Non-inferiority trials with time-to-event data: clarifying the impact of censoring

References

• Althunian, T. A., A. de Boer, R. H. H. Groenwold, and O. H. Klungel. 2017. Defining the noninferiority margin and analyzing noninferiority: an overview. British Journal of Clinical Pharmacology 83 (8):1636–1642. doi:10.1111/bcp.13280.
   PubMed Web of Science ®Google Scholar
• Bao, W., M. Gaffney, M. L. Pressler, R. Fayyad, W. Wisemandle, B. Beckerman, K. E. Wolski, and S. E. Nissen. 2020. Strengthening the interpretability of clinical trial results by assessing the effect of informative censoring on the primary estimand in PRECISION. Clinical Trials 17 (5):535–5442. doi:10.1177/1740774520934747.
   PubMed Web of Science ®Google Scholar
• Bristol, D. R. 1993. Planning survival studies to compare a treatment to an active control. Journal of Biopharmaceutical Statistics 3 (2):153–158. doi:10.1080/10543409308835056.
   PubMedGoogle Scholar
• Brown, D., P. Volkers, and S. Day. 2006. An introductory note to CHMP guidelines: Choice of the non-inferiority margin and data monitoring committees. Statistics in Medicine 25 (10):1623–1627. doi:10.1002/sim.2561.
   PubMed Web of Science ®Google Scholar
• Burotto, M., V. Prasad, and T. Fojo. 2015. Non-inferiority trials: Why oncologists must remain wary. The Lancet Oncology 16 (4):364–366. doi:10.1016/S1470-2045(15)70129-4.
   PubMed Web of Science ®Google Scholar
• Campigotto, F., and E. Weller. 2014. Impact of informative censoring on the Kaplan-Meier estimate of progression-free survival in phase II clinical trials. Journal of Clinical Oncology 32 (27):3068–3074. doi:10.1200/JCO.2014.55.6340.
   PubMed Web of Science ®Google Scholar
• Chang, H. W., and L. W. McKeague. 2019. Nonparametric testing for multiple survival functions with non-inferiority margins. Annals of Statistics 47 (1):205–232. doi:10.1214/18-AOS1686.
   PubMed Web of Science ®Google Scholar
• Clayton, D., and J. Cuzick. 1985. Multivariate generalizations of the proportional hazards model. Journal of the Royal Statistical Society: Series A (General) 148 (2):82–117. doi:10.2307/2981943.
   Google Scholar
• Cole, S. R., and M. A. Hernan. 2004. Adjusted survival curves with inverse probability weights. Computer Methods and Programs in Biomedicine 75 (1):45–49. doi:10.1016/j.cmpb.2003.10.004.
   PubMed Web of Science ®Google Scholar
• Com-Nougue, C., C. Rodary, and C. Patte. 1993. How to establish equivalence when data are censored: A randomized trial of treatments for B non-Hodgkin lymphoma. Statistics in Medicine 12 (14):1353–1364. doi:10.1002/sim.4780121407.
   PubMed Web of Science ®Google Scholar
• Committee for Medicinal Products for Human Use (CHMP) guideline on the choice of the non-inferiority margin. 2005. EMEA/CPMP/EWP/2158/99.
   Google Scholar
• Cox, D. R. 1972. Regression models and life-tables. Journal of the Royal Statistical Society, Series B 34 (2):187–220. doi:10.1111/j.2517-6161.1972.tb00899.x.
   Web of Science ®Google Scholar
• Cox, D. R. 1975. Partial likelihood. Biometrica 62 (2):269–276. doi:10.1093/biomet/62.2.269.
   Web of Science ®Google Scholar
• Curtis, L. H., B. G. Hammill, E. L. Eisensein, J. M. Kramer, and K. J. Anstrom. 2007. Using inverse probability-weighted estimators in comparative effectiveness analysis with observational databases. Medical Care 45 (10 Suppl 2):S103–107. doi:10.1097/MLR.0b013e31806518ac.
   PubMed Web of Science ®Google Scholar
• Fleming, T. R. 1990. Evaluation of active control trials in aids. Journal of Acquired Immune Deficiency Syndromes 2:S82–87.
   Google Scholar
• Freitag, G. 2005. Methods for assessing noninferiority with censored data. Biometrical Journal 47 (1):88–98. doi:10.1002/bimj.200410083.
   PubMed Web of Science ®Google Scholar
• Freitag, G., S. Lange, and A. Munk. 2006. Non-parametric assessment of non-inferiority with censored data. Statistics in Medicine 25 (7):1201–1217. doi:10.1002/sim.2444.
   PubMed Web of Science ®Google Scholar
• Hanagal, D. D., A. Pandey, and A. Ganguly. 2017. Correlated gamma frailty models for bivariate survival data. Communications in Statistics - Simulation and Computation 46 (5):3627–3644.
