Skip to main content

Advertisement

Log in

The Clone-Censor-Weight Method in Pharmacoepidemiologic Research: Foundations and Methodological Implementation

  • Published:
Current Epidemiology Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Over the past two decades, cautions have repeatedly been issued about the potential for immortal time bias in observational studies. This bias is particularly relevant in fields that routinely leverage large secondary databases such as pharmacoepidemiology. A variety of study design and analysis tools exist to prevent immortal time bias. A newer approach, the clone-censor-weight method, successfully eliminates the possibility for immortal time while maintaining a target trial emulation framework. We review the rationale for the clone-censor-weight approach, outline the steps for implementation, compare the method to other tools for handling immortal time, and describe how the method has been used in a convenience sample of applied studies.

Recent Findings

The clone-censor-weight method offers distinct advantages over other methods for handling immortal time bias, namely the retention of a target trial emulation framework. The clone-censor-weight method has been used across numerous substantive areas within pharmacoepidemiology, with variation in how the method is implemented.

Summary

The clone-censor-weight method represents a rigorous approach for emulating a target trial and preventing immortal time bias. Many pharmacoepidemiologic studies would benefit from appropriate use of this method, though future work should illuminate the impact of different implementation choices.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Suissa S. Effectiveness of inhaled corticosteroids in chronic obstructive pulmonary disease: immortal time bias in observational studies. Am J Respir Crit Care Med. 2003;168(1):49–53. https://doi.org/10.1164/rccm.200210-1231OC.

    Article  PubMed  Google Scholar 

  2. Suissa S. Immortal time bias in pharmacoepidemiology. Am J Epidemiol. 2008;167(4):492–9. https://doi.org/10.1093/aje/kwm324.

    Article  PubMed  Google Scholar 

  3. Platt RW, Hutcheon JA, Suissa S. Immortal time bias in epidemiology. Curr Epidemiol Rep. 2019;6(1):23–7. https://doi.org/10.1007/s40471-019-0180-5.

    Article  Google Scholar 

  4. •• Cain LE, Robins JM, Lanoy E, Logan R, Costagliola D, Hernán MA. When to Start Treatment? A Systematic Approach to the Comparison of Dynamic Regimes Using Observational Data. Int J Biostat. 2010;6(2). https://doi.org/10.2202/1557-4679.1212. This paper was one of the first papers to describe the use of the clone-censor-weight technique, though it did not have that moniker at the time. It offers a deep statistical appendix and lays out steps involved in implementation of the method, along with drawing equivalency between the IPTW and IPCW approaches to the weighting step.

  5. Hernán MA. How to estimate the effect of treatment duration on survival outcomes using observational data. BMJ. Published online February 1, 2018;k182. https://doi.org/10.1136/bmj.k182

  6. Hernán MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available: table 1. Am J Epidemiol. 2016;183(8):758–64. https://doi.org/10.1093/aje/kwv254.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Hernán MA, Sauer BC, Hernández-Díaz S, Platt R, Shrier I. Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. J Clin Epidemiol. 2016;79:70–5. https://doi.org/10.1016/j.jclinepi.2016.04.014.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Zhou Z, Rahme E, Abrahamowicz M, Pilote L. Survival bias associated with time-to-treatment initiation in drug effectiveness evaluation: a comparison of methods. Am J Epidemiol. 2005;162(10):1016–23. https://doi.org/10.1093/aje/kwi307.

    Article  PubMed  Google Scholar 

  9. Targownik LE, Suissa S. Understanding and avoiding immortal-time bias in gastrointestinal observational research. Am J Gastroenterol. 2015;110(12):1647–50. https://doi.org/10.1038/ajg.2015.210.

    Article  PubMed  Google Scholar 

  10. Jackson BE, Greenup RA, Strassle PD, et al. Understanding and identifying immortal-time bias in surgical health services research: an example using surgical resection of stage IV breast cancer. Surg Oncol. 2021;37:101539. https://doi.org/10.1016/j.suronc.2021.101539.

    Article  PubMed  Google Scholar 

  11. •• Duchesneau ED, Jackson BE, Webster-Clark M, et al. The timing, the treatment, the question: comparison of epidemiologic approaches to minimize immortal time bias in real-world data using a surgical oncology example. Cancer Epidemiol Biomarkers Prev. 2022;31(11):2079–2086. https://doi.org/10.1158/1055-9965.EPI-22-0495. This paper is a critical read for understanding how to interpret the results from competing approaches that address immortal time bias.

