Use of modified intention-to-treat analysis in studies of direct oral anticoagulants and risk of selection bias: a systematic review
Bibliographic record
Abstract
BACKGROUND: Following their evaluation in randomised controlled trials (RCTs), direct oral anticoagulants (DOACs) have replaced warfarin for stroke prevention in atrial fibrillation (AF), and treatment and prevention of venous thromboembolism (VTE). To avoid selection bias, it is recommended that RCTs use an intention-to-treat (ITT) analysis strategy. OBJECTIVE: The objective of this study was to systematically review and compare reported analytical strategies, the proportion of randomised patients included in analyses and the reasons for participant exclusions. STUDY SELECTION: A systematic search of PubMed, EMBASE and the Cochrane library for phase III trials of DOACs was conducted. Titles and abstracts were screened for relevance by two independent reviewers. Patient population, intervention studied, number of patients included in randomisation and analysis, reasons for exclusions from analysis and trial conclusions were extracted from each article. FINDINGS: Twenty-nine studies were included, five were about stroke prevention in AF, 10 about VTE treatment and 14 about thromboprophylaxis. Trials of AF and VTE treatment had low proportions of postrandomisation exclusions (around 1%). In contrast, surgical and medical thromboprophylaxis trials excluded almost 30% of participants postrandomisation. This was in spite of authors' claims of using an ITT or modified ITT approach. Higher exclusion proportions in these trials were associated with non-clinically defined primary outcomes and incomplete outcome assessments. CONCLUSIONS: Clinicians should be aware that the level of evidence in favour of DOAC use for thromboprophylaxis is weak due to high rates of postrandomisation exclusions and risks of selection bias.
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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 | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Meta-epidemiology (narrow)Meta-epidemiology (broad)Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | 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 | 0.337 | 0.595 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.035 | 0.037 |
| Bibliometrics | 0.026 | 0.020 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".