Outcome pre-specification requires sufficient detail to guard against outcome switching in clinical trials: a case study
Bibliographic record
Abstract
BACKGROUND: Pre-specification of outcomes is an important tool to guard against outcome switching in clinical trials. However, if the outcome is not sufficiently clearly defined, then different definitions could be applied and analysed, with only the most favourable result reported. METHODS: In order to assess the impact that differing outcome definitions could have on treatment effect estimates, we re-analysed data from TRIGGER, a cluster randomised trial comparing two red blood cell transfusion strategies for patients with acute upper gastrointestinal bleeding. We varied several aspects of the definition of further bleeding: (1) the criteria for what constitutes a further bleeding episode; (2) how further bleeding is assessed; and (3) the time-point at which further bleeding is measured. RESULTS: There were marked discrepancies in the estimated odds ratios (OR) (range 0.23-0.94) and corresponding P values (range < 0.001-0.89) between different outcome definitions. At the extremes, differing outcome definitions led to markedly different conclusions; one definition led to very little evidence of a treatment effect (OR = 0.94, 95% confidence interval [CI] = 0.37-2.40, P = 0.89), while another led to very strong evidence of a treatment effect (OR = 0.23, 95% CI = 0.11-0.50, P < 0.001). CONCLUSIONS: Outcomes should be pre-specified in sufficient detail to avoid differing definitions being analysed and only the most favourable result being reported. TRIAL REGISTRATION: Clinical Trials.gov, NCT02105532 . Registered on 7 April 2014.
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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 | MetaresearchResearch integrity Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Case report | low |
| gpt | Metaresearch Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.846 | 0.733 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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".