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Record W4286247398 · doi:10.1080/19466315.2022.2081601

Clinical and Statistical Perspectives on the ICH E9(R1) Estimand Framework Implementation

2022· article· en· W4286247398 on OpenAlex
Alexei Ionan, Miya Paterniti, Devan V. Mehrotra, John Scott, Bohdana Ratitch, Sylva H. Collins, Shanti Gomatam, Lei Nie, Kaspar Rufibach, Frank Bretz

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueStatistics in Biopharmaceutical Research · 2022
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsBayer (Canada)
Fundersnot available
KeywordsAddendumClinical trialPolitical scienceMedicine

Abstract

fetched live from OpenAlex

The ICH E9 (R1) Addendum on “Estimands and Sensitivity Analysis in Clinical Trials (Step 4)” was finalized in November 2019 and subsequently implemented by many regulatory agencies, including FDA (May 2021). This article is based on a session organized to cover experience implementing the estimand framework, including its use, impact on drug/biologic development, common challenges and ways to address them, as well as keys to productive interdisciplinary collaboration.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.034
metaresearch head score (Gemma)0.211
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.281
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0340.211
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0090.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.833
GPT teacher head0.752
Teacher spread0.080 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it