Use of continuous exposure variables when examining dose-dependent pharmacological effects – Application to the association between exposure to higher statin doses and the incidence of diabetes
Bibliographic record
Abstract
BACKGROUND: Many observational studies have found an association between the exposure to statins and the increased risk of diabetes, mostly through the use of intent-to-treat (ITT) like exposure measure (EM). ITT like EM may not adequately reflect the mechanism of action by which statins could cause diabetes. OBJECTIVES: To determine if continuous EMs can more accurately reflect the mechanism of action by which statins and incidence of diabetes would be associated than ITT like EM. METHODS: We obtained a cohort of 404,129 diabetes-free incident statin users from the Québec public drug insurance plan. Patients dispensed with a drug used in the treatment of diabetes or diagnosed with diabetes within 2-years follow-up were defined as cases. Controls were randomly matched to each case on the index date. Three EMs were tested. EM 1: exposure to a high versus low dose statin at baseline (ITT like); EM 2: cumulative standardized statin dose (cSSD) at the index date; and EM 3: cSSD in the 180 days prior to the index date. The optimal EM was selected based upon each model's Akaike's information criterion (AIC). Conditional logistic regressions were used to calculate conditional OR and model AIC. RESULTS: All three EMs identified an increased risk of diabetes among patients exposed to higher statin doses. Model AIC identified EM 3 as the best EM for this association. CONCLUSIONS: Our results indicate that higher statin doses increase the risk of diabetes but favour a cumulative reversible diabetogenic effect of statins.
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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 | 0.130 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".