  12. Shintani AK, Girard TD, Eden SK, Arbogast PG, Moons KGM, Ely EW. Immortal time bias in critical care research: application of time-varying Cox regression for observational cohort studies. Crit Care Med. 2009;37(11):2939–45. https://doi.org/10.1097/CCM.0b013e3181b7fbbb.

    Article  PubMed  PubMed Central  Google Scholar 

  13. The European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP). Guide on Methodological Standards in Pharmacoepidemiology (Revision 11). https://www.encepp.eu/standards_and_guidances/documents/01.ENCePPMethodsGuideRev.11.pdf. Accessed 15 Jan 2023.

  14. Wang SV, Pinheiro S, Hua W, et al. STaRT-RWE: structured template for planning and reporting on the implementation of real world evidence studies. BMJ. Published online January 12, 2021:m4856. https://doi.org/10.1136/bmj.m4856

  15. Wang SV, Pottegård A, Crown W, et al. HARmonized protocol template to enhance reproducibility of hypothesis evaluating real-world evidence studies on treatment effects: a good practices report of a joint ISPE/ISPOR task force. Pharmacoepidemiol Drug Saf. 2023;32(1):44–55. https://doi.org/10.1002/pds.5507.

    Article  PubMed  Google Scholar 

  16. Hernán MA, Wang W, Leaf DE. Target trial emulation: a framework for causal inference from observational data. JAMA. 2022;328(24):2446. https://doi.org/10.1001/jama.2022.21383.

    Article  PubMed  Google Scholar 

  17. Fu EL. Target trial emulation to improve causal inference from observational data: what, why, and how? J Am Soc Nephrol. 2023;34(8):1305–14. https://doi.org/10.1681/ASN.0000000000000152.

    Article  PubMed  Google Scholar 

  18. Kuehne F, Arvandi M, Hess LM, et al. Causal analyses with target trial emulation for real-world evidence removed large self-inflicted biases: systematic bias assessment of ovarian cancer treatment effectiveness. J Clin Epidemiol. 2022;152:269–80. https://doi.org/10.1016/j.jclinepi.2022.10.005.

    Article  PubMed  Google Scholar 

  19. Xu S, Ross C, Raebel MA, Shetterly S, Blanchette C, Smith D. Use of stabilized inverse propensity scores as weights to directly estimate relative risk and its confidence intervals. Value Health. 2010;13(2):273–7. https://doi.org/10.1111/j.1524-4733.2009.00671.x.

    Article  PubMed  Google Scholar 

  20. Dekker FW, De Mutsert R, Van Dijk PC, Zoccali C, Jager KJ. Survival analysis: time-dependent effects and time-varying risk factors. Kidney Int. 2008;74(8):994–7. https://doi.org/10.1038/ki.2008.328.

    Article  PubMed  Google Scholar 

  21. Newman NB, Osmundson EC. Practical demonstration of time bias with administration of adjuvant therapy in lung cancer. Lung Cancer. 2021;157:75–8. https://doi.org/10.1016/j.lungcan.2021.04.019.

    Article  CAS  PubMed  Google Scholar 

  22. Suissa S, Azoulay L. Metformin and the risk of cancer. Diabetes Care. 2012;35(12):2665–73. https://doi.org/10.2337/dc12-0788.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Didelez V, Stensrud MJ. On the logic of collapsibility for causal effect measures. Biom J. 2022;64(2):235–42. https://doi.org/10.1002/bimj.202000305.

    Article  MathSciNet  PubMed  Google Scholar 

  24. Hernán MA. The hazards of hazard ratios. Epidemiology. 2010;21(1):13–5. https://doi.org/10.1097/EDE.0b013e3181c1ea43.

    Article  MathSciNet  PubMed  PubMed Central  Google Scholar 

  25. Uno H, Claggett B, Tian L, et al. Moving beyond the hazard ratio in quantifying the between-group difference in survival analysis. J Clin Oncol. 2014;32(22):2380–5. https://doi.org/10.1200/JCO.2014.55.2208.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Uno H, Wittes J, Fu H, et al. Alternatives to hazard ratios for comparing the efficacy or safety of therapies in noninferiority studies. Ann Intern Med. 2015;163(2):127–34. https://doi.org/10.7326/M14-1741.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Murray EJ, Caniglia EC, Swanson SA, Hernández-Díaz S, Hernán MA. Patients and investigators prefer measures of absolute risk in subgroups for pragmatic randomized trials. J Clin Epidemiol. 2018;103:10–21. https://doi.org/10.1016/j.jclinepi.2018.06.009.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Jones M, Fowler R. Immortal time bias in observational studies of time-to-event outcomes. J Crit Care. 2016;36:195–9. https://doi.org/10.1016/j.jcrc.2016.07.017.

