DOI QR코드

DOI QR Code

Duration of Response as Clinical Endpoint: A Quick Guide for Clinical Researchers

  • Seonok Kim (Department of Clinical Epidemiology and Biostatistics, Asan Medical Center) ;
  • Min-Ju Kim (Department of Clinical Epidemiology and Biostatistics, Asan Medical Center) ;
  • Jooae Choe (Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center)
  • Received : 2024.06.20
  • Accepted : 2024.08.17
  • Published : 2024.11.01

Abstract

Keywords

References

  1. Lee BC, Kim GM, Park J, Chung JW, Choi JW, Chun HJ, et al. Comparison of chemoembolization outcomes using 70-150 µm and 100-300 ㎛ drug-eluting beads in treating small hepatocellular carcinoma: a Korean multicenter study. Korean J Radiol 2024;25:715-725  https://doi.org/10.3348/kjr.2024.0231
  2. Shah BD, Ghobadi A, Oluwole OO, Logan AC, Boissel N, Cassaday RD, et al. KTE-X19 for relapsed or refractory adult B-cell acute lymphoblastic leukaemia: phase 2 results of the single-arm, open-label, multicentre ZUMA-3 study. Lancet 2021;398:491-502  https://doi.org/10.1016/S0140-6736(21)01222-8
  3. Levy S, Verbeek WHM, Eskens FALM, van den Berg JG, de Groot DJA, van Leerdam ME, et al. First-line everolimus and cisplatin in patients with advanced extrapulmonary neuroendocrine carcinoma: a nationwide phase 2 single-arm clinical trial. Ther Adv Med Oncol 2022;14:17588359221077088 
  4. Hu C, Wang M, Wu C, Zhou H, Chen C, Diede S. Comparison of duration of response vs conventional response rates and progression-free survival as efficacy end points in simulated immuno-oncology clinical trials. JAMA Netw Open 2021;4:e218175 
  5. U.S. Food and Drug Administration. Clinical trial endpoints for the approval of cancer drugs and biologics: guidance for industry [accessed on July 31, 2024]. Available at: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-trial-endpoints-approval-cancer-drugs-and-biologics 
  6. Pilie PG, Jonasch E. Durable complete response in renal cell carcinoma clinical trials. Lancet 2019;393:2362-2364  https://doi.org/10.1016/S0140-6736(19)30949-3
  7. Weber HJ, Corson S, Li J, Mercier F, Roychoudhury S, Sailer MO, et al. Duration of and time to response in oncology clinical trials from the perspective of the estimand framework. Pharm Stat 2024;23:91-106  https://doi.org/10.1002/pst.2340
  8. Dimopoulos MA, Beksac M, Pour L, Delimpasi S, Vorobyev V, Quach H, et al. Belantamab mafodotin, pomalidomide, and dexamethasone in multiple myeloma. N Engl J Med 2024;391:408-421  https://doi.org/10.1056/NEJMoa2403407
  9. Huang B, Tian L. Utilizing restricted mean duration of response for efficacy evaluation of cancer treatments. Pharm Stat 2022;21:865-878  https://doi.org/10.1002/pst.2198
  10. Huang B, Tian L, Talukder E, Rothenberg M, Kim DH, Wei LJ. Evaluating treatment effect based on duration of response for a comparative oncology study. JAMA Oncol 2018;4:874-876  https://doi.org/10.1001/jamaoncol.2018.0275
  11. Huang B, Tian L, McCaw ZR, Luo X, Talukder E, Rothenberg M, et al. Analysis of response data for assessing treatment effects in comparative clinical studies. Ann Intern Med 2020;173:368-374  https://doi.org/10.7326/M20-0104
  12. Tian L, Jin H, Uno H, Lu Y, Huang B, Anderson KM, et al. On the empirical choice of the time window for restricted mean survival time. Biometrics 2020;76:1157-1166  https://doi.org/10.1111/biom.13237
  13. Luo X, Huang B, Tian L. PBIR: estimating the probability of being in response and related outcomes [accessed on July 31, 2024]. Available at: https://cran.r-project.org/web/packages/PBIR 
  14. Korn EL, Othus M, Chen T, Freidlin B. Assessing treatment efficacy in the subset of responders in a randomized clinical trial. Ann Oncol 2017;28:1640-1647  https://doi.org/10.1093/annonc/mdx197
  15. Matsuyama Y, Morita S. Estimation of the average causal effect among subgroups defined by post-treatment variables. Clin Trials 2006;3:1-9  https://doi.org/10.1191/1740774506cn135oa
  16. Frangakis CE, Rubin DB. Principal stratification in causal inference. Biometrics 2002;58:21-29  https://doi.org/10.1111/j.0006-341X.2002.00021.x
  17. Robins JM. Marginal structural models versus structural nested models as tools for causal inference. In: Halloran ME, Berry D, eds. Statistical models in epidemiology, the environment, and clinical trials. New York: Springer, 2000:95-133 
  18. Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology 2000;11:550-560 https://doi.org/10.1097/00001648-200009000-00011