Validating AI-Generated Code in Clinical Programming: An Auditable SOP

Use AI assistants to draft R code for a regulated analysis without compromising submission integrity — treat every AI output as an unqualified draft, run it through the same validation gates any code must pass, and keep a human-reviewed prompt-to-approval audit trail

A practical governance SOP for using AI coding assistants (ChatGPT, Claude, Copilot) to draft R code for a clinical analysis without risking your submission. Learn the core rule — AI output is an unqualified draft until it passes validation — then the workflow that makes it defensible: a human programmer reviews and understands every line, the same QC gates run (independent double programming, defensive assertions, reproducibility, tests), and a documented prompt -> draft -> review -> approved audit trail records what produced each derivation. A runnable example catches a seeded AI error with diffdf, and the SOP is mapped to the FDA’s draft AI credibility framework, GAMP 5, and the R Validation Hub.

TipKey takeaways

• AI output is a draft by an unqualified author. An assistant has no accountability, cannot sign a validation record, and its training data is not your study. That the code ran — even that it looks right — is not evidence it is correct. It is a starting point, not a result.
• Validate AI-generated code with the same gates any code must pass. Nothing about this SOP is AI-specific: a human programmer reviews and understands every line, then the code clears independent double programming, defensive assertions, reproducibility, and tests. The gate does not care who — or what — wrote the draft.
• Keep a prompt -> draft -> review -> approved audit trail. Record the model and version, the prompt, the raw draft, the reviewer, the changes made, and the approval. A derivation nobody can trace back to how it was produced is a hole in your record.
• Use the frameworks as principle, not as a shortcut. The FDA’s draft AI guidance (risk-based credibility, context of use, human accountability), GAMP 5, and the R Validation Hub all point the same way: assess risk, control it, and keep a human answerable for the result.
• Let AI do what is cheap to check, not what is dangerous to get wrong. Boilerplate, refactors, documentation, and test scaffolds are low-risk uses. Derivation logic, statistical method choices, and boundary rules are where a plausible-but-wrong draft slips through — validate those hardest.

Introduction

The assistant wrote a flawless-looking derive_agegr1() in seconds. It ran without error. Spot-checked against a couple of subjects, it even looked right. The deadline is tomorrow. Would you put its output in a submission on that basis?

The honest answer is no — and the reason is the whole of this lesson. An AI coding assistant (ChatGPT, Claude, GitHub Copilot) is a genuinely useful drafting tool for a clinical programmer. But its output is a draft written by an author who cannot be held accountable — one who has never read your statistical analysis plan, whose training data is not your study, and who will produce a confident, plausible, syntactically perfect derivation that is quietly wrong just as readily as a correct one. In a regulated analysis, “the code ran” was never the standard. The standard is that a qualified human validated it — and that standard does not move because the first draft came from a model instead of a keyboard.

This lesson is the SOP (standard operating procedure) for doing exactly that: using AI to draft R code for a regulated analysis without compromising submission integrity. The core principle is a single sentence — AI output is unqualified until it passes the same validation gates any code must pass — plus a documented, human-reviewed audit trail from prompt to approval. It is the governance capstone of this series: it takes the QC (quality control) disciplines you already have — independent double programming, defensive assertions, reproducible environments — and points them at AI-generated code.

The split above is the practical heart of it: an assistant is safe where a mistake is cheap to catch and dangerous where a mistake is a wrong number in a table. The SOP makes that judgement systematic. (The scores are an illustration, not a measurement.)

The core rule: AI output is an unqualified draft

Fix one sentence in your head before anything else:

AI-generated code is a draft by an unqualified author. It carries no validation status until a qualified human puts it through the same gates any other code must pass.

Everything else follows from that. “Unqualified” is not a judgement about capability — modern assistants write competent R. It is a statement about accountability and evidence. A validated derivation in a submission needs a qualified person who understood it, checked it, and is answerable for it. An assistant provides none of those things. So its output enters your process at the same point a junior programmer’s untested first draft would: as work to be reviewed, not as a result to be trusted.

This reframing is liberating, not restrictive. You do not need a special “AI validation methodology.” You need to route AI drafts through the QC discipline you already run — and to record that you did.