    Article  PubMed  Google Scholar 

  29. Gleiss A, Oberbauer R, Heinze G. An unjustified benefit: immortal time bias in the analysis of time-dependent events. Transpl Int. 2018;31(2):125–30. https://doi.org/10.1111/tri.13081.

    Article  PubMed  Google Scholar 

  30. Anderson JR, Cain KC, Gelber RD. Analysis of survival by tumor response. J Clin Oncol. 1983;1(11):710–9. https://doi.org/10.1200/JCO.1983.1.11.710.

    Article  CAS  PubMed  Google Scholar 

  31. Dafni U. Landmark analysis at the 25-year landmark point. Circ Cardiovasc Qual Outcomes. 2011;4(3):363–71. https://doi.org/10.1161/CIRCOUTCOMES.110.957951.

    Article  MathSciNet  PubMed  Google Scholar 

  32. Kahan BC, Cro S, Li F, Harhay MO. Eliminating ambiguous treatment effects using estimands. Am J Epidemiol. 2023;192(6):987–94. https://doi.org/10.1093/aje/kwad036.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Lundberg I, Johnson R, Stewart BM. What is your estimand? Defining the target quantity connects statistical evidence to theory. Am Sociol Rev. 2021;86(3):532–65. https://doi.org/10.1177/00031224211004187.

    Article  Google Scholar 

  34. Murray EJ, Caniglia EC, Petito LC. Causal survival analysis: a guide to estimating intention-to-treat and per-protocol effects from randomized clinical trials with non-adherence. Res Methods Med Health Sci. 2021;2(1):39–49. https://doi.org/10.1177/2632084320961043.

    Article  Google Scholar 

  35. Dong H, Robison LL, Leisenring WM, Martin LJ, Armstrong GT, Yasui Y. Estimating the burden of recurrent events in the presence of competing risks: the method of mean cumulative count. Am J Epidemiol. 2015;181(7):532–40. https://doi.org/10.1093/aje/kwu289.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Gaber CE, Edwards JK, Lund JL, Peery AF, Richardson DB, Kinlaw AC. Inverse probability weighting to estimate exposure effects on the burden of recurrent outcomes in the presence of competing events. Am J Epidemiol. 2023;192(5):830–9. https://doi.org/10.1093/aje/kwad031.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Petito LC, García-Albéniz X, Logan RW, et al. Estimates of overall survival in patients with cancer receiving different treatment regimens: emulating hypothetical target trials in the Surveillance, Epidemiology, and End Results (SEER)–medicare linked database. JAMA Netw Open. 2020;3(3):e200452. https://doi.org/10.1001/jamanetworkopen.2020.0452.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Chase D, Perhanidis J, Gupta D, Kalilani L, Golembesky A, González-Martín A. Real-world outcomes following first-line treatment in patients with advanced ovarian cancer with multiple risk factors for disease progression who received maintenance therapy or active surveillance. Oncol Ther. 2023;11(2):245–61. https://doi.org/10.1007/s40487-023-00227-6.

    Article  PubMed  PubMed Central  Google Scholar 

  39. García-Albéniz X, Hsu J, Bretthauer M, Hernán MA. Effectiveness of screening colonoscopy to prevent colorectal cancer among medicare beneficiaries aged 70 to 79 years: a prospective observational study. Ann Intern Med. 2017;166(1):18. https://doi.org/10.7326/M16-0758.

    Article  PubMed  Google Scholar 

  40. •• García-Albéniz X, Hernán MA, Logan RW, Price M, Armstrong K, Hsu J. Continuation of annual screening mammography and breast cancer mortality in women older than 70 years. Ann Intern Med. 2020;172(6):381. https://doi.org/10.7326/M18-1199. This paper implements both the sequence-of-trials approach and cloning at baseline and offers excellent details on the construction of the time-varying inverse probability of treatment weights.

