Evidence map›Paper›PMID 40976652›Full record

ArticlePharmacoepidemiology and drug safety2025

Demystifying Clone-Censor-Weighting to Studying Treatment Initiation Windows: An Example Using Publicly Available Synthetic Medicare Claims Data.

Michael Webster-Clark, Yi Li, Sophie Dell'Aniello, Robert W Platt

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Article in Pharmacoepidemiology and drug safety, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Michael Webster-ClarkDepartment of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
Yi LiDepartment of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada.
Sophie Dell'AnielloLady Davis Institute, Jewish General Hospital, Montreal, Quebec, Canada.
Robert W PlattDepartment of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada.ORCID https://orcid.org/0000-0002-5981-8443

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClone-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.

objectiveProvide a tutorial on CCW for comparing initiation windows and illustrate the causal contrasts underlying such comparisons.

methodsWe 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.

resultsWe 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.

conclusionsThough 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.

Indexed as

MedicareMyocardial InfarctionPlatelet Aggregation InhibitorsAgedAged, 80 and overAspirinClopidogrelDatabases, FactualFemaleHumansKaplan-Meier EstimateMaleTiclopidineTime FactorsUnited StatesAspirinClopidogrelPlatelet Aggregation InhibitorsTiclopidineclone‐censor‐weightingselection biastarget trial emulationtreatment timingtreatment windows

Identifiers

PMID40976652
PMCPMC12450615

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.