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pith:2026:6LZI6IQXZBUJNRWYIMKUM727K2
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Creating treatment and component hierarchies in component network meta-analysis

Adriani Nikolakopoulou, Audrey B\'eliveau, Augustine Wigle, Lifeng Lin

A workflow identifies uniquely estimable relative effects to build valid treatment hierarchies in component network meta-analysis.

arxiv:2605.15142 v1 · 2026-05-14 · stat.ME

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4 Citations open
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Claims

C1strongest claim

We present a step-by-step workflow for answering treatment hierarchy questions in both frequentist and Bayesian CNMA, including explicitly identifying the uniquely estimable relative effects.

C2weakest assumption

That identifying uniquely estimable relative effects is always feasible from the network structure and sufficient to construct valid non-misleading hierarchies without further assumptions on connectivity or data.

C3one line summary

A workflow for treatment hierarchies in CNMA that identifies uniquely estimable relative effects in frequentist and Bayesian settings.

References

133 extracted · 133 resolved · 0 Pith anchors

[1] Ades, A. E. and Davies, Annabel L. and Phillippo, David M. and Pedder, Hugo and Thom, Howard and Downing, Beatrice and Caldwell, Deborah M. and Welton, Nicky J. , year = 2025, journal =. Treatment Rec 2025
[2] Ades, A. E. and Welton, Nicky J. and Dias, Sofia and Phillippo, David M. and Caldwell, Deborah M. , year = 2024, journal =. Twenty Years of Network Meta-Analysis: 2024
[3] Electronic Journal of Statistics , volume =
[4] Al Mohamad, Diaa and Goeman, Jelle J. and. Simultaneous. Biometrics , volume =
[5] and Premji, Zahra A
Receipt and verification
First computed 2026-05-17T21:40:25.556870Z
Last reissued 2026-05-17T21:57:18.877080Z
Builder pith-number-builder-2026-05-17-v1
Signature unsigned_v0
Schema pith-number/v1.0

Canonical hash

f2f28f2217c86896c6d84315467f5f5688684ba979822a29ea2f6e47af307ebc

Aliases

arxiv: 2605.15142 · arxiv_version: 2605.15142v1 · pith_short_12: 6LZI6IQXZBUJ · pith_short_16: 6LZI6IQXZBUJNRWY · pith_short_8: 6LZI6IQX
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/6LZI6IQXZBUJNRWYIMKUM727K2 \
  | 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: f2f28f2217c86896c6d84315467f5f5688684ba979822a29ea2f6e47af307ebc
Canonical record JSON
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    "primary_cat": "stat.ME",
    "submitted_at": "2026-05-14T17:46:51Z",
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