Why an assistant’s output is unqualified

Three properties of an AI assistant make its raw output unfit to trust, and each maps to a specific risk you are already trained to control.

• No accountability. In a regulated environment, a validated result has a name attached — someone who reviewed it and signs the record. An assistant cannot review, cannot sign, and cannot be held responsible when a derived value is wrong. Accountability does not transfer to the tool; it stays with the human who used it. If no human understood and approved the code, no one is accountable for it — which is disqualifying on its own.
• Confident, plausible, and sometimes wrong. A language model generates the most likely next token, not the correct derivation. It will invent a function that does not exist, apply a subtly wrong statistical method, or flip a < to a <= at a category boundary — and present all of it with the same fluent confidence as code that is right. The failure mode is not a crash you would notice; it is a plausible answer you would not.
• Its training data is not your study. The assistant has never read your protocol, your statistical analysis plan, or your dataset specifications. It pattern-matches against generic examples from its training, which is exactly why it produces derivations that are reasonable in general and wrong for your study — the population flag your protocol defines differently, the visit window your SAP specifies, the imputation rule your analysis requires.

None of this means “do not use AI.” It means: treat what it produces as unqualified, and qualify it yourself.

The SOP: validate before use

Here is the workflow end to end. The shape is deliberately ordinary — it is your existing QC process with an explicit AI-drafting step bolted on the front and an explicit audit-trail step recording it.

1. Author the prompt. A qualified programmer writes the prompt from the real specification (the SAP, the dataset spec), stating the derivation, the inputs, and the rule precisely. The prompt is an authored artefact — keep it.
2. AI drafts the code. The assistant returns a draft. It is unqualified. Nothing about it being syntactically valid or running without error changes that.
3. A human reviews and understands every line. The reviewing programmer reads the draft against the specification and must be able to explain why each line is correct — not “the AI wrote it,” but “this implements the spec’s rule, and here is how.” Code the reviewer cannot fully justify is rewritten, not waved through. This step is the one an assistant can never do for you.
4. The same QC gates run — unchanged. The reviewed code now clears the exact gates any code must:
   • Independent double programming — a second programmer re-derives the output from the same spec with separate code, and diffdf() compares the two to zero differences. (Crucially, the QC programmer does not start from the AI draft — that would defeat independence.)
   • Defensive assertions — the code guards its own inputs and outputs with checkmate/stopifnot(), so a violated assumption fails loudly instead of producing a quietly wrong number.
   • A reproducible environment — the analysis runs in a pinned, restorable environment via renv, so the validated result regenerates byte-for-byte later.
   • Tests — unit tests cover the derivation’s boundaries and edge cases.
5. Document the approval. The reviewer records the outcome and signs off, and the audit trail (below) captures how the code was produced. Only now is the code qualified.

The same steps, as roles and gates:

Step	Who	Action	Gate it must clear
1. Prompt	Production programmer	Write the prompt from the specification	Prompt recorded
2. Draft	AI assistant	Generate candidate code	(none — output is unqualified)
3. Review	Production programmer	Read, understand, and justify every line vs the spec	Reviewer can explain each line
4a. Double programming	Independent QC programmer	Re-derive separately; compare with diffdf()	Zero differences
4b. Assertions	Production programmer	Guard inputs/outputs with checkmate/stopifnot()	Assertions pass
4c. Reproducibility	Production programmer	Pin the environment with renv	Result regenerates
4d. Tests	Production programmer	Unit-test boundaries and edge cases	Tests green
5. Approval	Reviewer / lead	Sign off; record the audit trail	Documented approval

The assistant appears once, at step 2, and its output holds no status until it has passed everything after it. That is the entire governance idea: the AI moves fast; the gate does not move at all.

The gate in action: catch an AI derivation error

The abstract rule becomes concrete the moment a gate catches a real bug. Take a common task: derive AGEGR1, the pooled age group on an ADaM (Analysis Data Model, the analysis-ready dataset layer) subject-level dataset, to the spec <65, 65-74, >=75.

