Assessing the Impact of COVID-19 on the Clinical Trial Objective and Analysis of Oncology Clinical Trials—Application of the Estimand Framework

References

• Aalen, O. O., and Johansen, S. (1978), “An Empirical Transition Matrix for Non-Homogeneous Markov Chains Based on Censored Observations,” Scandinavian Journal of Statistics, 5, 141–150.
   Web of Science ®Google Scholar
• Akacha, M., Bretz, F., and Ruberg, S. (2017), “Estimands in Clinical Trials—Broadening the Perspective,” Statistics in Medicine, 36, 5–19. DOI: 10.1002/sim.7033.
   PubMed Web of Science ®Google Scholar
• American Society of Clinical Oncology (2020), “COVID-19 Patient Care Information,” ASCO Coronavirus Resources, available at https://www.asco.org/asco-coronavirus-information/care-individuals-cancer-during-covid-19.
   Google Scholar
• Bauer, P., and Koenig, F. (2006), “The Reassessment of Trial Perspectives From Interim Data—A Critical View,” Statistics in Medicine, 25, 23–36. DOI: 10.1002/sim.2180.
   PubMed Web of Science ®Google Scholar
• Burki, T. K. (2020), “Cancer Guidelines During the COVID-19 Pandemic,” The Lancet Oncology, 21, 629–630. DOI: 10.1016/S1470-2045(20)30217-5.
   PubMed Web of Science ®Google Scholar
• Dai, M., Liu, D., Liu, M., Zhou, F., Li, G., Chen, Z., Zhang, Z., You, H., Wu, M., Zheng, Q., Xiong, Y., Xiong, H., Wang, C., Chen, C., Xiong, F., Zhang, Y., Peng, Y., Ge, S., Zhen, B., Yu, T., Wang, L., Wang, H., Liu, Y., Chen, Y., Mei, J., Gao, X., Li, Z., Gan, L., He, C., Li, Z., Shi, Y., Qi, Y., Yang, J., Tennen, D. G., Chai, L., Mucci, L. A., Santillana, M., and Cai, H. (2020), “Patients With Cancer Appear More Vulnerable to SARS-COV-2: A Multi-Center Study During the COVID-19 Outbreak,” Cancer Discovery, DOI: 10.1158/2159-8290.CD-20-0422.
   PubMed Web of Science ®Google Scholar
• Degtyarev, E., and Rufibach, K. (2020), “Oncology Estimand Working Group,” available at http://www.oncoestimand.org.
   Google Scholar
• Degtyarev, E., Zhang, Y., Sen, K., Lebwohl, D., Akacha, M., Hampson, L. V., Bornkamp, B., Maniero, A., Bretz, F., and Zuber, E. (2019), “Estimands and the Patient Journey: Addressing the Right Question in Oncology Clinical Trials,” Journal of Clinical Oncology Precision Oncology, 3, 1–10. DOI: 10.1200/PO.18.00381.
   Google Scholar
• European Medicines Agency (2020a), “Guidance on the Management of Clinical Trials During the COVID-19 (Coronavirus) Pandemic,” Scientific Guideline, available at https://ec.europa.eu/health/sites/health/files/files/eudralex/vol-10/guidanceclinicaltrials_covid19_en.pdf.
   Google Scholar
• European Medicines Agency (2020b), “ICH E9 (R1) Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials,” Scientific Guideline, available at https://www.ema.europa.eu/en/documents/scientific-guideline/ich-e9-r1-addendum-estimands-sensitivity-analysis-clinical-trials-guideline-statistical-principles_en.pdf.
   Google Scholar
• European Medicines Agency (2020c), “Points to Consider on Implications of Coronavirus Disease on Methodological Aspects of Ongoing Clinical Trials,” Scientific Guideline, available at https://www.ema.europa.eu/en/documents/scientific-guideline/points-consider-implications-coronavirus-disease-covid-19-methodological-aspects-ongoing-clinical_en.pdf.
   Google Scholar
• European Society for Medical Oncology (2020), “Cancer Patient Management During the COVID-19 Pandemic,” Guidelines, available at https://www.esmo.org/guidelines/cancer-patient-management-during-the-covid-19-pandemic.
   Google Scholar
• Food and Drug Administration (2020), “FDA Guidance on Conduct of Clinical Trials of Medical Products During COVID-19 Pandemic,” Regulatory Information, available at https://www.fda.gov/regulatory-information/search-fda-guidance-documents/fda-guidance-conduct-clinical-trials-medical-products-during-covid-19-public-health-emergency.
