Bias Estimation In Study Design: A Meta-Epidemiological Analysis of Transcatheter Versus Surgical Aortic Valve Replacement
Bibliographic record
Abstract
Abstract Objective: To estimate the bias associated with specific nonrandomized study attributes among studies comparing transcatheter aortic valve implantation with surgical aortic valve replacement for the treatment of severe aortic stenosis.Data sources and study selection: We searched 7 databases from inception to June 2017: Medline, Medline In-Process/ePubs, Embase, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Scopus, and Web of Science. We included all RCTs and nonrandomized studies that reported outcomes of interest.Data extraction and synthesis: We categorized studies according to study design, and evaluated 41 nonrandomized study attributes as potential sources of bias. We calculated odds ratios (OR) and other effect measures with 95% confidence intervals (CI) using random effects models.Main outcomes: One month postoperative mortality, and length of stay. Bias was defined as the difference in estimates of treatment effects between nonrandomized studies and high quality (low risk of bias) RCTs, which were considered to provide “gold standard” estimates.Results: We included 6 RCTs and 87 nonrandomized studies. Surgical risk scores were similar for comparison groups in RCTs, but were higher for patients having transcatheter aortic valve implantation in nonrandomized studies. Nonrandomized studies underestimated the benefit of transcatheter aortic valve implantation compared with RCTs. For example, nonrandomized studies without adjustment estimated a higher risk of postoperative mortality for transcatheter aortic valve implantation compared with surgical aortic valve replacement (OR 1.43 [95% CI, 1.26 to 1.62]) than high quality RCTs (OR 0.78 [95% CI, 0.54 to 1.11). Nonrandomized studies using propensity score matching (OR 1.13 [95% CI, 0.85 to 1.52]) and regression modelling (OR 0.68 [95% CI, 0.57 to 0.81]) to adjust results estimated treatment effects closer to high quality RCTs. Nonrandomized studies describing losses to follow-up estimated treatment effects that were significantly closer to high quality RCT than nonrandomized studies that did not.Conclusion: Studies with different attributes produce different estimates of treatment effects. Study design attributes related to the completeness of follow-up may explain biased treatment estimates in nonrandomized studies, as in the case of aortic valve replacement where high-risk patients were preferentially selected for the newer (transcatheter) procedure.
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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)Meta-epidemiology (narrow) Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
| gpt | Meta-epidemiology (narrow)Meta-epidemiology (broad)Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
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 | 0.275 | 0.465 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.020 | 0.049 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".