The dataset is an ADSL (the Subject-Level Analysis Dataset — one row per subject). Below, the “AI-drafted” derivation makes a classic, plausible mistake — it writes the middle boundary as AGE <= 75, which quietly sweeps every 75-year-old into 65-74 instead of >=75. The code runs. It looks right. A qualified QC programmer, working independently from the same spec, re-derives the variable with cut() and compares the two with diffdf() — the double-programming gate from earlier in this series. Watch it catch what a glance would not.

library(diffdf)

# A small subject-level dataset, standing in for ADSL (built in-block so this runs anywhere)
adsl <- data.frame(
  USUBJID = sprintf("01-%03d", 1:8),
  AGE     = c(64, 65, 74, 75, 80, 62, 65, 75),
  stringsAsFactors = FALSE
)

# --- The AI-drafted derivation: plausible, runs cleanly, subtly wrong (<= 75) ---
ai_out <- adsl
ai_out$AGEGR1 <- ifelse(adsl$AGE < 65, "<65",
                 ifelse(adsl$AGE <= 75, "65-74", ">=75"))   # bug: 75 belongs in ">=75"

# --- Independent QC re-derivation from the SAME spec (<65, 65-74, >=75) ---
qc_out <- adsl
qc_out$AGEGR1 <- as.character(
  cut(adsl$AGE, breaks = c(-Inf, 65, 75, Inf), right = FALSE,
      labels = c("<65", "65-74", ">=75"))
)

# --- The gate: compare the two on the subject key ---
diffdf(ai_out, qc_out, keys = "USUBJID")

Differences found between the objects!

Summary of BASE and COMPARE
  ====================================
    PROPERTY      BASE        COMP    
  ------------------------------------
      Name       ai_out      qc_out   
     Class     data.frame  data.frame 
    Rows(#)        8           8      
   Columns(#)      3           3      
  ------------------------------------


Not all Values Compared Equal
  =============================
   Variable  No of Differences 
  -----------------------------
    AGEGR1           2         
  -----------------------------


  ===================================
   VARIABLE  USUBJID  BASE   COMPARE 
  -----------------------------------
    AGEGR1   01-004   65-74   >=75   
    AGEGR1   01-008   65-74   >=75   
  -----------------------------------

diffdf() reports two differences — subjects 01-004 and 01-008, both aged 75, where the AI draft says 65-74 but the correct derivation says >=75. Neither a clean run nor a quick eyeball caught it; the independent gate did. That is the whole argument in one output: running is not the same as correct, and the gate is what tells them apart.

Fix the boundary and the gate confirms it:

library(diffdf)

adsl <- data.frame(
  USUBJID = sprintf("01-%03d", 1:8),
  AGE     = c(64, 65, 74, 75, 80, 62, 65, 75),
  stringsAsFactors = FALSE
)

# The corrected derivation: strict "< 75" for the middle band
ai_fixed <- adsl
ai_fixed$AGEGR1 <- ifelse(adsl$AGE < 65, "<65",
                   ifelse(adsl$AGE < 75, "65-74", ">=75"))

qc_out <- adsl
qc_out$AGEGR1 <- as.character(
  cut(adsl$AGE, breaks = c(-Inf, 65, 75, Inf), right = FALSE,
      labels = c("<65", "65-74", ">=75"))
)

diffdf(ai_fixed, qc_out, keys = "USUBJID")

No issues were found!

“No issues were found!” — the corrected AI-drafted derivation now matches the independent QC derivation exactly. Only at this point, with the code reviewed and the gate green, is the derivation qualified. The lesson is not that AI is untrustworthy; it is that trust is earned at the gate, the same way it is for any code.

The audit trail: what you keep

A qualified result is not enough on its own — a regulated process must also show how it was produced. An audit trail is the record that reconstructs that history: who did what, when, and on the basis of what. For AI-assisted code, that record has to answer a question a reviewer or an inspector may reasonably ask years later: which model and prompt produced this derivation, and who validated it? Capture enough to answer it.