  41. Buranupakorn T, Thangsuk P, Patumanond J, Phinyo P. Emulation of a target trial to evaluate the causal effect of palliative care consultation on the survival time of patients with hepatocellular carcinoma. Cancers. 2021;13(5):992. https://doi.org/10.3390/cancers13050992.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Emilsson L, García-Albéniz X, Logan RW, Caniglia EC, Kalager M, Hernán MA. Examining bias in studies of statin treatment and survival in patients with cancer. JAMA Oncol. 2018;4(1):63. https://doi.org/10.1001/jamaoncol.2017.2752.

    Article  PubMed  Google Scholar 

  43. Gaber CE, Shaheen NJ, Edwards JK, et al. Trimodality therapy vs definitive chemoradiation in older adults with locally advanced esophageal cancer. JNCI Cancer Spectr. 2022;6(6):pkac069. https://doi.org/10.1093/jncics/pkac069.

    Article  PubMed  PubMed Central  Google Scholar 

  44. •• Maringe C, Majano SB, Exarchakou A, Smith M, Rachet B. Reflections on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data. Int J Epidemiol. 2020;0(0). This paper provides excellent background on the intuition for the steps of clone-censor-weight and additionally comes with an example code.

  45. • Fu EL, Evans M, Clase CM, et al. Stopping renin-angiotensin system inhibitors in patients with advanced CKD and risk of adverse outcomes: a nationwide study. J Am SocNephrol. 2021;32(2):424–435. https://doi.org/10.1681/ASN.20200506822020050682. This paper provides a detailed account of how weights are constructed and offers a helpful visual in the appendix for what the weights are achieving.

  46. Fu EL, Evans M, Carrero JJ, et al. Timing of dialysis initiation to reduce mortality and cardiovascular events in advanced chronic kidney disease: nationwide cohort study. BMJ. Published online November 29, 2021:e066306. https://doi.org/10.1136/bmj-2021-066306

  47. Chen A, Ju C, Mackenzie IS, et al. Impact of beta-blockers on mortality and cardiovascular disease outcomes in patients with obstructive sleep apnoea: a population-based cohort study in target trial emulation framework. Lancet Reg Health - Eur. 2023;33:100715. https://doi.org/10.1016/j.lanepe.2023.100715.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Xu Y, Fu EL, Trevisan M, et al. Stopping renin-angiotensin system inhibitors after hyperkalemia and risk of adverse outcomes. Am Heart J. 2022;243:177–86. https://doi.org/10.1016/j.ahj.2021.09.014.

    Article  CAS  PubMed  Google Scholar 

  49. Caniglia EC, Rojas-Saunero LP, Hilal S, et al. Emulating a target trial of statin use and risk of dementia using cohort data. Neurology. 2020;95(10):e1322–32. https://doi.org/10.1212/WNL.0000000000010433.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Xie Y, Bowe B, Al-Aly Z. Molnupiravir and risk of hospital admission or death in adults with covid-19: emulation of a randomized target trial using electronic health records. BMJ. Published online March 7, 2023:e072705. https://doi.org/10.1136/bmj-2022-072705

  51. Boyne DJ, Cheung WY, Hilsden RJ, et al. Association of a shortened duration of adjuvant chemotherapy with overall survival among individuals with stage III colon cancer. JAMA Netw Open. 2021;4(3):e213587. https://doi.org/10.1001/jamanetworkopen.2021.3587.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Evans RN, Harris J, Rogers CA, Macgowan AP. The effect of duration of therapy for treatment of Staphylococcus aureus blood stream infection: an application of cloning to deal with immortal-time bias in an analysis of data from a cohort study (BSI-FOO). J Antimicrob Chemother. 2023;78(1):196–204. https://doi.org/10.1093/jac/dkac374.

