IMPACT OF DRUG EXPOSURE DEFINITIONS ON OBSERVED ASSOCIATIONS IN PHARMACOEPIDEMIOLOGY RESEARCH
Bibliographic record
Abstract
BACKGROUND - A variety of methods are used to define exposure in pharmacoepidemiologic studies. Although each method has known biases, the relative effect of these biases on an observed association has not been fully examined. OBJECTIVE - To explore the influence of different exposure definitions on estimates, using the association between metformin and all-cause mortality as a proto-typical model. METHODS - New users of oral anti-hyperglycemic drugs were identified using administrative health databases from Alberta, Canada between 1998 and 2010. Drug exposure was described using definitions that are commonly used in observational studies. All analyses included the same covariates of age, gender, and a comorbidity score, and subjects not exposed to metformin served as the reference group. The measure of association was assessed using a Cox Proportional Hazards model for cohort studies and conditional logistic regression for case-control studies. RESULTS - We identified 64,293 new oral anti-hyperglycemic drugs users; mean age 68.9 years, 33,131 (52%) males, and 24,745 (39%) deaths during a mean follow-up of 6 years. In adjusted models, the association between metformin and mortality ranged from 0.23 (95% CI 0.22-0.25) to 0.92 (95% CI 0.88-0.95) reduction. Most metformin exposure definitions, however, provided estimates in the 0.6-0.8 reduction range, aligning with the results of previous observational studies. CONCLUSIONS - The variety of exposure definitions tested in this analysis produced a wide range of associations between metformin and mortality risk. Therefore, pharmacoepidemiological studies should implement sensitivity analyses including at least two exposure definitions to provide more robust and potentially valid study estimates.
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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 | 0.583 | 0.705 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".