References
- Sehn LH, Salles G. Diffuse large B-cell lymphoma. N Engl J Med. 2021;384(9):842–858. doi:10.1056/NEJMra2027612.
- Ferlay J, Ervik M, Lam F, et al. Global cancer observatory: cancer today. 2020 [cited Dec 2022]. Available from: https://gco.iarc.fr/today/home.
- National Cancer Institute (NCI) Surveillance Epidemiology and End Results Program. Cancer stat facts: NHL – diffuse large B-cell lymphoma (DLBCL). 2019 [cited Dec 2022]. Available from: https://seer.cancer.gov/statfacts/html/dlbcl.html.
- Liu Y, Barta SK. Diffuse large B-cell lymphoma: 2019 update on diagnosis, risk stratification, and treatment. Am J Hematol. 2019;94(5):604–616. doi:10.1002/ajh.25460.
- Camus V, Tilly H. Polatuzumab vedotin, an anti-CD79b antibody-drug conjugate for the treatment of relapsed/refractory diffuse large B-cell lymphoma. Future Oncol. 2021;17(2):127–135. doi:10.2217/fon-2020-0675.
- Tilly H, Morschhauser F, Sehn LH, et al. Polatuzumab vedotin in previously untreated diffuse large B-cell lymphoma. N Engl J Med. 2022;386(4):351–363. doi:10.1056/NEJMoa2115304.
- Gallacher D, Kimani P, Stallard N. Extrapolating parametric survival models in health technology assessment: a simulation study. Med Decis Making. 2021;41(1):37–50. doi:10.1177/0272989X20973201.
- R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2021.
- Bennett I, Gregory J, Smith S, et al. flexsurvPlus: provides functions to perform survival analysis for economic models. R Package version 1.06. 2023. https://roche.github.io/flexsurvPlus/main/authors.html
- Yu B, Peng Y. Mixture cure models for multivariate survival data. J Comput Stat Data Anal. 2008;52(3):1524–1532. doi:10.1016/j.csda.2007.04.018.
- Human Mortality Database. 2022 [cited Dec 2022]. Available from: https://www.mortality.org/.
- Felizzi F, Paracha N, Pöhlmann J, et al. Mixture cure models in oncology: a tutorial and practical guidance. Pharmacoecon Open. 2021;5(2):143–155. doi:10.1007/s41669-021-00260-z.
- Gibson E, Koblbauer I, Begum N, et al. Modelling the survival outcomes of immuno-oncology drugs in economic evaluations: a systematic approach to data analysis and extrapolation. Pharmacoeconomics. 2017;35(12):1257–1270. doi:10.1007/s40273-017-0558-5.
- Davies A, Briggs AH, Schneider JE, et al. The ends justify the mean: outcome measures for estimating the value of new cancer therapies. Health Outcomes Res Med. 2012;3(1):e25–e26. doi:10.1016/j.ehrm.2012.01.001.
- Royston P, Lambert PC. Flexible parametric survival analysis using stata: beyond the Cox model. A Stata Press Publication. College Station (TX): StataCorp LP; 2011.
- Rutherford MJ, Crowther MJ, Lambert PC. The use of restricted cubic splines to approximate complex hazard functions in the analysis of time-to-event data: a simulation study. J Stat Comput Simul. 2015;85(4):777–793. doi:10.1080/00949655.2013.845890.
- Gray J, Sullivan T, Latimer NR, et al. Extrapolation of survival curves using standard parametric models and flexible parametric spline models: comparisons in large registry cohorts with advanced cancer. Med Decis Making. 2021;41(2):179–193. doi:10.1177/0272989X20978958.
- Jackson CH. flexsurv: a platform for parametric survival modeling in R. J Stat Softw. 2016;70:i08. doi:10.18637/jss.v070.i08.
- Royston P, Parmar M. Flexible parametric proportional‐hazards and proportional‐odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat Med. 2002;21(15):2175–2197. doi:10.1002/sim.1203.
- Sehn LH, Martelli M, Trněný M, et al. A randomized, open-label, phase III study of obinutuzumab or rituximab plus CHOP in patients with previously untreated diffuse large B-cell lymphoma: final analysis of GOYA. J Hematol Oncol. 2020;13(1):71. doi:10.1186/s13045-020-00900-7.
- Haute Autorité de Santé. Choices in methods for economic evaluation—HAS. 2020 [cited Aug 2023]. Available from: https://www.has-sante.fr/upload/docs/application/pdf/2020-11/methodological_guidance_2020_-choices_in_methods_for_economic_evaluation.pdf
- Canadian Agency for Drugs and Technologies in Health. Guidelines for the economic evaluation of health technologies: Canada. 2006 [cited Aug 2023]. Available from: https://www.cadth.ca/guidelines-economic-evaluation-health-technologies-canada-4th-edition.
- Australian Government Department of Health. Guidelines for preparing a submission to the Pharmaceutical Benefits Advisory Committee. Version 5.0. 2016 [cited Aug 2023]. Available from: https://pbac.pbs.gov.au/content/information/files/pbac-guidelines-version-5.pdf.
- Palmer S, Borget I, Friede T, et al. A guide to selecting flexible survival models to inform economic evaluations of cancer immunotherapies. Value Health. 2023;26(2):185–192. doi:10.1016/j.jval.2022.07.009.
- Bullement A, Willis A, Amin A, et al. Evaluation of survival extrapolation in immuno-oncology using multiple pre-planned data cuts: learnings to aid in model selection. BMC Med Res Methodol. 2020;20:103.
- Klijn SL, Fenwick E, Kroep S, et al. What did time tell us? A comparison and retrospective validation of different survival extrapolation methods for immuno-oncologic therapy in advanced or metastatic renal cell carcinoma. Pharmacoeconomics. 2021;39(3):345–356. doi:10.1007/s40273-020-00989-1.
- National Institute for Health and Care Excellence. Final appraisal document, Polatuzumab vedotin in combination for untreated diffuse large B-cell lymphoma. 2023. [cited Aug 2023]. Available from: https://www.nice.org.uk/guidance/gid-ta10785/documents/final-appraisal-determination-document.