The intention-to-treat approach in randomized controlled trials: Are authors saying what they do and doing what they say?
Bibliographic record
Abstract
BACKGROUND: Intention-to-treat (ITT) is an approach to the analysis of randomized controlled trials (RCT) in which patients are analyzed as randomized regardless of the treatment actually received. PURPOSE: To ascertain the proportion of RCT reporting the use of intention-to-treat and the accuracy of that report and to examine the distribution and analysis of missing data for the studies reporting an ITT analysis. METHOD: We conducted a cross-sectional literature review of RCTs reported in 10 medical journals in 2002. All articles were assessed using a standardized form. Two evaluators independently reviewed a 10% sample of articles to assess reliability. Subsequently, one evaluator reviewed the remaining articles. The proportion of articles reporting the use of ITT was calculated. Among these, the proportion of articles that ;analyzed patients as randomized' and the proportion and analysis of missing data were evaluated using standardized definitions. RESULTS: Of the 403 articles, 249 (62%) reported the use of ITT. Among these, available patients were clearly analyzed as randomized in 192 (77%). Authors used a modified ITT in 23 (9%); clearly violated a major component of ITT in 17 (7%), and the approach used was unclear in 17 (7%). More than 60% of articles had missing data in their primary analysis. Few articles reported a strategy for missing data. The main reason for missing data was loss to follow-up. LIMITATIONS: A single evaluator evaluated most articles, but the high concordance obtained during the inter-rater evaluation suggests that the assessments were consistent. In addition, the small spectrum of journals limits generalizability. Finally, there could be a difference between what was reported and what was performed. CONCLUSIONS: This study emphasizes that authors use the label ;intention-to-treat' quite differently. The most common use refers to the analysis of all available subjects as randomized regardless of the missing data aspect.
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How this classification was reachedexpand
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 | 0.758 | 0.891 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.013 |
| Bibliometrics | 0.030 | 0.035 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".