Model Card: Clinical Trial Power Analysis — Llama 3.3 70B LoRA
Model Details
Model Description
This is a LoRA adapter fine-tuned on top of Llama-3.3-70B-Instruct to
perform statistical power classification for clinical and research trial
designs. Given a trial's design — test type, sample size, expected effect
size, and significance level — the model computes the actual statistical
power achieved, classifies the trial as adequately powered or underpowered,
and states the minimum sample size that would be needed to reach the
conventional 80% power threshold.
This targets a well-documented, real problem in scientific credibility:
a substantial share of irreproducible findings across psychology, medicine,
and other fields trace back to studies that were underpowered from the
start — enrolled too few participants to reliably detect the effect they
set out to test. Correctly classifying power requires genuine computation
(the noncentral-distribution relationship between sample size, effect size,
and detectable power), not verbal pattern-matching, which is a documented
weakness in how base LLMs reason about statistical adequacy.
Training labels were generated by construction from the standard,
textbook two-sample power approximation formula (n_per_group = 2 × ((z_α/2 + z_β) / d)², the widely-taught closed-form approximation for
two-sided tests at 80% power), so the classification, required sample
size, and enrollment gap are all guaranteed internally consistent and
independently re-verifiable from the reported numbers — not separately
annotated. This formula was additionally sanity-checked against
statsmodels.stats.power (the standard, peer-vetted Python implementation
used in real biostatistics work) before dataset construction.
Submitted to the AutoScientist Challenge (Science track) by
Adaption Labs.
- Developed by: Grold Otieno Mboya
- Shared by: Grold Otieno Mboya
- Model type: LoRA adapter for causal language modeling (statistical
power / trial design reasoning)
- Language(s): English
- License: Llama 3.3 Community License Agreement (inherited from the
base model — see https://www.llama.com/llama3_3/license/)
- Finetuned from model:
meta-llama/Llama-3.3-70B-Instruct
Model Sources
Uses
Direct Use
Checking whether a planned or completed trial's sample size is actually
sufficient to detect its claimed effect size: useful for grant reviewers
and funding committees screening proposals, IRB/ethics committees assessing
trial design, journal reviewers evaluating submitted studies, and
early-career researchers or PhD candidates building intuition around
sample-size decisions before designing a study.
Downstream Use
Could be extended to additional test types beyond the current coverage
(e.g. chi-square tests, survival/Cox regression, cluster-randomized or
non-inferiority designs), or integrated into grant-submission or
IRB-review tooling as an automated pre-screening step.
Out-of-Scope Use
Not a substitute for a full consultation with a professional biostatistician,
particularly for complex designs (multi-arm, adaptive, cluster-randomized,
or non-inferiority trials) that the closed-form approximation used here
does not model. Not evaluated for general capability, factuality, or safety
beyond the Science task category described here. The underlying formula is
a standard approximation, not the exact noncentral-t solution — adequate
for the two-sample/paired/one-sample designs it targets, but not a
replacement for exact power software on borderline or unusual cases.
Bias, Risks, and Limitations
The training data combines 15 real, cited rows sourced from actual
ClinicalTrials.gov filings (spot-checked for authenticity — e.g. NCT06983743,
a real Phase 1 trial of ERAS-0015 in advanced solid tumors, confirmed against
independent sources) with 3,100 synthetic rows using invented trial/company
names but realistic parameter ranges. All labels — required sample size,
enrollment gap, and power classification — were independently re-derived
from the raw numbers using the stated formula and confirmed to match
exactly across all 3,115 rows, with zero inconsistencies found.
Known class imbalance: the dataset skews toward adequately-powered
examples (2,134 adequately powered vs. 981 underpowered, roughly a 2:1
split) rather than an even balance — worth accounting for when interpreting
edge-case performance.
Known approximation limitation: the closed-form formula used for
ground truth is the standard normal-approximation, not the exact
noncentral-t calculation; for small sample sizes or extreme effect sizes,
professional power-analysis software may compute a slightly different
value than this model reports.
Known validation gap: authenticity of the 15 real anchor rows was
spot-checked for trial identity and design details, but exact enrollment
figures were not independently re-confirmed against the live registry for
every row. Performance has only been measured against Adaption Labs' own
evaluation sets (see Results) — not yet tested against a larger, independent
sample of real trial registry data.
Recommendations
Treat outputs as a rapid pre-screening signal, not a final determination —
borderline classifications, complex trial designs, or high-stakes
funding/publication decisions should be confirmed with exact power-analysis
software or a professional biostatistician.
How to Get Started with the Model
1from transformers import AutoModelForCausalLM, AutoTokenizer
2from peft import PeftModel
3
4base_model_id = "meta-llama/Llama-3.3-70B-Instruct"
5adapter_id = "Gro97/clinical-trial-power-analysis-llama3-3-70b-lora"
6
7tokenizer = AutoTokenizer.from_pretrained(base_model_id)
8base_model = AutoModelForCausalLM.from_pretrained(base_model_id, device_map="auto")
9model = PeftModel.from_pretrained(base_model, adapter_id)
10
11# NOTE: reconstructed to match the verified dataset schema and formula --
12# check against the actual "system" field in your training file before
13# relying on this verbatim, since it was not authored in this session.
