Abstract 19486: Does the Association Between Adherence to Statin Medications and Mortality Depend on the Measurement Approach? A Retrospective Cohort Study
Bibliographic record
Abstract
Background: Optimal adherence to statin medications is associated with reduced mortality rates. However, it is not clear if the estimated benefits of statin adherence are influenced by the method used to measure adherence. Objective: To contrast the association between all-cause mortality and statin adherence when two different measurement approaches are used: (i.e., fixed summary measurement versus repeated measurement). Methods: A retrospective cohort study was conducted using administrative data from Saskatchewan, Canada between 1994 and 2008. Eligible individuals received a statin prescription following discharge from a hospitalization for acute coronary syndrome (ACS). Adherence was measured using proportion of days covered (PDC) expressed either as: 1) a fixed summary measure, or 2) as a repeatedly measured covariate. Cox proportional hazards models were used to test the association between each adherence measure and mortality after covariate adjustment. Results: Among 9,051 eligible individuals, optimal adherence (≥80%) modeled with a fixed summary measure was not associated with mortality (adjusted HR 0.97, 95% CI 0.86 to 1.09). In contrast, optimal adherence defined by the repeated measures approach was associated with a 25% reduction in the risk of death (adjusted HR 0.75, 95% CI 0.67 to 0.85). Conclusions: Unlike summary measure, the repeated measures approach appears to provide a significant reduction of all-cause mortality of adherence to statins. This effect may be a result of the repeated measures approach being more sensitive, or more prone to survival bias. Therefore, we recommend comparing different measurement approaches whenever possible.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".