Demystifying Clone‐Censor‐Weighting to Study Treatment Initiation Windows: An Example Using Publicly Available Synthetic Medicare Claims Data
Bibliographic record
Abstract
BACKGROUND: Clone-censor-weighting (CCW) can compare treatment regimens that are initially indistinguishable (such as starting treatment within specific time windows) without using landmarks or creating immortal time. The causal contrasts estimated in these cases and the analyses themselves can become quite complex; however. OBJECTIVE: Provide a tutorial on CCW for comparing initiation windows and illustrate the causal contrasts underlying such comparisons. METHODS: We identified patients with myocardial infarctions without past aspirin or clopidogrel use in Medicare's synthetic public data files. We assigned "clones" to three regimens: (1) initiation within 30 days; (2) initiation within 90 days; or 3) initiation from 30 to 90 days. Clones were censored when deviating from their assigned regimen by failing to initiate treatment in time or by initiating treatment too early. We addressed informative censoring using inverse probability of censoring weights (IPCW), calculated weighted 180-day risks of re-hospitalization or death using Kaplan-Meier methods, and visualized the portion of the population exposed during the first 90 days to compare exposure distributions underlying regimens. RESULTS: We identified 1589 patients experiencing myocardial infarction with no past medication use. 15% initiated within 30 days and 26% initiated between 30 and 90 days. After IPCW, the 180-day outcome risk was 40.2% in the 30-day regimen, 35.7% in the 90-day regimen, and 35.2% in the 30-to-90 day regimen. CONCLUSIONS: Though CCW can be complex to implement and the effects it estimates can vary substantially across study populations that initiate treatments at different times, it is a useful tool for contrasting initiation windows.
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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 | 0.026 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".