   Google Scholar
• Frangakis, C. E., and Rubin, D. B. (2002), “Principal Stratification in Causal Inference,” Biometrics, 58, 21–29. DOI: 10.1111/j.0006-341x.2002.00021.x.
   PubMed Web of Science ®Google Scholar
• Freidlin, B., and Korn, E. L. (2019), “Methods for Accommodating Nonproportional Hazards in Clinical Trials: Ready for the Primary Analysis?,” Journal of Clinical Oncology, 37, 3455–3459. DOI: 10.1200/JCO.19.01681.
   PubMed Web of Science ®Google Scholar
• Gates, B. (2020), “Responding to COVID-19-A Once-in-a-Century Pandemic?,” New England Journal of Medicine, 382, 1677–1679. DOI: 10.1056/NEJMp2003762.
   PubMed Web of Science ®Google Scholar
• Huang, B., and Kuan, P. F. (2018), “Comparison of the Restricted Mean Survival Time With the Hazard Ratio in Superiority Trials With a Time-to-Event Endpoint,” Pharmaceutical Statistics, 17, 202–213. DOI: 10.1002/pst.1846.
   PubMed Web of Science ®Google Scholar
• Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., Cheng, Z., Yu, T., Xia, J., Wei, Y., Wu, W., Xie, X., Yin, W., Li, H., Liu, M., Xiao, Y., Gao, H., Guo, L., Xie, J., Wang, G., Jiang, R., Gao, Z., Jin, Q., Wang, J., and Cao, B. (2020), “Clinical Features of Patients Infected With 2019 Novel Coronavirus in Wuhan, China,” Lancet, 395, 497–506. DOI: 10.1016/S0140-6736(20)30183-5.
   PubMed Web of Science ®Google Scholar
• Jo, B., and Stuart, E. A. (2009), “On the Use of Propensity Scores in Principal Causal Effect Estimation,” Statistics in Medicine, 28, 2857–2875. DOI: 10.1002/sim.3669.
   PubMed Web of Science ®Google Scholar
• Kay, R. (1986), “Treatment Effects in Competing-Risks Analysis of Prostrate Cancer Data,” Biometrics, 42, 203–211. DOI: 10.2307/2531258.
   PubMed Web of Science ®Google Scholar
• Lin, R. S., Lin, J., Roychoudhury, S., Anderson, K. M., Hu, T., Huang, B., Leon, L. F., Liao, J. J. Z., Liu, R., Luo, X., Mukhopadhyay, P., Qin, R., Tatsuoka, K., Wang, X., Wang, Y., Zhu, J., Chen, T., Iacona, R., and Cross-Pharma Non-Proportional Hazards Working Group (2020), “Alternative Analysis Methods for Time to Event Endpoints Under Nonproportional Hazards: A Comparative Analysis,” Statistics in Biopharmaceutical Research, 12, 187–198. DOI: 10.1080/19466315.2019.1697738.
   Web of Science ®Google Scholar
• Lipkovich, I., Ratitch, B., and O’Kelly, M. (2016), “Sensitivity to Censored-at-Random Assumption in the Analysis of Time-to-Event Endpoints,” Pharmaceutical Statistics, 15, 216–229. DOI: 10.1002/pst.1738.
   PubMed Web of Science ®Google Scholar
• Lou, Y., Jones, M. P., and Sun, W. (2019), “Assessing the Ratio of Means as a Causal Estimand in Clinical Endpoint Bioequivalence Studies in the Presence of Intercurrent Events,” Statistics in Medicine, 38, 5214–5235. DOI: 10.1002/sim.8367.
   PubMed Web of Science ®Google Scholar
• Meyer, R. D., Ratitch, B., Wolbers, M., Marchenko, O., Quan, H., Li, D., Fletcher, C., Li, X., Wright, D., Shentu, Y., Englert, S., Shen, W., Dey, J., Liu, T., Zhou, M., Bohidar, N., Zhao, P., and Hale, M. (2020), “Statistical Issues and Recommendations for Clinical Trials Conducted During the COVID-19 Pandemic,” Statistics in Biopharmaceutical Research, DOI: 10.1080/19466315.2020.1779122.
   PubMed Web of Science ®Google Scholar
• Ratitch, B., Bell, J., Mallinckrodt, C., Bartlett, J. W., Goel, N., Molenberghs, G., O’Kelly, M., Singh, P., and Lipkovich, I. (2020), “Choosing Estimands in Clinical Trials: Putting the ICH E9(R1) Into Practice,” Therapeutic Innovation and Regulatory Science, 54, 324–341. DOI: 10.1007/s43441-019-00061-x.
