Out-of-scope use cases
## 9. Governance & Audit Trail
- Model history
- Decisions logged
- Open issues
## Appendix
- Full prior justification
- Code snapshots
- Session info4.4 One-click regeneration
rmarkdown::render("reports/analysis_report.Rmd")If this fails, the workflow is not audit-ready.
Bayesian logistic regression and Bayesian neural networks are increasingly used in clinical AI specifically for uncertainty quantification — the full posterior predictive distribution tells a clinician not just the predicted probability but how much the model’s estimate should be trusted. DoD RAIMF audit requirements align naturally with Bayesian workflow documentation: prior justification, convergence diagnostics, and posterior predictive checks create the audit trail that governance frameworks demand. A Bayesian model without posterior predictive checks may have failed to converge or may be sampling from an unidentified posterior — producing confident-looking predictions that are statistically meaningless. In MAVEN-integrated decision support, a model that cannot communicate its own uncertainty is not a safer model; it is a model whose failures will be invisible until they cause harm.
Closing Notes
Bayesian methods do not guarantee rigor.
Bayesian workflows enforce it.
When priors are justified, when sensitivity is explored, when decisions are logged, and when reports regenerate cleanly,
you are no longer relying on trust. You are providing evidence.
Series Callout
This post is part of a broader Toolkit Series for Applied Statistics, AI, and Clinical Analytics:
- Bayesian Workflow Toolkit
- Calibration Toolkit
- Missing Data Toolkit
- Rare Events Toolkit
- Causal Inference Toolkit
- Survival Analysis Toolkit
- Prediction Modeling Toolkit
- Real-World Evidence Toolkit
- OMOP and Interoperability Toolkit
- Trauma Registry Analytics Toolkit