    Article  CAS  Google Scholar 

  53. Trevisi L, Hernán MA, Mitnick CD, et al. Effectiveness of bedaquiline use beyond six months in patients with multidrug-resistant tuberculosis. Am J Respir Crit Care Med. 2023;207(11):1525–32. https://doi.org/10.1164/rccm.202211-2125OC.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Lu Y, Gehr AW, Meadows RJ, et al. Timing of adjuvant chemotherapy initiation and mortality among colon cancer patients at a safety-net health system. BMC Cancer. 2022;22(1):593. https://doi.org/10.1186/s12885-022-09688-w.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Garcia-Albeniz X, Chan JM, Paciorek A, et al. Immediate versus deferred initiation of androgen deprivation therapy in prostate cancer patients with PSA-only relapse. An observational follow-up study. Eur J Cancer. 2015;51(7):817–24. https://doi.org/10.1016/j.ejca.2015.03.003.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Wanis KN, Madenci AL, Hernán MA, Murray EJ. Adjusting for adherence in randomized trials when adherence is measured as a continuous variable: an application to the lipid research clinics coronary primary prevention trial. Clin Trials. 2020;17(5):570–5. https://doi.org/10.1177/1740774520920893.

    Article  PubMed  Google Scholar 

  57. Birnie K, Tomson C, Caskey FJ, et al. Comparative effectiveness of dynamic treatment strategies for medication use and dosage: emulating a target trial using observational data. Epidemiology. 2023;34(6):879–87. https://doi.org/10.1097/EDE.0000000000001649.

    Article  PubMed  Google Scholar 

  58. Moura LMVR, Yan Z, Donahue MA, et al. No short-term mortality from benzodiazepine use post-acute ischemic stroke after accounting for bias. J Clin Epidemiol. 2023;154:136–45. https://doi.org/10.1016/j.jclinepi.2022.12.013.

    Article  PubMed  Google Scholar 

  59. Caniglia EC, Sabin C, Robins JM, et al. When to monitor CD4 cell count and HIV RNA to reduce mortality and AIDS-defining illness in virologically suppressed HIV-positive persons on antiretroviral therapy in high-income countries: a prospective observational study. JAIDS J Acquir Immune Defic Syndr. 2016;72(2):214–21. https://doi.org/10.1097/QAI.0000000000000956.

    Article  CAS  PubMed  Google Scholar 

  60. Cain LE, Logan R, Robins JM, et al. When to initiate combined antiretroviral therapy to reduce mortality and AIDS-defining illness in HIV-infected persons in developed countries: an observational study. Ann Intern Med. 2011;154(8):509–15. https://doi.org/10.7326/0003-4819-154-8-201104190-00001.

    Article  PubMed  Google Scholar 

  61. Boyne DJ, Brenner DR, Gupta A, et al. Head-to-head comparison of FOLFIRINOX versus gemcitabine plus nab-paclitaxel in advanced pancreatic cancer: a target trial emulation using real-world data. Ann Epidemiol. 2023;78:28–34. https://doi.org/10.1016/j.annepidem.2022.12.005.

    Article  PubMed  Google Scholar 

  62. Murray EJ, Hernán MA. Improved adherence adjustment in the Coronary Drug Project. Trials. 2018;19(1):158. https://doi.org/10.1186/s13063-018-2519-5.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Don EE, Mijatovic V, Van Eekelen R, Huirne JAF. The effect of myomectomy on reproductive outcomes in patients with uterine fibroids: a retrospective cohort study. Reprod Biomed Online. 2022;45(5):970–8. https://doi.org/10.1016/j.rbmo.2022.05.025.

    Article  PubMed  Google Scholar 

  64. Lagerberg T, Matthews AA, Zhu N, Fazel S, Carrero JJ, Chang Z. Effect of selective serotonin reuptake inhibitor treatment following diagnosis of depression on suicidal behaviour risk: a target trial emulation. Neuropsychopharmacology. Published online July 28, 2023. https://doi.org/10.1038/s41386-023-01676-3

Download references

Author information

Authors and Affiliations

Authors

Contributions

C.G. searched the literature and wrote the main manuscript text. K.H. searched the literature, helped create table, and provided edits and comments during review of manuscript draft. S.K. searched the literature, helped create table, and provided edits and comments during review of manuscript draft. J.L. provided edits and comments during review of manuscript draft. T.L. provided edits and comments during review of manuscript draft. E.M. provided edits and comments during review, including drafting new content for the main manuscript text.

Corresponding author

Correspondence to Charles E. Gaber.

Ethics declarations

Competing interests

The authors declare no competing interests.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gaber, C.E., Hanson, K.A., Kim, S. et al. The Clone-Censor-Weight Method in Pharmacoepidemiologic Research: Foundations and Methodological Implementation. Curr Epidemiol Rep (2024). https://doi.org/10.1007/s40471-024-00346-2

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40471-024-00346-2

Keywords

Navigation