   PubMed Web of Science ®Google Scholar
• Robins, J. M., and Finkelstein, D. M. (2000), “Correcting for Noncompliance and Dependent Censoring in an AIDS Clinical Trial With Inverse Probability of Censoring Weighted (IPCW) Log-Rank Tests,” Biometrics, 56, 779–788. DOI: 10.1111/j.0006-341x.2000.00779.x.
   PubMed Web of Science ®Google Scholar
• Royston, P., and Parmar, M. K. (2020), “A Simulation Study Comparing the Power of Nine Tests of the Treatment Effect in Randomized Controlled Trials With a Time-to-Event Outcome,” Trials, 21, 315,DOI: 10.1186/s13063-020-4153-2.
   PubMedGoogle Scholar
• Rufibach, K. (2019), “Treatment Effect Quantification for Time-to-Event Endpoints—Estimands, Analysis Strategies, and Beyond,” Pharmaceutical Statistics, 18, 145–165. DOI: 10.1002/pst.1917.
   PubMed Web of Science ®Google Scholar
• Shet, A., Ray, D., Malavige, N., Santosham, M., and Bar-Zeev, N. (2020), “Differential COVID-19-Attributable Mortality and BCG Vaccine Use in Countries,” Unpublished Manuscript, DOI: 10.1101/2020.04.01.20049478.
   Google Scholar
• Singh, A. G., and Chaturvedi, P. (2020), “Clinical Trials During COVID-19,” Head & Neck, DOI: 10.1002/hed.26223.
   Google Scholar
• Stegherr, R., Beyersmann, J., Jehl, V., Rufibach, K., Leverkus, F., Schmoor, C., and Friede, T. (2019), “Survival Analysis for AdVerse Events With VarYing Follow-Up Times (SAVVY): Rationale and Statistical Concept of a Meta-Analytic Study,” arXiv no. 1912.00263.
   Google Scholar
• Stuart, E. A., and Jo, B. (2015), “Assessing the Sensitivity of Methods for Estimating Principal Causal Effects,” Statistical Methods in Medical Research, 24, 657–674. DOI: 10.1177/0962280211421840.
   PubMed Web of Science ®Google Scholar
• Sun, J. (2006), The Statistical Analysis of Interval-Censored Failure Time Data, New York, NY: Springer-Verlag.
   Google Scholar
• U.S. National Library of Medicine (2020a), “A Study to Evaluate the Safety and Efficacy of Tocilizumab in Patients With Severe COVID-19 Pneumonia (COVACTA),” ClinicalTrials.gov, available at https://clinicaltrials.gov/ct2/show/NCT04320615.
   Google Scholar
• U.S. National Library of Medicine (2020b), “Phase 3 Randomized, Double-Blind, Placebo-Controlled Multi-Center Study to Assess the Efficacy and Safety of Ruxolitinib in Patients With COVID-19 Associated Cytokine Storm (RUXCOVID),” ClinicalTrials.gov, available at https://clinicaltrials.gov/ct2/show/NCT04362137.
   Google Scholar
• Unkel, S., Amiri, M., Benda, N., Beyersmann, J., Knoerzer, D., Kupas, K., Langer, F., Leverkus, F., Loos, A., Ose, C., Proctor, T., Schmoor, C., Schwenke, C., Skipka, G., Unnebrink, K., Voss, F., and Friede, T. (2019), “On Estimands and the Analysis of Adverse Events in the Presence of Varying Follow-Up Times Within the Benefit Assessment of Therapies,” Pharmaceutical Statistics, 18, 166–183. DOI: 10.1002/pst.1915.
   PubMed Web of Science ®Google Scholar
• Viele, K., Berry, S., Neuenschwander, B., Amzal, B., Chen, F., Enas, N., Hobbs, B., Ibrahim, J. G., Kinnersley, N., Lindborg, S., Micallef, S., Roychoudhury, S., and Thompson, L. (2014), “Use of Historical Control Data for Assessing Treatment Effects in Clinical Trials,” Pharmaceutical Statistics, 13, 41–54. DOI: 10.1002/pst.1589.
   PubMed Web of Science ®Google Scholar
• World Health Organization (2020), “Coronavirus Disease (COVID-19) Situation Report—132,” Coronavirus Disease (COVID-2019) Situation Reports, available at https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports.
   Google Scholar
• Yung, G., and Liu, Y. (2019), “Sample Size and Power for the Unweighted Log-Rank Test and Kaplan-Meier Based Tests With Allowance for Nonproportional Hazards,” Biometrics, DOI: 10.1111/biom.13196.
   PubMed Web of Science ®Google Scholar