Evidence map›Paper›PMID 39642890›Full record

ArticleCancer medicine2024

De-Mystifying the Clone-Censor-Weight Method for Causal Research Using Observational Data: A Primer for Cancer Researchers.

Charles E Gaber, Armen A Ghazarian, Paula D Strassle, Tatiane B Ribeiro, Maribel Salas, Camille Maringe, Xabier Garcia-Albeniz, Richard Wyss, Wei Du, Jennifer L Lund

Abstract read
In one paragraph

Article in Cancer medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Active Monitoring versus Surgical Excision for Atypical Ductal Hyperplasia.Cancer prevention research (Philadelphia, Pa.) · 2026
    Observational
  5. Mycophenolate and azathioprine in fibrotic interstitial lung disease.American journal of respiratory and critical care medicine · 2026
    Observational
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Review
  12. Article
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  14. Article
  15. Article
  16. Article
  17. Review
  18. Observational
  19. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Charles E GaberDepartment of Pharmacy Systems, Outcomes, and Policy, University of Illinois-Chicago, Chicago, Illinois, USA.ORCID 0000-0002-6752-2968
Armen A GhazarianClinical Safety and Pharmacovigilance, Daiichi Sankyo Inc.,, Basking Ridge, New Jersey, USA.
Paula D StrassleDivision of Intramural Research, National Institute of Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland, USA.ORCID 0000-0002-1072-1560
Tatiane B RibeiroDepartment of Epidemiology, School of Public Health, University of São Paulo, São Paulo, Brazil.
Maribel SalasClinical Safety and Pharmacovigilance, Daiichi Sankyo Inc.,, Basking Ridge, New Jersey, USA.
Camille MaringeInequalities in Cancer Outcomes Network, London School of Hygiene and Tropical Medicine, London, UK.
Xabier Garcia-AlbenizRTI Health Solutions, Barcelona, Spain.
Richard WyssDivision of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Wei DuSchool of Public Health, Southeast University, Nanjing, China.
Jennifer L LundDepartment of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.ORCID 0000-0002-1108-0689

Funding

Virology Research Program (Program 4)P30CA016086 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Deborah F. Tate · 1985 to 2026
$201.5M
UNIV OF NORTH CAROLINA CLINICAL NUTRITION RESEARCH UNITP30DK056350 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Venkata Saroja Voruganti · 1999 to 2026
$31.6M
International Society for PharmacoepidemiologyNCI NIH HHS P30 CA016086NCI NIH HHS P30-CA16086NIDDK NIH HHS P30 DK056350
6 · The paper itself

Abstract

backgroundRegulators and oncology healthcare providers are increasingly interested in using observational studies of real-world data (RWD) to complement clinical evidence from randomized controlled trials for informed decision-making. To generate valid evidence, RWD studies must be carefully designed to avoid systematic biases. The clone-censor-weight (CCW) method has been proposed to address immortal time and other time-related biases.

methodsThe objective of this manuscript is to de-mystify the CCW method for cancer researchers by describing and presenting its core components in an accessible and digestible format, using visualizations and examples from cancer-relevant studies. The CCW method has been applied in diverse settings, including investigations of the effects of surgery within a certain time after cancer diagnosis, the continuation of annual screening mammography, and chemotherapy duration on survival.

resultsThe method handles complex data wherein the treatment group to which an individual belongs is unknown at the start of follow-up. The three steps of the CCW method involve cloning or duplicating the patient population and assigning one clone to each treatment strategy, artificially censoring the clones when their observed data are inconsistent with the assigned strategy and weighting the cloned and censored population to address selection bias created by the artificial censoring.

conclusionsThe CCW method is a powerful tool for designing RWD studies in cancer that are free from time-related biases and successfully, to the extent possible, emulate features of a randomized clinical trial.

Indexed as

NeoplasmsObservational Studies as TopicCausalityHumansResearch Designcancer screeningcancer treatmentcausal inferenceobservational studytarget trial emulation

Identifiers

PMID39642890
PMCPMC11623977

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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.