Reporting, handling and assessing the risk of bias associated with missing participant data in systematic reviews: a methodological survey
Bibliographic record
Abstract
OBJECTIVES: To describe how systematic reviewers are reporting missing data for dichotomous outcomes, handling them in the analysis and assessing the risk of associated bias. METHODS: We searched MEDLINE and the Cochrane Database of Systematic Reviews for systematic reviews of randomised trials published in 2010, and reporting a meta-analysis of a dichotomous outcome. We randomly selected 98 Cochrane and 104 non-Cochrane systematic reviews. Teams of 2 reviewers selected eligible studies and abstracted data independently and in duplicate using standardised, piloted forms with accompanying instructions. We conducted regression analyses to explore factors associated with using complete case analysis and with judging the risk of bias associated with missing participant data. RESULTS: Of Cochrane and non-Cochrane reviews, 47% and 7% (p<0.0001), respectively, reported on the number of participants with missing data, and 41% and 9% reported a plan for handling missing categorical data. The 2 most reported approaches for handling missing data were complete case analysis (8.5%, out of the 202 reviews) and assuming no participants with missing data had the event (4%). The use of complete case analysis was associated only with Cochrane reviews (relative to non-Cochrane: OR=7.25; 95% CI 1.58 to 33.3, p=0.01). 65% of reviews assessed risk of bias associated with missing data; this was associated with Cochrane reviews (relative to non-Cochrane: OR=6.63; 95% CI 2.50 to 17.57, p=0.0001), and the use of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology (OR=5.02; 95% CI 1.02 to 24.75, p=0.047). CONCLUSIONS: Though Cochrane reviews are somewhat less problematic, most Cochrane and non-Cochrane systematic reviews fail to adequately report and handle missing data, potentially resulting in misleading judgements regarding risk of bias.
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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 | 0.824 | 0.905 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.021 |
| Bibliometrics | 0.036 | 0.031 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".