Potential impact on estimated treatment effects of information lost to follow-up in randomised controlled trials (LOST-IT): systematic review
Bibliographic record
Abstract
OBJECTIVE: To assess the reporting, extent, and handling of loss to follow-up and its potential impact on the estimates of the effect of treatment in randomised controlled trials. DESIGN: Systematic review. We calculated the percentage of trials for which the relative risk would no longer be significant under a number of assumptions about the outcomes of participants lost to follow-up. DATA SOURCES: Medline search of five top general medical journals, 2005-07. ELIGIBILITY CRITERIA: Randomised controlled trials that reported a significant binary primary patient important outcome. RESULTS: Of the 235 eligible reports identified, 31 (13%) did not report whether or not loss to follow-up occurred. In reports that did give the relevant information, the median percentage of participants lost to follow-up was 6% (interquartile range 2-14%). The method by which loss to follow-up was handled was unclear in 37 studies (19%); the most commonly used method was survival analysis (66, 35%). When we varied assumptions about loss to follow-up, results of 19% of trials were no longer significant if we assumed no participants lost to follow-up had the event of interest, 17% if we assumed that all participants lost to follow-up had the event, and 58% if we assumed a worst case scenario (all participants lost to follow-up in the treatment group and none of those in the control group had the event). Under more plausible assumptions, in which the incidence of events in those lost to follow-up relative to those followed-up is higher in the intervention than control group, results of 0% to 33% trials were no longer significant. CONCLUSION: Plausible assumptions regarding outcomes of patients lost to follow-up could change the interpretation of results of randomised controlled trials published in top medical journals.
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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 | 0.531 | 0.839 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.009 | 0.011 |
| 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".