Estimands for overall survival in clinical trials with treatment switching in oncology.


Journal

Pharmaceutical statistics
ISSN: 1539-1612
Titre abrégé: Pharm Stat
Pays: England
ID NLM: 101201192

Informations de publication

Date de publication:
01 2022
Historique:
revised: 28 04 2021
received: 21 05 2020
accepted: 10 07 2021
pubmed: 5 10 2021
medline: 27 1 2022
entrez: 4 10 2021
Statut: ppublish

Résumé

An addendum of the ICH E9 guideline on Statistical Principles for Clinical Trials was released in November 2019 introducing the estimand framework. This new framework aims to align trial objectives and statistical analyses by requiring a precise definition of the inferential quantity of interest, that is, the estimand. This definition explicitly accounts for intercurrent events, such as switching to new anticancer therapies for the analysis of overall survival (OS), the gold standard in oncology. Traditionally, OS in confirmatory studies is analyzed using the intention-to-treat (ITT) approach comparing treatment groups as they were initially randomized regardless of whether treatment switching occurred and regardless of any subsequent therapy (treatment-policy strategy). Regulatory authorities and other stakeholders often consider ITT results as most relevant. However, the respective estimand only yields a clinically meaningful comparison of two treatment arms if subsequent therapies are already approved and reflect clinical practice. We illustrate different scenarios where subsequent therapies are not yet approved drugs and thus do not reflect clinical practice. In such situations the hypothetical strategy could be more meaningful from patient's and prescriber's perspective. The cross-industry Oncology Estimand Working Group (www.oncoestimand.org) was initiated to foster a common understanding and consistent implementation of the estimand framework in oncology clinical trials. This paper summarizes the group's recommendations for appropriate estimands in the presence of treatment switching, one of the key intercurrent events in oncology clinical trials. We also discuss how different choices of estimands may impact study design, data collection, trial conduct, analysis, and interpretation.

Identifiants

pubmed: 34605168
doi: 10.1002/pst.2158
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

150-162

Informations de copyright

© 2021 John Wiley & Sons Ltd.

