AI-Generated Statistical Code for Clinical Researchers: 5 Failure Modes and the Verification Stack

AI statistical code for clinical research usually compiles on the first attempt. That is the trap. Code that runs is not code that is correct, and a clean console invites you to trust the output. An independent-samples t-test applied to paired measurements still returns an estimate, a p-value, and a tidy figure — and none of them signal that the test family was wrong.

The errors are not random. Language models fail on clinical analysis in a small set of recurring ways, and each one has a specific check that catches it. Below is the list, then the verification routine that belongs between the model output and your manuscript.

Five failure modes

Wrong test family for the design

The model reads your sentence, not your data structure. Ask for a group comparison and it reaches for the most common test — usually an independent-samples t-test or a chi-square — whether or not your measurements are paired, repeated, or nested. Pre-post designs, matched cohorts, and crossover trials get flattened into two independent groups. The output looks submittable. The standard errors are not.

Clustering that never gets modelled

Cluster-randomised trials, multi-site studies, and repeated measures within a patient all break the independence assumption a plain regression relies on. Generated code rarely adds a random effect or a GEE correction unless you name the clustering variable in the prompt. Without it, the intervals are too narrow and the p-values run optimistic. CONSORT 2025 asks trialists to state how clustering was handled in the analysis — a field these scripts leave empty by default.

Misspecified survival objects

In R, a Cox model is only as correct as the Surv() object underneath it. The frequent generated mistakes: treating censoring as the event, swapping the time and status arguments, or ignoring delayed entry in registry data that needs left-truncation. The survival package documentation is explicit about argument order, but the model fits and prints hazard ratios either way.

Environment drift in hospital systems

Code written against a recent tidyverse can fail quietly on a locked-down hospital workstation running an older R. Functions get deprecated, default arguments shift, and a pipeline that gave one answer on a laptop gives another on the analysis machine — or errors in a way that tempts a quick patch. Pin package versions and save the session info next to the results.

Assumptions the output never states

A regression table does not tell you whether linearity, proportional hazards, or equal variance were checked. The model will not volunteer that it skipped the diagnostics. Every generated analysis needs an explicit assumptions pass — the same one a statistics reviewer runs, walked through in statistical assumption check with Claude.

The three-layer verification stack

One read-through is not enough, because the layers fail differently.

Layer 1 — code review. Read the code against the study design, not against the prose you fed the model. Does the test match the data structure? Is the clustering variable in the model formula? Is the Surv() object built with time and status in the right order?

Layer 2 — output sanity check. Compare the numbers to what the raw data should produce. A mean outside the observed range, a hazard ratio pointing the wrong way, an analysed sample size that does not match your participant flow — these show up on inspection, never in the code itself.

Layer 3 — assumption audit. Enumerate the assumptions each test requires, then confirm each was tested and reported. This is where a wrong-but-clean analysis gets caught. It is the same pattern — correct-looking output, wrong underlying logic — that shows up in AI blind spots in randomization schemes.

Before the numbers reach your manuscript

Generated statistical code is a draft analysis, not a result. Treat it the way you would treat a co-author's first pass: fast, useful, and unverified until you have checked it against the design. AI for Academic's peer-review stress test at aiforacademic.world audits a manuscript's methods and results against the reporting standard for your study type, which surfaces the missing diagnostics and the mismatched tests before a reviewer does. Free to start.