A systematic, concept‐based method of developing the exposure measure for drug safety and effectiveness studies
Bibliographic record
Abstract
PURPOSE: In drug safety and effectiveness studies based on secondary data, the choice of an appropriate exposure measure for a given outcome can be challenging. Different measures of exposure can yield different estimates of treatment effect and safety. There is a knowledge gap with respect to developing and refining measures of drug exposure, to ensure that the exposure measure addresses the study question and is suitable for statistical analysis. METHODS: We present a transparent, step-by-step approach to the development of drug exposure measures involving secondary data. This approach would be of interest to students and investigators with initial training in pharmacoepidemiology. We illustrate the approach using a study about Parkinson's disease. RESULTS: We described the exposure specifications according to the study question. Next, we refined the exposure measure by linking it to knowledge about four major concepts in drug safety and effectiveness studies: drug use patterns, duration, timing, and dose. We then used this knowledge to guide the ultimate choice of exposure measure: time-varying, cumulative 6-month exposure to tamsulosin (a drug used to treat prostate hyperplasia). CONCLUSIONS: The proposed approach links exposure specifications to four major concepts in drug safety and effectiveness studies. Formulating subject-matter knowledge about these major concepts provides an avenue to develop the rationale and specifications for the exposure measure.
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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 | 0.239 | 0.318 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".