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Castañon E, Sanchez-Arraez A, Alvarez-Manceñido F, Jimenez-Fonseca P, Carmona-Bayonas A. Critical reappraisal of phase III trials with immune checkpoint inhibitors in non-proportional hazards settings. Eur J Cancer 2020; 136:159-168. [DOI: 10.1016/j.ejca.2020.06.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Revised: 04/18/2020] [Accepted: 06/09/2020] [Indexed: 10/23/2022]
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Dahlberg SE, Korn EL, Le-Rademacher J, Mandrekar SJ. Clinical Versus Statistical Significance in Studies of Thoracic Malignancies. J Thorac Oncol 2020; 15:1406-1408. [DOI: 10.1016/j.jtho.2020.06.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 06/03/2020] [Accepted: 06/11/2020] [Indexed: 11/26/2022]
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Cohen R, Shi Q, André T. Immunotherapy for Early Stage Colorectal Cancer: A Glance into the Future. Cancers (Basel) 2020; 12:E1990. [PMID: 32708216 PMCID: PMC7409300 DOI: 10.3390/cancers12071990] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 07/10/2020] [Accepted: 07/17/2020] [Indexed: 12/16/2022] Open
Abstract
Immune checkpoint inhibitors (ICI) have reshaped therapeutic strategies for cancer patients. The development of ICI for early stage colorectal cancer is accompanied by specific challenges: (i) the selection of patients who are likely to benefit from these treatments, i.e., patients with tumors harboring predictive factors of efficacy of ICI, such as microsatellite instability and/or mismatch repair deficiency (MSI/dMMR), or other potential parameters (increased T cell infiltration using Immunoscore® or others, high tumor mutational burden, POLE mutation), (ii) the selection of patients at risk of disease recurrence (poor prognostic features), and (iii) the choice of an accurate clinical trial methodological framework. In this review, we will discuss the ins and outs of clinical research of ICI for early stage MSI/dMMR CC patients in adjuvant and neoadjuvant settings. We will then summarize data that might support the development of ICI in localized colorectal cancer beyond MSI/dMMR.
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Affiliation(s)
- Romain Cohen
- Department of Medical Oncology, Hôpital Saint-Antoine, Sorbonne Université, Assistance Publique-Hôpitaux de Paris (AP-HP), F-75012 Paris, France;
- Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55905, USA;
| | - Qian Shi
- Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55905, USA;
| | - Thierry André
- Department of Medical Oncology, Hôpital Saint-Antoine, Sorbonne Université, Assistance Publique-Hôpitaux de Paris (AP-HP), F-75012 Paris, France;
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Degtyarev E, Rufibach K, Shentu Y, Yung G, Casey M, Englert S, Liu F, Liu Y, Sailer O, Siegel J, Sun S, Tang R, Zhou J. Assessing the Impact of COVID-19 on the Clinical Trial Objective and Analysis of Oncology Clinical Trials-Application of the Estimand Framework. Stat Biopharm Res 2020; 12:427-437. [PMID: 34191975 PMCID: PMC8011489 DOI: 10.1080/19466315.2020.1785543] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 06/17/2020] [Accepted: 06/17/2020] [Indexed: 12/11/2022]
Abstract
Abstract-Coronavirus disease 2019 (COVID-19) outbreak has rapidly evolved into a global pandemic. The impact of COVID-19 on patient journeys in oncology represents a new risk to interpretation of trial results and its broad applicability for future clinical practice. We identify key intercurrent events (ICEs) that may occur due to COVID-19 in oncology clinical trials with a focus on time-to-event endpoints and discuss considerations pertaining to the other estimand attributes introduced in the ICH E9 addendum. We propose strategies to handle COVID-19 related ICEs, depending on their relationship with malignancy and treatment and the interpretability of data after them. We argue that the clinical trial objective from a world without COVID-19 pandemic remains valid. The estimand framework provides a common language to discuss the impact of COVID-19 in a structured and transparent manner. This demonstrates that the applicability of the framework may even go beyond what it was initially intended for.
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Affiliation(s)
| | | | | | | | | | | | | | - Yi Liu
- Nektar Therapeutics, San Francisco, CA
| | - Oliver Sailer
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | | | | | - Rui Tang
- Servier Pharmaceuticals, Boston, MA
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Chen X, Wang X, Chen K, Zheng Y, Chappell RJ, Dey J. Comparison of survival distributions in clinical trials: A practical guidance. Clin Trials 2020; 17:507-521. [PMID: 32594788 DOI: 10.1177/1740774520928614] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND In randomized clinical trials with censored time-to-event outcomes, the logrank test is known to have substantial statistical power under the proportional hazards assumption and is widely adopted as a tool to compare two survival distributions. However, the proportional hazards assumption is impossible to validate in practice until the data are unblinded. However, the statistical analysis plan of a randomized clinical trial and in particular its primary analysis method must be pre-specified before any unblinded information may be reviewed. PURPOSE The purpose of this article is to guide applied biostatisticians in the prespecification of a desired primary analysis method when a treatment effect with nonproportional hazards is anticipated. While articles proposing alternate statistical tests are aplenty, to the best of our knowledge, there is no article available that attempts to simplify the choice and prespecification of a primary statistical test under specific expected patterns on nonproportional hazards. We provide such guidance by reviewing various tests proposed as more powerful alternatives to the standard logrank test under nonproportional hazards and simultaneously comparing their performance under a wide variety of nonproportional hazards scenarios to elucidate their advantages and disadvantages. METHOD In order to select the most preferable test for detecting specific differences between survival distributions of interest while controlling false positive rates, we review and assess the performance of weighted and adaptively weighted logrank tests, weighted and adaptively weighted Kaplan-Meier tests and versatile tests under various patterns of nonproportional hazards treatment effects through simulation. CONCLUSION We validate some of the claimed properties of the proposed extensions and identify tests that may be more preferable under specific expected pattern of nonproportional hazards when such knowledge is available. We show that versatile tests, while achieving robustness to departures from proportional hazards, may lose interpretation of directionality (superiority or inferiority) and can only be seen to test departures from equality. Detailed summary and discussion of the performance of each test in terms of type I error rate and power are provided to formulate specific guidance about their applicability and use.
