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Record W3204186164 · doi:10.1016/j.simpa.2021.100147

InteractionR: An R package for full reporting of effect modification and interaction

2021· article· en· W3204186164 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSoftware Impacts · 2021
Typearticle
Languageen
FieldMathematics
TopicAdvanced Causal Inference Techniques
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec - Santé
KeywordsBootstrapping (finance)Multiplicative functionDelta methodPercentileR packageInteractionComputer scienceConfidence intervalVariance (accounting)Scale (ratio)Variance componentsAdditive modelSimple (philosophy)EconometricsStatisticsData miningMathematicsMachine learningEstimator

Abstract

fetched live from OpenAlex

<h2>Abstract</h2> Effect modification and/or Interaction are frequently assessed in epidemiological research. However, in most cases, authors do not present sufficient information for the readers to fully assess the extent and significance of interaction on both additive and multiplicative scale. Also, due to being readily available in most software, the delta method has proliferated in the literature for the estimation of confidence intervals (CIs) for measures of additive interactions; despite its well documented poor performance compared to alternative methods. We introduce <i>interactionR</i>, an open-source R package with user-friendly functions that ensures full reporting of effect modification or interaction based on recommended guidelines. In addition to the simple asymptotic delta method, the package also allows for estimation of CIs for additive interaction measures using the variance recovery and percentile bootstrapping methods.

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)0.001
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.207
GPT teacher head0.487
Teacher spread0.280 · 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