Meta-Analyses Proved Inconsistent in How Missing Data Were Handled Across Their Included Primary Trials: A Methodological Survey
Bibliographic record
Abstract
Lara A Kahale,1 Assem M Khamis,2 Batoul Diab,1 Yaping Chang,3 Luciane Cruz Lopes,4 Arnav Agarwal,3,5 Ling Li,6 Reem A Mustafa,3,7 Serge Koujanian,8 Reem Waziry,9 Jason W Busse,3,10– 12 Abeer Dakik,1 Lotty Hooft,13 Gordon H Guyatt,3,14 Rob JPM Scholten,13 Elie A Akl1,3 1Clinical Research Institute, American University of Beirut, Beirut, Lebanon; 2Wolfson Palliative Care Research Centre, Hull York Medical School, University of Hull, Hull, UK; 3Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada; 4Pharmaceutical Sciences Post Graduate Course, University of Sorocaba, UNISO, Sorocaba, Sao Paulo, Brazil; 5Department of Medicine, University of Toronto, Toronto, Ontario, Canada; 6Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, People’s Republic of China; 7Departments of Medicine and Biomedical & Health Informatics, University of Missouri-Kansas City, Kansas City, MO, USA; 8Department of Evaluative Clinical Sciences, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada; 9Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; 10Department of Anesthesia, McMaster University, Hamilton, Canada; 11The Michael G. DeGroote National Pain Centre, McMaster University, Hamilton, Canada; 12Chronic Pain Centre of Excellence for Canadian Veterans, Hamilton, Canada; 13Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands; 14Department of Medicine, McMaster University, Hamilton, CanadaCorrespondence: Elie A AklDepartment of Internal Medicine, American University of Beirut Medical Center, P.O. Box: 11-0236; Riad-El-Solh, Beirut 1107– 2020, LebanonTel +961 1 374374Email ea32@aub.edu.lbBackground: How systematic review authors address missing data among eligible primary studies remains uncertain.Objective: To assess whether systematic review authors are consistent in the way they handle missing data, both across trials included in the same meta-analysis, and with their reported methods.Methods: We first identified 100 eligible systematic reviews that included a statistically significant meta-analysis of a patient-important dichotomous efficacy outcome. Then, we successfully retrieved 638 of the 653 trials included in these systematic reviews’ meta-analyses. From each trial report, we extracted statistical data used in the analysis of the outcome of interest to compare with the data used in the meta-analysis. First, we used these comparisons to classify the “analytical method actually used” for handling missing data by the systematic review authors for each included trial. Second, we assessed whether systematic reviews explicitly reported their analytical method of handling missing data. Third, we calculated the proportion of systematic reviews that were consistent in their “analytical method actually used” across trials included in the same meta-analysis. Fourth, among systematic reviews that were consistent in the “analytical method actually used” across trials and explicitly reported on a method for handling missing data, we assessed whether the “analytical method actually used” and the reported methods were consistent.Results: We were unable to determine the “analytical method reviews actually used” for handling missing outcome data among 397 trials. Among the remaining 241, systematic review authors most commonly conducted “complete case analysis” (n=128, 53%) or assumed “none of the participants with missing data had the event of interest” (n=58, 24%). Only eight of 100 systematic reviews were consistent in their approach to handling missing data across included trials, but none of these reported methods for handling missing data. Among seven reviews that did explicitly report their analytical method of handling missing data, only one was consistent in their approach across included trials (using complete case analysis), and their approach was inconsistent with their reported methods (assumed all participants with missing data had the event).Conclusion: The majority of systematic review authors were inconsistent in their approach towards reporting and handling missing outcome data across eligible primary trials, and most did not explicitly report their methods to handle missing data. Systematic review authors should clearly identify missing outcome data among their eligible trials, specify an approach for handling missing data in their analyses, and apply their approach consistently across all primary trials.Keywords: missing data, assumption, randomized controlled trial, systematic review, meta-analysis
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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 | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.551 | 0.413 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.025 | 0.005 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.024 | 0.009 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".