Systematic review and meta-analysis of cardiovascular outcome trials: importance of <i>post hoc</i> studies
Bibliographic record
Abstract
This commentary refers to ‘Mineralocorticoid receptor antagonists and atrial fibrillation: a meta-analysis of clinical trials’, by A. Oraii et al., https://doi.org/10.1093/eurheartj/ehad811 and the discussion piece ‘Double counting in meta-analyses: a statistical complication in cardiovascular medicine trials’, by S. Mehta and E.C. Martinez, https://doi.org/10.1093/eurheartj/ehaf086. We thank Mehta et al.1 for their interest in our study and their concern about accuracy of our meta-analysis results. We share the authors’ concern about risk of double-counting in contemporary meta-analyses of cardiovascular outcome trials. However, our recently published meta-analysis on the effect of mineralocorticoid receptor antagonists (MRA) on cardiovascular outcomes does not include any double-counting of patients and we confirm the validity of the pooled effect estimates. In this discussion forum, we will explain the methodology used to derive event rates for our meta-analysis to prevent future confusion and serve as a practical guide for other researchers in similar future endeavours. Composite endpoints (e.g. cardiovascular death or heart failure [HF] hospitalization) are commonly used in cardiovascular outcome trials and therefore frequently included in systematic reviews and meta-analyses to derive pooled treatment effects. However, these composite endpoints may not be readily available in the main publication of the eligible randomized controlled trials. In our case, the main publications of FIDELIO-DKD and FIGARO-DKD trials that investigated cardio-renal effects of finerenone in patients with diabetic kidney disease reported information on several cardiovascular outcomes, but no information was presented for the composite endpoint of cardiovascular death or HF hospitalization.2,3 Rather than excluding these two studies from our meta-analysis of the composite endpoint, we sought additional sources of information by exploring post hoc analyses and pooled individual-patient data analyses to identify any relevant information that might be available.
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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 | 0.485 | 0.758 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.023 | 0.027 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.011 | 0.004 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".