De‐Mystifying the Clone‐Censor‐Weight Method for Causal Research Using Observational Data: A Primer for Cancer Researchers
ABSTRACT Background Regulators 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 careful...
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Wiley
2024-12-01
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| Series: | Cancer Medicine |
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| Online Access: | https://doi.org/10.1002/cam4.70461 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Charles E. Gaber |
| collection | DOAJ |
| description | ABSTRACT Background Regulators 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. Methods The 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. Results The 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. Conclusions The 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. |
| format | Article |
| id | doaj-art-87d6c8ef59fa4bdb8b9c2a4edb67efb4 |
| institution | OA Journals |
| issn | 2045-7634 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Medicine |
| spelling | doaj-art-87d6c8ef59fa4bdb8b9c2a4edb67efb42025-08-20T01:58:46ZengWileyCancer Medicine2045-76342024-12-011323n/an/a10.1002/cam4.70461De‐Mystifying the Clone‐Censor‐Weight Method for Causal Research Using Observational Data: A Primer for Cancer ResearchersCharles E. Gaber0Armen A. Ghazarian1Paula D. Strassle2Tatiane B. Ribeiro3Maribel Salas4Camille Maringe5Xabier Garcia‐Albeniz6Richard Wyss7Wei Du8Jennifer L. Lund9Department of Pharmacy Systems, Outcomes, and Policy University of Illinois—Chicago Chicago Illinois USAClinical Safety and Pharmacovigilance Daiichi Sankyo Inc., Basking Ridge New Jersey USADivision of Intramural Research National Institute of Minority Health and Health Disparities, National Institutes of Health Bethesda Maryland USADepartment of Epidemiology School of Public Health, University of São Paulo São Paulo BrazilClinical Safety and Pharmacovigilance Daiichi Sankyo Inc., Basking Ridge New Jersey USAInequalities in Cancer Outcomes Network London School of Hygiene and Tropical Medicine London UKRTI Health Solutions Barcelona SpainDivision of Pharmacoepidemiology and Pharmacoeconomics Brigham and Women's Hospital, Harvard Medical School Boston Massachusetts USASchool of Public Health, Southeast University Nanjing ChinaDepartment of Epidemiology Gillings School of Global Public Health, University of North Carolina at Chapel Hill Chapel Hill North Carolina USAABSTRACT Background Regulators 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. Methods The 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. Results The 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. Conclusions The 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.https://doi.org/10.1002/cam4.70461cancer screeningcancer treatmentcausal inferenceobservational studytarget trial emulation |
| spellingShingle | 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 De‐Mystifying the Clone‐Censor‐Weight Method for Causal Research Using Observational Data: A Primer for Cancer Researchers Cancer Medicine cancer screening cancer treatment causal inference observational study target trial emulation |
| title | De‐Mystifying the Clone‐Censor‐Weight Method for Causal Research Using Observational Data: A Primer for Cancer Researchers |
| title_full | De‐Mystifying the Clone‐Censor‐Weight Method for Causal Research Using Observational Data: A Primer for Cancer Researchers |
| title_fullStr | De‐Mystifying the Clone‐Censor‐Weight Method for Causal Research Using Observational Data: A Primer for Cancer Researchers |
| title_full_unstemmed | De‐Mystifying the Clone‐Censor‐Weight Method for Causal Research Using Observational Data: A Primer for Cancer Researchers |
| title_short | De‐Mystifying the Clone‐Censor‐Weight Method for Causal Research Using Observational Data: A Primer for Cancer Researchers |
| title_sort | de mystifying the clone censor weight method for causal research using observational data a primer for cancer researchers |
| topic | cancer screening cancer treatment causal inference observational study target trial emulation |
| url | https://doi.org/10.1002/cam4.70461 |
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