Missing outcome data in randomised clinical trials of psychological interventions: a review of published trial reports in major psychiatry journals
Bibliographic record
Abstract
BACKGROUND: Missing outcome data can pose a serious threat to the validity of randomised clinical trial results. We aimed to study the extent of missing outcome data in randomised clinical trials of psychological interventions. METHODS: We performed a retrospective study of randomised clinical trial reports of psychological interventions published in World Psychiatry, JAMA Psychiatry, Lancet Psychiatry, American Journal of Psychiatry, British Journal of Psychiatry, or Psychotherapy and Psychosomatics from 2017 to 2022. We assessed the proportion of missing outcome data, whether missing data patterns differed between types of outcomes, participants, intervention lengths, and psychological intervention types, how missing outcome data were handled in the statistical analyses, and whether trialists discussed missing outcome data in the discussion section of the manuscript. RESULTS: We identified 182 randomised clinical trials (233 primary outcomes), of which 206 outcomes (88.4%) were assessed at high risk of bias due to missing data. The overall mean percentage of missing outcome data was 18.3% (95% confidence interval (CI): 16.7-20%) for all outcomes. The percentages of missing data were 18.9% (95% CI: 17.1-20.6%; 180 outcomes) for symptom severity scales and 1.8% (95% CI: 2.3-3.3%; 6 outcomes) for 'hard' binary outcomes. Trials including participants with borderline personality disorder had the highest percentage of missing outcome data (33.1%; 95% CI: 22.3-43.9%) compared with other psychiatric disorders. Fisher's exact test showed that intervention lengths and psychological intervention types were associated with the proportion of missing outcome data (p < 0.001), but there were no clear patterns. CONCLUSION: Missing outcome data is a considerable problem in randomised clinical trials of psychological interventions, and trialists should consider the corresponding methodological limitations in the design and analysis to reduce the risk of bias due to missing outcome data. CLINICAL TRIAL REGISTRATION NUMBER: Not applicable.
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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 | 0.240 | 0.589 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.025 | 0.026 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".