Publication bias in pharmacogenetics of statin-associated muscle symptoms: A meta-epidemiological study
Bibliographic record
Abstract
Background and aims Statin-associated muscle symptoms (SAMS) are a major cause of treatment discontinuation. Clinical Pharmacogenetics Implementation Consortium (CPIC) recommend dose adjustment for statin treatment according to known SLCO1B1 genotype to reduce SAMS. We hypothesized that the association between SLCO1B1 genotype and SAMS is misestimated because of publication bias. Methods We searched published systematic reviews on the association between SLCO1B1 genotype and SAMS. To assessed publication bias, we used funnel plot visual inspection, Egger's test, and the Bayes Factor (BF Publication-bias ) from Robust Bayesian Meta-Analysis (RoBMA). We compared the odds ratios (OR Uncorrected ) from meta-analyses before and after correcting for publication bias using trim-and-fill (OR Trim&Fill ) and RoBMA (OR RoBMA ) methods. Results We included 8 cohort and 11 case-control studies, totaling 62 OR of three SLCO1B1 genotypes and six statin drugs. In the primary analysis, the funnel plot was suggestive of publication bias, confirmed by Egger's test ( p= 0.001) and RoBMA (BF Publication-bias = 18). Correcting the estimate for publication bias resulted in loss of the association, from a significant OR Uncorrected (1.31 95%CI [1.13–1.53]) to corrected ORs suggesting no difference: OR Trim&Fill (1.07 95%CI [0.89–1.30]) and OR RoBMA (1.02 95%CI [1.00–1.33]). This suggested that publication bias overestimated the association by 18 % and 23 %, respectively. Similar results were found for genotype rs4149056, simvastatin and atorvastatin. Conclusions The effect of the SLCO1B1 genotype on the risk of developing SAMS is overestimated in the published literature, especially rs4149056. This could lead prescribers to incorrectly decreasing statin doses or even avoiding statin use, leading to a loss of the potential cardiovascular benefit of statins.
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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: Reporting · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | MetaresearchMeta-epidemiology (narrow)Meta-epidemiology (broad) Domain: Methods · Genre: Review 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 | 0.074 | 0.131 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.037 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".