Références

International Council for Harmonisation of Technical Requirements for Human Use (ICH). E9 (R1) addendum on estimands and sensitivity analysis in clinical trials to the guideline on statistical principles for clinical trials. https://database.ich.org/sites/default/files/E9-R1_Step4_Guideline_2019_1203.pdf.
Sternberg CN, Hawkins RE, Wagstaff J, et al. A randomized, double-blind phase III study of pazopanib in patients with advanced and/or metastatic renal cell carcinoma: final overall survival results and safety update. Eur J Cancer. 2013;49(6):1287-1296. https://doi.org/10.1016/j.ejca.2012.12.010
Demetri GD, Reichardt P, Kang YK, et al. Final overall survival (OS) analysis with modeling of crossover impact in phase III GRID trial of regorafenib vs placebo in advanced gastrointestinal stromal tumors (GIST). J Clin Oncol. 2016;34(4_suppl):156-156. https://doi.org/10.1200/jco.2016.34.4_suppl.156
Davis C, Naci H, Gurpinar E, Poplavska E, Pinto A, Aggarwal A. Availability of evidence of benefits on overall survival and quality of life of cancer drugs approved by European medicines agency: retrospective cohort study of drug approvals 2009-13. BMJ. 2017;359:j4530. https://doi.org/10.1136/bmj.j4530
Salas-Vega S, Shearer E, Mossialos E. Relationship between costs and clinical benefits of new cancer medicines in Australia, France, the UK, and the US. Soc Sci Med. 2020;258:113042, ISSN 0277-9536. https://doi.org/10.1016/j.socscimed.2020.113042
Salcher-Konrad M, Naci H, Davis C. Approval of cancer drugs with uncertain therapeutic value: a comparison of regulatory decisions in Europe and the United States. Milbank Q. 2020;98:1219-1256. https://doi.org/10.1111/1468-0009.12476
Pasalic D, McGinnis GJ, Fuller CD, et al. Progression-free survival is a suboptimal predictor for overall survival among metastatic solid tumour clinical trials. Eur J Cancer. 2020;136:176-185, ISSN 0959-8049. https://doi.org/10.1016/j.ejca.2020.06.015
Rufibach K. Treatment effect quantification for time-to-event endpoints - Estimands, analysis strategies, and beyond. Pharm Stat. 2019;18(2):145-165.
European Medicines Agency. Question and answer on adjustment for cross-over in estimating effects in oncology trials. https://www.ema.europa.eu/en/documents/scientific-guideline/question-answer-adjustment-cross-over-estimating-effects-oncology-trials_en.pdf. Adopted by CHMP 13 December 2018.
Bornkamp B, Bermann G. Estimating the treatment effect in a subgroup defined by an early post-baseline biomarker measurement in randomized clinical trials with time-to-event endpoint. Stat Biopharmaceut Res. 2020;12(1):19-28. https://doi.org/10.1080/19466315.2019.1575280
Magnusson BP, Schmidli H, Rouyrre N, Scharfstein DO. Bayesian inference for a principal stratum estimand to assess the treatment effect in a subgroup characterized by post-randomization event occurrence. Stat Med. 2019;38(23):4761-4771. https://doi.org/10.1002/sim.83333
Bornkamp B, Rufibach K, Lin J, et al. Principal stratum strategy: potential role in drug development. Pharmaceut Stat. 2021;20(4):737-751. https://onlinelibrary.wiley.com/doi/10.1002/pst.2104
Mattei A, Mealli F, Ding P. Assessing causal effects in the presence of treatment switching through principal stratification. 2020. https://arxiv.org/pdf/2002.11989.pdf.
Bylicki O, Barazzutti H, Paleiron N, Margery J, Assié JB, Chouaïd C. First-line treatment of non-small-cell lung cancer (NSCLC) with immune checkpoint inhibitors. BioDrugs. 2019;33(11):1-13. https://doi.org/10.1007/s40259-019-00339-4
Barlesi F, Özgüroğlu M, Vansteenkiste JF, et al. Assessing the impact of subsequent checkpoint inhibitor (CPI) treatment on overall survival: post hoc analyses from the phase III JAVELIN lung 200 study of avelumab versus docetaxel in platinum-treated locally advanced/metastatic non-small cell lung cancer (NSCLC). Ann Oncol. 2019;30(Suppl_5):V611-V612. https://doi.org/10.1093/annonc/mdz260.014
Institute for Quality and Efficiency in Health Care (IQWiG, Germany). Benefit assessment. https://www.iqwig.de/en/press/press-releases/press-releases/ibrutinib-indication-of-added-benefit-in-one-of-three-therapeutic-indications.7318.html
Institute for Quality and Efficiency in Health Care (IQWiG, Germany). Ibrutinib (Imbruvica) for the treatment of chronic lymphocytic leukemia (CLL). Created May 2, 2016. Updated December 19, 2019. https://www.informedhealth.org/ibrutinib-imbruvica-for-the-treatment-of-chronic.2633.en.pdf?all_backgrounds=0&all_details=0&all_lexicons=0&all_reports=0&overview=1&print=1&theme=0.
Institute for Quality and Efficiency in Health Care (IQWiG, Germany). Ibrutinib - Benefit assessment according to §35a Social Code Book V1. Published April 28, 2016. https://www.iqwig.de/en/projects-results/projects/drug-assessment/a16-04-ibrutinib-benefit-assessment-according-to-35a-social-code-book-v.7200.html.
Institute for Quality and Efficiency in Health Care (IQWiG, Germany). Ibrutinib (Imbruvica) for the treatment of chronic Waldenström's macroglobulinemia. Created May 2, 2016. Updated December 19, 2019. https://www.informedhealth.org/ibrutinib-imbruvica-for-the-treatment-of.2621.en.pdf?all_backgrounds=0&all_details=0&all_lexicons=0&all_reports=0&overview=1&print=1&theme=0.
Rittmeyer A, Barlesi F, Waterkamp D, et al. Atelizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): a phase 3, open-label, multicentre randomized controlled trial. The Lancet. 2018;389(10066):255-265.
National Institute for Health and Care Excellence. Final appraisal determination: Atezolizumab for treating locally advanced or metastatic non-small-cell lung cancer after chemotherapy. March 2018. https://www.nice.org.uk/guidance/ta520/documents/final-appraisal-determination-document.
Larkin J, Minor D, D'Angelo S, et al. Overall survival in patients with advanced melanoma who received Nivolumab versus Investigator's choice chemotherapy in CheckMate 037: a randomized, controlled, open-label phase III trial. J Clin Oncol. 2018;36(4):383-390. https://doi.org/10.1200/JCO.2016.71.8023
Demetri GD, Reichardt P, Kang YK, Blay JY, Joensuu H, Schaefer KB, Kuss I, Kappeler C, Casali PG An updated overall survival analysis with correction for protocol-planned crossover of the international phase III, randomized, placebo-controlled trial of regorafenib in advanced gastrointestinal stromal tumors after failure of imatinib and sunitinib (GRID). J Clin Oncol. 2017; 33(3_suppl): 110. https://doi.org/10.1200/jco.2015.33.3_suppl.110.
Watkins C, Huang X, Latimer N, Tang Y, Wright EJ. Adjusting overall survival for treatment switches: commonly used methods and practical application. Pharm Stat. 2013;12(6):348-357. https://doi.org/10.1002/pst.1602
Alshurafa M, Briel M, Akl EA, et al. Inconsistent definitions for intention-to-treat in relation to missing outcome data: systematic review of the methods literature. PloS One. 2012;7(11):e49163. https://doi.org/10.1371/journal.pone.0049163
International Council for Harmonisation of Technical Requirements for Human Use (ICH). E9 Statistical Principles for Clinical Trials. Step 5. Published September 1, 1998.
Fleming TR, Rothmann MD, Lu HL. Issues in using progression-free survival when evaluating oncology products. J Clin Oncol. 2009;27(17):2874-2880. https://doi.org/10.1200/JCO.2008.20.4107
Robins JM, Finkelstein DM. Correcting for noncompliance and dependent censoring in an AIDS clinical trial with inverse probability of censoring weighted (IPCW) log-rank tests. Biometrics. 2000;56(3):779-788. https://doi.org/10.1111/j.0006-341x.2000.00779.x
Center for Medical Technology Policy. Best practices for the design, implementation, analysis, and reporting of oncology trials with high rates of treatment switching. A Guidance Document from the Green Park Collaborative. Center for Medical Technology Policy. October 2016, Version 1.0.
Hernan MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology. 2000;11(5):561-570. https://doi.org/10.1097/00001648-200009000-00012
Robins JM, Tsiatis AA. Correcting for noncompliance in randomized trials using rank preserving structural failure time models. Commun Stat Theory Methods. 1991;20(8):2609-2631. https://doi.org/10.1080/03610929108830654
Branson M, Whitehead J. Estimating a treatment effect in survival studies in which patients switch treatment. Stat Med. 2002;21(17):2449-2463. https://doi.org/10.1002/sim.1219
Herrlinger U, Schaefer N, Stainbach JP, et al. Bevacizumab plus Irinotecan versus Temozolomide in newly diagnosed O 6-Methylguanine-DNA Methyltransferase nonmethylated Glioblastoma: the randomized GLARIUS trial. J Clin Oncol. 2016;34(14):1611-1619. https://doi.org/10.1200/JCO.2015.63.4691
Latimer NR, White IR, Tilling K, Siebert U. Improved two-stage estimation to adjust for treatment switching in randomised trials: g-estimation to address time-dependent confounding. Stat Methods Med Res. 2020;29(10):2900-2918. https://doi.org/10.1177/0962280220912524.S
Latimer NR. Treatment switching in oncology trials and the acceptability of adjustment methods. Expert Rev Pharmacoecon Outcomes Res. 2015;15(4):561-564. https://doi.org/10.1586/14737167.2015.1037835
Latimer NR, Bell H, Abrams KR, Amonkar MM, Casey M. Adjusting for treatment switching in the METRIC study shows further improved overall survival with trametinib compared with chemotherapy. Cancer Med. 2016;5(5):806-815.
Morden JP, Lambert PC, Latimer N, Abrams KR, Wailoo AJ. Assessing methods for dealing with treatment switching in randomized controlled trials: a simulation study. BMC Med Res Methodol. 2011;11(4):1-20. https://doi.org/10.1186/1471-2288-11-4