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Affiliation(s)
| | - Xin Wang
- Vertex Pharmaceuticals, Boston, MA, USA
| | - Kun Chen
- Gilead Sciences, Inc., Foster City, CA, USA
| | | | - Richard J Chappell
- Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
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Affiliation(s)
- Boris Freidlin
- Boris Freidlin, PhD and Edward L. Korn, PhD, Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD
| | - Edward L Korn
- Boris Freidlin, PhD and Edward L. Korn, PhD, Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD
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Uno H, Tian L. Is the Log-Rank and Hazard Ratio Test/Estimation the Best Approach for Primary Analysis for All Trials? J Clin Oncol 2020; 38:2000-2001. [PMID: 32315272 DOI: 10.1200/jco.19.03097] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Hajime Uno
- Hajime Uno, PhD, Departments of Medical Oncology and Data Sciences, Dana-Farber Cancer Institute, Boston, MA; and Lu Tian, ScD, Department of Biomedical Data Science, Stanford University, Palo Alto, CA
| | - Lu Tian
- Hajime Uno, PhD, Departments of Medical Oncology and Data Sciences, Dana-Farber Cancer Institute, Boston, MA; and Lu Tian, ScD, Department of Biomedical Data Science, Stanford University, Palo Alto, CA
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Huang B, Wei LJ, Ludmir EB. Estimating Treatment Effect as the Primary Analysis in a Comparative Study: Moving Beyond P Value. J Clin Oncol 2020; 38:2001-2002. [PMID: 32315271 DOI: 10.1200/jco.19.03111] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Bo Huang
- Bo Huang, PhD, Pfizer, Groton, CT; Lee-Jen Wei, PhD, Harvard T.H. Chan School of Public Health, Boston, MA; and Ethan B. Ludmir, MD, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Lee-Jen Wei
- Bo Huang, PhD, Pfizer, Groton, CT; Lee-Jen Wei, PhD, Harvard T.H. Chan School of Public Health, Boston, MA; and Ethan B. Ludmir, MD, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Ethan B Ludmir
- Bo Huang, PhD, Pfizer, Groton, CT; Lee-Jen Wei, PhD, Harvard T.H. Chan School of Public Health, Boston, MA; and Ethan B. Ludmir, MD, The University of Texas MD Anderson Cancer Center, Houston, TX
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Royston P, B Parmar MK. A simulation study comparing the power of nine tests of the treatment effect in randomized controlled trials with a time-to-event outcome. Trials 2020; 21:315. [PMID: 32252820 PMCID: PMC7132898 DOI: 10.1186/s13063-020-4153-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 02/08/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND The logrank test is routinely applied to design and analyse randomized controlled trials (RCTs) with time-to-event outcomes. Sample size and power calculations assume the treatment effect follows proportional hazards (PH). If the PH assumption is false, power is reduced and interpretation of the hazard ratio (HR) as the estimated treatment effect is compromised. Using statistical simulation, we investigated the type 1 error and power of the logrank (LR)test and eight alternatives. We aimed to identify test(s) that improve power with three types of non-proportional hazards (non-PH): early, late or near-PH treatment effects. METHODS We investigated weighted logrank tests (early, LRE; late, LRL), the supremum logrank test (SupLR) and composite tests (joint, J; combined, C; weighted combined, WC; versatile and modified versatile weighted logrank, VWLR, VWLR2) with two or more components. Weighted logrank tests are intended to be sensitive to particular non-PH patterns. Composite tests attempt to improve power across a wider range of non-PH patterns. Using extensive simulations based on real trials, we studied test size and power under PH and under simple departures from PH comprising pointwise constant HRs with a single change point at various follow-up times. We systematically investigated the influence of high or low control-arm event rates on power. RESULTS With no preconceived type of treatment effect, the preferred test is VWLR2. Expecting an early effect, tests with acceptable power are SupLR, C, VWLR2, J, LRE and WC. Expecting a late effect, acceptable tests are LRL, VWLR, VWLR2, WC and J. Under near-PH, acceptable tests are LR, LRE, VWLR, C, VWLR2 and SupLR. Type 1 error was well controlled for all tests, showing only minor deviations from the nominal 5%. The location of the HR change point relative to the cumulative proportion of control-arm events considerably affected power. CONCLUSIONS Assuming ignorance of the likely treatment effect, the best choice is VWLR2. Several non-standard tests performed well when the correct type of treatment effect was assumed. A low control-arm event rate reduced the power of weighted logrank tests targeting early effects. Test size was generally well controlled. Further investigation of test characteristics with different types of non-proportional hazards of the treatment effect is warranted.
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Affiliation(s)
- Patrick Royston
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, 90 High Holborn, London, WC1V 6LJ, UK.
| | - Mahesh K B Parmar
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, 90 High Holborn, London, WC1V 6LJ, UK
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Buyse M, Saad ED, Burzykowski T, Péron J. Assessing Treatment Benefit in Immuno-oncology. STATISTICS IN BIOSCIENCES 2020. [DOI: 10.1007/s12561-020-09268-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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