Bias and Loss to Follow‐Up in Cardiovascular Randomized Trials: A Systematic Review
Bibliographic record
Abstract
Background Loss to follow-up (LTFU) is common in randomized controlled trials. However, its potential impact on primary outcomes from cardiovascular randomized controlled trials is not known. Methods and Results We conducted a prospective systematic review (PROSPERO: CRD42019121959) for randomized controlled trials published in 8 leading journals over 5 years from January 2014 to December 2018. Extent, reporting, and handling of LTFU data were recorded, and the proportion of a trial's primary outcome results that lose statistical significance was calculated after making plausible assumptions for the intervention and control arms. These assumptions could drive differential treatment effects between the groups considering relative event incidence between LTFU participants and those included in the primary outcome. We identified 117 randomized controlled trials of which 91 (78%) trials reported LTFU, 23 (20%) reported no LTFU, and 3 (3%) trials did not report on whether LTFU occurred. The median percentage of study participants lost to follow-up was 2% (interquartile range, 0.33%-5.3%). Only 10 trials (9%) had a low cluster of risk factors for impairment in trial quality. The percentage of trials losing statistical significance varied from 2% when the relative event incidence for LTFU between the randomized groups was 1 for the intervention arm and 1.5 for the control arm to 16% when the relative event incidence was 3 for the intervention arm and 1 for the control arm. Conclusions Almost 1 in 6 (16%) cardiovascular randomized trials published in leading journals may have a change in the primary outcome if plausible assumptions are made about differential event rates of participants lost to follow up. There is scope for improvement arising from LTFU in randomized trials in cardiovascular medicine. Registration URL: https://www.crd.york.ac.uk/prospero; Unique identifier: CRD42019121959.
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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 | 0.290 | 0.594 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.019 | 0.019 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".