The Prevalent New-user Design for Studies With no Active Comparator: The Example of Statins and Cancer
Bibliographic record
Abstract
BACKGROUND: Observational studies evaluating the effect of a drug versus "non-use" are challenging, mainly when defining cohort entry for non-users. The approach using successive monthly cohorts to emulate the randomized trial can be perceived as somewhat opaque and complex. Alternatively, the prevalent new-user design can provide a potentially simpler more transparent emulation. This design is illustrated in the context of statins and cancer incidence. METHODS: We used the Clinical Practice Research Datalink to identify a cohort of subjects with low-density lipoprotein cholesterol level <5 mmol/L. We used a prevalent new-user design, matching each statin initiator to a non-user from the same time-based exposure set on time-conditional propensity scores with all subjects followed for 10 years for cancer incidence. We estimated the hazard ratio and 95% confidence interval (CI) of cancer incidence with statin use versus non-use using a Cox proportional hazards model, and the results were compared with those using the method of successive monthly cohorts. RESULTS: The study cohort included 182,073 statin initiators and 182,073 matched non-users. The hazard ratio of any cancer after statin initiation versus non-use was 1.01 (95% CI = 0.98, 1.04), compared with 1.04 (95% CI = 1.02, 1.06) under the successive monthly cohorts approach. We estimated similar effects for specific cancers. CONCLUSION: Using the prevalent new-user design to emulate a randomized trial when compared to "non-use" led to results comparable with the more complex successive monthly cohorts approach. The prevalent new-user design emulates the trial in a potentially more intuitive and palpable manner, providing simpler data presentations in line with those portrayed in a classical trial while producing comparable results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.281 | 0.368 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".