Impact of missing participant data for dichotomous outcomes on pooled effect estimates in systematic reviews: a protocol for a methodological study
Bibliographic record
Abstract
BACKGROUND: There is no consensus on how authors conducting meta-analysis should deal with trial participants with missing outcome data. The objectives of this study are to assess in Cochrane and non-Cochrane systematic reviews: (1) which categories of trial participants the systematic review authors consider as having missing participant data (MPD), (2) how trialists reported on participants with missing outcome data in trials, (3) whether systematic reviewer authors actually dealt with MPD in their meta-analyses of dichotomous outcomes consistently with their reported methods, and (4) the impact of different methods of dealing with MPD on pooled effect estimates in meta-analyses of dichotomous outcomes. METHODS/DESIGN: We will conduct a methodological study of Cochrane and non-Cochrane systematic reviews. Eligible systematic reviews will include a group-level meta-analysis of a patient-important dichotomous efficacy outcome, with a statistically significant effect estimate. Teams of two reviewers will determine eligibility and subsequently extract information from each eligible systematic review in duplicate and independently, using standardized, pre-piloted forms. The teams will then use a similar process to extract information from the trials included in the meta-analyses of interest. We will assess first which categories of trial participants the systematic reviewers consider as having MPD. Second, we will assess how trialists reported on participants with missing outcome data in trials. Third, we will compare what systematic reviewers report having done, and what they actually did, in dealing with MPD in their meta-analysis. Fourth, we will conduct imputation studies to assess the effects of different methods of dealing with MPD on the pooled effect estimates of meta-analyses. We will specifically calculate for each method (1) the percentage of systematic reviews that lose statistical significance and (2) the mean change of effect estimates across systematic reviews. DISCUSSION: The impact of different methods of dealing with MPD on pooled effect estimates will help judge the associated risk of bias in systematic reviews. Our findings will inform recommendations regarding what assumptions for MPD should be used to test the robustness of meta-analytical results.
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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 | 0.405 | 0.585 |
| Meta-epidemiology (narrow) | 0.009 | 0.011 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.025 | 0.026 |
| Insufficient payload (model declined to judge) | 0.068 | 0.023 |
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".