Record	Example	Why it is in the audit trail
Model & version	claude-opus-4-x, accessed 2026-07-01	Different models/versions behave differently; the record must name what produced the draft
Prompt	The exact instruction and any context given	The prompt is the authored input; it lets a reviewer judge whether the ask itself was sound
Raw draft	The assistant’s unedited output	Distinguishes what the AI produced from what the human changed
Reviewer	The qualified programmer’s name	Accountability attaches to a person, not a tool
Changes made	Diff from raw draft to approved code	Shows the human’s judgement was applied, and where
QC evidence	diffdf() report, test results, assertions	The gates that qualified the code
Approval	Sign-off, date	The record that the result is validated

You do not need a bespoke system for this. A prompt saved beside the code, a commit that records the raw draft and then the reviewed version, and your existing QC and sign-off records together are the audit trail. The only new discipline is not throwing away the prompt and the raw draft — the two artefacts that say an AI produced the first version and a human qualified the final one.

Mapping to the frameworks

No regulator has published a rule titled “how to use ChatGPT to write clinical code.” What exists are higher-level frameworks whose principles this SOP applies. Use them as principle, and be precise about what each does and does not cover.

The FDA’s draft AI framework — the principle, not a mandate for code assistants. In January 2025 the FDA issued its first draft guidance on AI for drug development, Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products. It proposes a risk-based credibility assessment framework in which the validation effort scales with an AI model’s context of use (COU) — how the model’s output is used and how much a wrong output would matter — and it keeps a human sponsor accountable. Two things to be exact about:

• It is draft guidance, not a final rule. The comment period closed in April 2025 and it proposes, rather than requires, an approach. Treat it as direction of travel, not a binding checklist.
• It governs AI models that produce evidence, not code assistants. The framework’s subject is an AI model whose output supports a regulatory decision — for example, a model predicting a clinical endpoint or a manufacturing attribute. That is related but distinct from what this lesson is mostly about: an AI assistant that drafts code a human then validates. A code-generation assistant is a software tool whose output — the code — you validate like any code (a GAMP-style, risk-based activity plus your QC gates); the code assistant is not itself “an AI model producing submission evidence.” What you borrow from the FDA framework is its principle — risk proportional to context of use, and a human answerable for the result — not a direct obligation.

GAMP 5 — validate the software tool, risk-based. The de-facto standard for computerized system validation (ISPE’s GAMP 5: A Risk-Based Approach to Compliant GxP Computerized Systems, 2nd Edition — GAMP = Good Automated Manufacturing Practice; GxP = the “good practice” quality regulations, e.g. GCP/GMP/GLP) frames validation as effort proportional to risk. An AI coding assistant is, in GAMP terms, a tool used to produce a deliverable; you assure the deliverable (the code) against its intended use with an effort that matches its risk. ISPE’s dedicated GAMP Guide: Artificial Intelligence (2025) extends this specifically to AI-enabled systems in GxP contexts.

The R Validation Hub — the same logic for R packages. The cross-industry R Validation Hub treats package trust as a risk-based activity: assess a component’s risk, then apply proportional evidence. AI-drafted R code is one more component to which you apply exactly that lens.

All three converge on the same three moves this SOP encodes: assess the risk, control it with proportional validation, and keep a human accountable for the result. The AI does not change the principles — it is just a new source of drafts that must run the same gauntlet.

What AI is good and bad at here

The hero figure sorted uses by residual risk; here is the practical reading of it.

Good uses — low risk, cheap to check. Let the assistant handle work where a mistake is obvious or quickly caught: boilerplate and refactors, converting a data frame’s shape, drafting roxygen docstrings and comments, and scaffolding unit tests (which you then read and, ideally, extend by hand). In these, the assistant saves real time and a review catches errors immediately.

Dangerous uses — high risk, plausibly wrong. Be most skeptical exactly where the failure mode is a correct-looking wrong answer: derivation logic (the < vs <= boundary above), the choice of statistical method or model, imputation rules, and any edge-case handling your SAP specifies. Here an assistant will produce something reasonable-in-general and wrong-for-your-study, and it is the human review plus the gates — not the assistant — that must catch it. Never let AI decide a statistical method; let it, at most, draft the code for a method a qualified statistician has already chosen.

The rule of thumb: use AI where you can cheaply verify it, and validate hardest where you cannot glance and know.

