Pith Number

pith:EEJRLPA3

pith:2026:EEJRLPA32ZDDHHK4QPD5DOPGLS

not attested not anchored not stored refs resolved

Digital Twins as Synthetic Controls in Single-Arm Trials

Aaron M. Smith, Daniele Bertolini, Franklin Fuller, Jonathan R. Walsh, Run Zhuang

Digital twins from machine learning models can serve as synthetic controls in single-arm clinical trials

arxiv:2605.12832 v1 · 2026-05-12 · stat.AP · cs.LG · stat.ML

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2 Internet Archive

3 Author claim open · sign in to claim

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Claims

C1strongest claim

outcome-model-based synthetic control arms are an important tool for single-arm trials... we focus on digital twins, personalized predictions of disease progression generated from machine learning models trained on historical datasets, which naturally leverage these flexible approaches.

C2weakest assumption

That machine learning models trained on historical datasets will produce accurate and unbiased predictions of disease progression for patients in the current single-arm trial, even when populations differ in unmeasured ways.

C3one line summary

Digital twins from outcome models trained on historical data can function as robust synthetic controls in single-arm trials, supported by doubly robust estimators, power formulas, and reanalyses in ALS and Huntington's disease.

References

29 extracted · 29 resolved · 0 Pith anchors

[1] Hern´ an and James M 2020

[2] Imbens and Donald B 2015

[3] Donald B. Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies.Journal of Educational Psychology, 66(5):688–701, 1974 1974

[4] Long story short: Omitted variable bias in causal machine learning, 2024 2024

[5] Placebo effects: from the neurobiological paradigm to translational implica- tions.Neuron, 84(3):623–637, November 2014 2014

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T03:09:12.076596Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

211315bc1bd646339d5c83c7d1b9e65ca70f6633254effb9a9e9398c1990b2ac

Aliases

arxiv: 2605.12832 · arxiv_version: 2605.12832v1 · doi: 10.48550/arxiv.2605.12832 · pith_short_12: EEJRLPA32ZDD · pith_short_16: EEJRLPA32ZDDHHK4 · pith_short_8: EEJRLPA3

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/EEJRLPA32ZDDHHK4QPD5DOPGLS \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 211315bc1bd646339d5c83c7d1b9e65ca70f6633254effb9a9e9398c1990b2ac

Canonical record JSON

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    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "stat.AP",
    "submitted_at": "2026-05-12T23:58:48Z",
    "title_canon_sha256": "1022778fb3a18373efcbf43e657c9d041c372ebfd99048a56416610511653379"
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