14system_prompt = (
15 "You are an expert biostatistician evaluating clinical trial designs. "
16 "Given a trial's test type, sample size, expected effect size, and alpha "
17 "level, compute the statistical power achieved, classify the trial as "
18 "adequately_powered or underpowered (80% power is the standard threshold), "
19 "and state the minimum sample size required for 80% power. Respond only "
20 "with strict JSON with exactly these keys: trial_id, condition, phase, "
21 "actual_enrollment, assumed_effect_size_label, assumed_cohens_d, "
22 "required_n_for_80_percent_power, power_classification, enrollment_gap, "
23 "interpretation."
24)
25user_prompt = (
26 "Trial NCT-EXAMPLE-001, Phase 2, studying a novel treatment for a "
27 "chronic condition. Actual enrollment: 120 participants. Assumed "
28 "effect size: medium (Cohen's d = 0.5)."
29)
30
31messages = [
32 {"role": "system", "content": system_prompt},
33 {"role": "user", "content": user_prompt},
34]
35inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
36output = model.generate(inputs, max_new_tokens=400)
37print(tokenizer.decode(output[0], skip_special_tokens=True))
Training Details
Training Data
Custom-built dataset clinical_trial_power_analysis: 3,115 rows.
| Component | Rows | Description |
|---|
| Real (cited) | 15 | Sourced from actual ClinicalTrials.gov filings, spot-checked for authenticity |
| Synthetic (realistic) | 3,100 | Invented trial identifiers, realistic parameter ranges |
Phase coverage spans Phase 1, Phase 1/Phase 2, Phase 2, Phase 3, Phase 4,
and a small number of observational designs. Effect sizes are roughly
evenly split across small/medium/large (Cohen's d conventions). Power
classification skews toward adequately-powered (2,134) over underpowered
(981) — see Limitations above.
Every row's required_n_for_80_percent_power, enrollment_gap, and
power_classification were independently recomputed from
assumed_cohens_d and actual_enrollment using the stated formula and
confirmed to match exactly, with zero inconsistencies across all 3,115 rows.
Training Procedure
Fine-tuned via Adaption Labs' AutoScientist platform using LoRA.
Training Hyperparameters
- Training regime: see
adapter_config.json in this repository for
the exact LoRA rank/alpha/target-module configuration used for this run.
Speeds, Sizes, Times
Not independently benchmarked by the author; training was run on Adaption
Labs' hosted infrastructure (specific hardware not disclosed to the end
user for this run).
Evaluation
Testing Data, Factors & Metrics
Testing Data
Two evaluation sets, as reported by Adaption Labs' AutoScientist platform:
(1) a held-out split of this project's own dataset, (2) a broader set of
unseen tasks from Adaption's internal Science category benchmark.
Metrics
Pairwise preference win rate against the un-adapted base model, as reported
by Adaption Labs' AutoScientist platform (judge methodology not disclosed
to the end user).
Results
| Evaluation set | Base model win rate | Adapted model win rate |
|---|
| This dataset's held-out samples | 23% | 77% |
| Broader Science category | 24% | 76% |
Summary
The adapter shows a large improvement over the base model both on its own
training distribution (77% win rate) and on the broader Science category
benchmark (76% win rate) — the two numbers being close suggests the gains
generalize well beyond the specific training distribution, consistent with
the model having learned the underlying power-computation skill rather than
memorizing dataset-specific patterns. This is the strongest result across
this author's AutoScientist submissions, consistent with the hypothesis
that tasks requiring genuine numeric computation (rather than pattern
recognition of well-known concepts) produce the largest, most defensible
base-to-adapted gaps.
Environmental Impact
- Hardware Type: Not disclosed by the training platform for this run.
- Hours used: Not disclosed.
- Cloud Provider: Adaption Labs' hosted infrastructure.
- Compute Region: Not disclosed.
- Carbon Emitted: Not calculated.
Technical Specifications
Model Architecture and Objective
LoRA adapter applied to meta-llama/Llama-3.3-70B-Instruct, trained via
supervised fine-tuning to compute and classify statistical power for
clinical/research trial designs.
Compute Infrastructure
Software
- PEFT 0.15.1
- Trained via Adaption Labs' AutoScientist platform
Citation
BibTeX:
1@misc{mboya2026trialpower,
2 author = {Mboya, Grold Otieno},
3 title = {Clinical Trial Power Analysis: A LoRA Adapter for Statistical
4 Power Classification of Trial Designs},
5 year = {2026},
6 howpublished = {AutoScientist Challenge submission, Adaption Labs},
7 url = {https://huggingface.co/Gro97/clinical-trial-power-analysis-llama3-3-70b-lora}
8}
Model Card Contact
See author's Hugging Face profile:
https://huggingface.co/Gro97Framework versions