🟢 With an AI agent

Ask Prova “help me write a defensible SOP for using an AI assistant to draft an ADaM derivation in R — the prompt-to-approval steps, the QC gates it must pass, and what to record in the audit trail” — it answers grounded in this series’ QC lessons, with the runnable double-programming and assertion checks you can try on the draft. The runtime is the judge. Ask Prova →

Common issues

“The code ran, so it’s fine.” This is the single most dangerous instinct with AI-generated code — and the one the whole SOP exists to break. Running without error means the syntax is valid, nothing more. The AGEGR1 bug above ran perfectly and was wrong. A clean run is the beginning of validation, never the end of it; only the gates (review, double programming, assertions, tests) qualify the output.

Trusting AI for the statistical method, not just the boilerplate. It is reasonable to let an assistant draft the code for a mixed model a statistician has specified. It is not reasonable to ask it which model to use and take the answer. AI is a competent typist and a poor methodologist: it will confidently recommend a plausible-but-inappropriate method for your design. Keep the method decision with a qualified human; let AI, at most, implement a decision already made.

No record of which model and prompt produced a derivation. Six months later, a reviewer asks how a derived variable was built and you cannot say whether a human or a model wrote the first version, with what instruction, or who checked it. If you use AI but keep no prompt, no raw draft, and no note of the model, you have a validation gap that is invisible until someone asks. Capturing the audit trail (above) is cheap; reconstructing it after the fact is impossible.

Frequently asked questions

NoteCan I use AI to write clinical trial code?

Yes — as a drafting tool, not as a source of validated code. An AI assistant can draft R code for a clinical analysis, but that draft is unqualified: a qualified human must review and understand every line, and the code must then pass the same validation gates any code passes (independent double programming, defensive assertions, reproducibility, tests). Keep a prompt-to-approval audit trail recording the model, the prompt, the raw draft, the reviewer, and the sign-off. Used that way, AI speeds up the drafting without weakening the validation.

NoteIs AI-generated code validated?

No. AI-generated code has no validation status of its own — that the code ran, or looks correct, is not evidence it is correct. It becomes validated only after a qualified human reviews it and it clears your QC gates, exactly like code written by hand. The author of the draft (human or model) does not confer validation; the review and the gates do.

NoteWhat does the FDA say about AI in drug development?

In January 2025 the FDA issued its first draft guidance on AI for drug and biological products, proposing a risk-based credibility assessment framework tied to an AI model’s context of use. Two caveats matter: it is draft guidance (it proposes, it does not require), and it targets AI models that produce evidence supporting a regulatory decision — which is distinct from an AI coding assistant that drafts code a human then validates. For code assistants, take the FDA framework’s principle (risk proportional to context of use, human accountability) and apply your usual computerized-system-validation and QC discipline to the code itself.

NoteWhat do I keep for the audit trail?

Enough to reconstruct how each AI-assisted derivation was produced and qualified: the model and version, the exact prompt, the raw draft the assistant returned, the reviewer, the changes from draft to approved code, the QC evidence (the diffdf() report, test results, passing assertions), and the approval with a date. In practice a saved prompt, a commit history that shows the raw draft and the reviewed version, and your existing QC and sign-off records cover it — the only new habit is not discarding the prompt and the raw draft.

NoteDoes using AI mean I can skip double programming?

No — if anything, independent double programming matters more. AI produces plausible-but-wrong output that survives a casual look, which is exactly what an independent re-derivation is built to catch. Keep the double programming genuinely independent: the QC programmer works from the specification, not from the AI draft, or the check is no longer independent.

Test your understanding

ImportantYour turn: qualify an AI-drafted derivation

An assistant drafts a derivation of a treatment-emergent flag TRTEMFL: it should be "Y" when an adverse event’s start day is on or after the first treatment day (ASTDY >= 1), else "N". The AI writes ifelse(ASTDY > 1, "Y", "N") — a plausible > vs >= slip. You have an independent QC derivation to compare against.

