Interpreting trial results following use of different intention-to-treat approaches for preventing attrition bias: a meta-epidemiological study protocol
Bibliographic record
Abstract
INTRODUCTION: When participants drop out of randomised clinical trials, as frequently happens, the intention-to-treat (ITT) principle does not apply, potentially leading to attrition bias. Data lost from patient dropout/lack of follow-up are statistically addressed by imputing, a procedure prone to bias. Deviations from the original definition of ITT are referred to as modified intention-to-treat (mITT). As yet, the impact of the potential bias associated with mITT has not been assessed. Our objective is to investigate potential bias and disadvantages of performing mITT and evaluate possible concerns when executing different mITT approaches in meta-analyses. METHODS AND ANALYSIS: Using meta-epidemiology on randomised trials considered less prone to bias (ie, good internal validity) and assessing biological or targeted agents in patients with rheumatoid arthritis, we will meta-analyse data from 10 biological and targeted drugs based on collections of trials that would correspond to 10 individual meta-analyses. ETHICS AND DISSEMINATION: This study will enhance transparency for evaluating mITT treatment effects described in meta-analyses. The intended audience will include healthcare researchers, policymakers and clinicians. Results of the study will be disseminated by peer-review publication. PROTOCOL REGISTRATION: In PROSPERO CRD42013006702, 11. December 2013.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | MetaresearchMeta-epidemiology (narrow)Meta-epidemiology (broad) Domain: Methods · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.366 | 0.524 |
| Meta-epidemiology (narrow) | 0.009 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.023 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.020 | 0.018 |
| Insufficient payload (model declined to judge) | 0.034 | 0.014 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".