Publication bias in pharmacogenetics of statin-associated muscle symptoms, an umbrella review with a meta-epidemiological study
Bibliographic record
Abstract
Abstract Background Statin-associated muscle symptoms (SAMS) are a major cause of treatment discontinuation. Adjusting statin dosages for solute carrier organic anion transporter family member 1B1 (SLCO1B1) genotype has been proposed to reduce SAMS. We hypothesized that the association between SLCO1B1 genotype and SAMS is misestimated because of publication bias. Methods We searched for published systematic reviews evaluating the association between SLCO1B1 genotype and SAMS. We collected the odds ratio (OR) of this association in each clinical study. We assessed the presence of publication bias using the visual inspection of a funnel plot and Egger’s test and used the Bayes Factor (BF Publication-bias ) of the Robust Bayesian Meta-Analysis (RoBMA) as a sensitivity analysis. We evaluated the effect of publication bias by comparing qualitatively and quantitatively (ratio of OR [ROR]) OR of the meta-analysis i) uncorrected for potential publication bias (OR Uncorrected ) and ii) corrected using the trim-and-fill (OR Trim&Fill ). We also used the RoBMA (OR RoBMA ) for corrected OR as a sensitivity analysis. Our primary analysis covered the associations between any SLCO1B1 genotype and any statin drug. Secondary analysis focused on SLCO1B1 genotypes and statin drug subgroups. Results We included 8 cohort and 11 case-control studies, totaling 62 OR of three SLCO1B1 genotypes and five statin drugs plus one ‘mixed’ statin treatment. All controls were statin-tolerant patients. 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: i) OR Trim&Fill (1.07 95% CI [0.89–1.30]) and ii) OR RoBMA (1.02 95% CI [1.00–1.33]). The ROR Trim&Fill and the ROR RoBMA suggested that publication bias overestimated the association by 18% and 23%, respectively. The results were similar for the most studied SLCO1B1 genotype, as for simvastatin and atorvastatin. Conclusion The effect of the SLCO1B1 genotype on the risk of developing SAMS is overestimated in the published literature. 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. Clinical perspective What is new? There is significant publication bias in the available literature regarding the association between SLCO1B1 genotype and statin-associated muscle symptoms. The available literature overestimates the importance of the SLCO1B1 genotype on statin-associated muscle symptoms. What are the clinical implications? The cardiovascular benefit of statins might be wrongly lost when adjusting statin therapy with the SLCO1B1 genotype. The effect of publication bias should be considered when writing guidelines.
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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 (narrow)Meta-epidemiology (broad)Research integrity Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | 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 | 0.075 | 0.177 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".