1. In prose: name the two SOP steps that should catch this before it ships, and say why “the code ran” does not.
2. In R: build a small data frame with ASTDY = c(1, 2, 0, 5), derive the AI version (> 1) and an independent QC version (>= 1) of TRTEMFL, and use diffdf() on a subject key to catch the discrepancy.

TipHint

For step 1, think about who reads the draft against the spec (step 3) and which gate re-derives it independently (step 4a). For step 2, give each row a USUBJID, add both flags with ifelse(), and call diffdf(ai, qc, keys = "USUBJID"). The subject with ASTDY = 1 is where > and >= disagree.

TipSolution

Step 1. The human review (step 3) — a reviewer reading > 1 against the spec’s >= 1 should question the boundary — and independent double programming (step 4a) — a QC programmer re-deriving from the spec, then diffdf(), catches any subject on the boundary. “The code ran” only means the syntax is valid; the > 1 version runs perfectly and is wrong for every subject whose event starts on treatment day 1.

library(diffdf)

df <- data.frame(
  USUBJID = sprintf("01-%03d", 1:4),
  ASTDY   = c(1, 2, 0, 5)
)

# AI draft: plausible ">" instead of ">="
ai <- df
ai$TRTEMFL <- ifelse(df$ASTDY > 1, "Y", "N")

# Independent QC derivation from the spec (>= 1)
qc <- df
qc$TRTEMFL <- ifelse(df$ASTDY >= 1, "Y", "N")

diffdf(ai, qc, keys = "USUBJID")
#> Not all Values Compared Equal ... AGEGR1-style report:
#> subject 01-001 (ASTDY = 1): BASE "N"  COMPARE "Y"  -> the boundary bug, caught

The one subject with ASTDY = 1 differs — diffdf() flags it, the review explains it, and the derivation is corrected to >= 1 before it is qualified.

NoteQuick check: an AI-drafted derivation runs cleanly and looks right. What is its validation status?

A. Validated — it ran without error and passed a visual check. B. Unqualified — until a human reviews it and it clears the QC gates, it has no validation status. C. Validated, provided the assistant is a current, capable model.

TipShow answer

B. Running cleanly and looking right are not evidence of correctness — the AGEGR1 bug did both and was wrong. AI output is an unqualified draft regardless of which model produced it (ruling out C); it earns validation only through human review plus the same QC gates any code must pass (ruling out A).

Conclusion

Using AI to draft clinical code does not require a new validation philosophy — it requires holding the line on the one you already have. Fix the core rule: AI output is a draft by an unqualified author, with no validation status until a qualified human reviews it and it clears the same gates as any code — independent double programming, defensive assertions, a reproducible environment, tests. Add one discipline on top: a prompt-to-approval audit trail that records the model, the prompt, the raw draft, the reviewer, the changes, and the sign-off, so anyone can later see how each derivation was produced and who qualified it. Map it to the frameworks for principle — the FDA’s draft, risk-based credibility framework (context of use, human accountability), GAMP 5’s risk-proportional validation, the R Validation Hub — not for a shortcut. Do that, and AI becomes what it should be: a fast drafter feeding a gate that has not moved an inch. The runtime is still the judge.

Note

This lesson is reproducible: the diffdf() demonstration blocks — the AI-drafted AGEGR1 bug being caught, and the corrected derivation passing — reproduce here. Copy either block and run it to reproduce the difference report. The audit-trail and SOP-step contents are reference tables, and the AI drafting step happens in your own tooling. The runtime is the judge.

Related lessons

• Double programming in R with diffdf — the independent-verification gate this SOP points at AI-generated code; the diffdf() engine that caught the seeded bug above. · Defensive programming in R with checkmate and assertthat — the assertion gate that makes an AI draft fail loudly on a violated assumption instead of returning a quietly wrong value. · Reproducible environment in R with renv — pinning the environment so a validated, AI-assisted analysis regenerates exactly later. · Create ADSL in R with admiral — the kind of derivation an assistant might draft and this SOP must qualify.
• Where this fits: this is the governance capstone of the QC series — build a derived dataset, validate its outputs (double programming), defend its inputs (assertions), and freeze its environment (reproducibility), then apply all of those gates to AI-drafted code and record how it was produced. The gate is the same; only the source of the first draft is new.

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