Conducting descriptive epidemiology and causal inference studies using observational data: A 10-point primer for stroke researchers
Bibliographic record
Abstract
Routinely-collected health data and emerging data-linkage capabilities provide researchers and clinicians with rich opportunities to answer important research questions by conducting observational studies. We provide stroke researchers with 10 important points to consider and implement to ensure the validity and interpretability of descriptive epidemiology and causal inference studies based on observational data. We discuss different types of observational studies and biases that may arise in such studies. We review types of causal effects and the use of Target Trial emulation and Directed Acyclic Graphs to improve validity of observational studies. We also illustrate appropriate and inappropriate use of covariate adjustment for the analyses of observational studies and review the methods for estimating the effects of treatments, interventions, and exposures in causal inference studies. Finally, we provide recommendations for clinical researchers and journal manuscript reviewers in stroke domain and beyond for the appropriate use and reporting of these methods.
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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 | 0.302 | 0.486 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.014 | 0.029 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.015 | 0.039 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".