Auteurs

Juliane Manitz (J)

Global Biostatistics, EMD Serono, Billerica, Massachusetts, USA.

Natalia Kan-Dobrosky (N)

Statistical Science, PPD, Wilmington, North Carolina, USA.

Hannes Buchner (H)

Biostatistics and Data Science, Staburo GmbH, Munich, Germany.

Marie-Laure Casadebaig (ML)

GBDS, BMS, Boudry, Switzerland.

Evgeny Degtyarev (E)

Clinical Development and Analytics, Novartis, Basel, Switzerland.

Jyotirmoy Dey (J)

Data and Statistical Sciences, AbbVie Inc., North Chicago, Illinois, USA.

Vincent Haddad (V)

Oncology Biometric, AstraZeneca, Cambridge, UK.

Fei Jie (F)

Biostatistics and Data Management, Daiichi Sankyo Inc, Basking Ridge, New Jersey, USA.

Emily Martin (E)

Global Biostatistics, EMD Serono, Billerica, Massachusetts, USA.

Mindy Mo (M)

Oncology Clinical Statistics US, Bayer, Whippany, New Jersey, USA.

Kaspar Rufibach (K)

Methods, Collaboration, and Outreach, F. Hoffmann-La Roche Ltd, Basel, Switzerland.

Yue Shentu (Y)

Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, New Jersey, USA.

Viktoriya Stalbovskaya (V)

Clinical Development, Merus, Utrecht, The Netherlands.

Rui Sammi Tang (R)

Global Biometric, Servier Pharmaceuticals, Boston, Massachusetts, USA.

Godwin Yung (G)

Methods, Collaboration, and Outreach, Genentech, San Francisco, California, USA.

Jiangxiu Zhou (J)

Biostatistics, GSK, Collegeville, Pennsylvania, USA.

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