A Systematic Survey of the Optimal Strategy for Dealing With Missing Binary Outcomes in Simulation Studies of Randomized Controlled Trials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIM: To summarize the optimal strategies for dealing with missing binary outcome data (MBOD) in randomized controlled trials (RCTs) as informed by simulation studies, and to summarize the quality of reporting in these studies. METHODS: To identify simulation studies comparing at least two strategies to deal with MBOD and evaluating their performance (bias, coverage and power), we searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials via Ovid, Web of Science, and JSTOR from their inception up to December 20, 2023. We evaluated reporting quality using established criteria for simulation studies in medical statistics. We summarized data using descriptive statistics and a narrative synthesis. RESULTS: Our search identified 29,460 citations, of which five proved eligible. Multiple imputation (MI), investigated in five studies, showed consistently good performance in all domains tested for missing completely at random (MCAR) and missing at random (MAR) but with important limitations in missing not at random (MNAR). Complete case analysis (CCA), investigated in four studies of which three addressed model-based CCA, performed well in bias and coverage under MAR and MCAR, but less well for MNAR. One study reported that non-model-based CCA performed poorly with respect to bias under MAR. Non-model-based single imputation, investigated in two studies, showed consistently poor performance across all domains tested for MAR, MCAR and MNAR. One study reported that model-based single imputation performed well with respect to bias under MAR. Regarding reporting quality, all studies reported the aims, dependence of simulated data sets, scenarios and statistical methods evaluated, number of simulations performed, justification of data generation and criteria used to evaluate the simulation performance. None of the studies reported the starting seeds, random number generators and failures occurring during simulation. CONCLUSIONS: Simulation studies address methods to deal with MBOD in RCTs, provided evidence that the MI approach is superior with respect to bias and coverage compared with CCA. Non-model-based single imputation generally performed poorly.
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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 | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | 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 | 0.058 | 0.529 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it