Pharmacoepidemiology II: The Nested Case‐Control Study—A Novel Approach in Pharmacoepidemiologic Research
Bibliographic record
Abstract
This article on pharmacoepidemiology, the second of two parts, is a more focused discussion of the methodology of cohort studies and case-control studies, the basic methodologies of which were discussed in part I. The nested case-control study incorporates the strengths of both the cohort and case-control studies but may alleviate some of the methodologic challenges inherent in both types of studies. In a nested case-control study, a cohort of individuals is followed during certain time periods until a certain outcome is reached. The analysis is conducted as a case-control study in which cases are matched to only a sample of control subjects. Matching allows for control of potential confounding variables such as age, calendar time, and disease duration. Also, the time dependency of an exposure can be quantified without complicated statistical techniques. Matching the cases and controls by time allows the investigator to stratify exposure based on current, past, or intermittent use. By using the principles of epidemiology, the nested case-control study allows for the control of confounding variables, as well as better quantification of time-dependent exposures.
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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 | 0.025 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".