   Web of Science ®Google Scholar
• Horton, N. J., and S. R. Lipsitz. 2001. Multiple imputation in practice: comparison of software packages for regression models with missing variables. The American Statistician 55 (3):244–254. doi:10.1198/000313001317098266.
   Web of Science ®Google Scholar
• Hsu, C. H., and J. M. Taylor. 2009. Nonparametric comparison of two survival functions with dependent censoring via nonparametric multiple imputation. Statistics in Medicine 28 (3):462–475. doi:10.1002/sim.3480.
   PubMed Web of Science ®Google Scholar
• Huang, X., and R. A. Wolfe. 2002. A frailty model for informative censoring. Biometrics 58 (3):510–520. doi:10.1111/j.0006-341X.2002.00510.x.
   PubMed Web of Science ®Google Scholar
• Jackson, D., I. White, S. Seaman, H. Evans, K. Baisley, and J. Carpenter. 2014. Relaxing the independent censoring assumption in the Cox proportional hazards model using multiple imputation. Statistics in Medicine 33 (27):4681–4694. doi:10.1002/sim.6274.
   PubMed Web of Science ®Google Scholar
• Kim, M., C. Wang, and X. Xue. 2019. Assessing the influence of treatment nonadherence on noninferiority trials using the tipping point approach. Statistics in Medicine 38 (4):650–659. doi:10.1002/sim.7999.
   PubMed Web of Science ®Google Scholar
• Kim, M. Y., and J. D. Goldberg. 2001. The effects of outcome misclassification and measurement error on the design and analysis of therapeutic equivalence trials. Statistics in Medicine 20 (14):2065–2078. doi:10.1002/sim.847.
   PubMed Web of Science ®Google Scholar
• Kombrink, K., A. Munk, and T. Friede. 2013. Design and semiparametric analysis of non-inferiority trials with active and placebo control for censored time-to-event data. Statistics in Medicine 32 (18):3055–3066. doi:10.1002/sim.5769.
   PubMed Web of Science ®Google Scholar
• Liu, T., and D. F. Heitjan. 2012. Sensitivity of the discrete-time Kaplan-Meier estimate to nonignorable censoring: Application in a clinical trial. Statistics in Medicine 31 (25):2998–3010. doi:10.1002/sim.5454.
   PubMed Web of Science ®Google Scholar
• Liu, Y. 2017. Sensitivity analyses for informative censoring in survival data: A trial example. Journal of Biopharmaceutical Statistics 27 (4):595–610. doi:10.1080/10543406.2016.1167076.
   PubMed Web of Science ®Google Scholar
• Lu, Z., and W. Zhang. 2012. Semiparametric likelihood estimation in survival models with informative censoring. Journal of Multivariate Analysis 106:187–211. doi:10.1016/j.jmva.2011.10.010.
   Web of Science ®Google Scholar
• Martins, A., M. Aerts, N. Hens, A. Wienke, and S. Abrams. 2019. Correlated gamma frailty models for bivariate survival time data. Statistical Methods in Medical Research 28 (10–11):3437–3450. doi:10.1177/0962280218803127.
   PubMed Web of Science ®Google Scholar
• Matsuyama, Y. 2003. Sensitivity analysis for the estimation of rates of change with non-ignorable drop-out: An application to a randomized clinical trial of the vitamin D3. Statistics in Medicine 22 (5):811–827. doi:10.1002/sim.1367.
   PubMed Web of Science ®Google Scholar
• Mielke, M., A. Munk, and A. Schacht. 2008. Planning and assessing non-inferiority in a gold standard design with censored, exponentially distributed endpoints. Statistics in Medicine 27 (25):5093–5110. doi:10.1002/sim.3348.
   PubMed Web of Science ®Google Scholar
• Nie, L., and Z. Zhang. 2018. Design of noninferiority trials in oncology. In Oncology clinical trials: successful design, conduct, and analysis, ed. W. K. Kelly and S. Halabi, pp. 159–164. New York, NY: Demos Medical.
   Google Scholar
• Oakes, D. 1989. Bivariate survival models induced by frailties. Journal of the American Statistical Association 84 (406):487–493. doi:10.1080/01621459.1989.10478795.
   Web of Science ®Google Scholar
• Persson, I., and H. Khamis. 2005. Bias of the Cox model hazard ratio. Journal of Modern Applied Statistical Methods 4 (1):90–99. doi:10.22237/jmasm/1114906200.
   Google Scholar
• Riechelmann, R. P., A. Alex, L. Cruz, G. M. Bariani, and P. M. Hoff. 2013. Non-inferiority cancer clinical trials: Scope and purposes underlying their design. Annals of Oncology 24 (7):1942–1947. doi:10.1093/annonc/mdt073.
   PubMed Web of Science ®Google Scholar
• Robins, J. M. 1993. Information recovery and bias adjustment in proportional hazards regression analysis of randomized trials using surrogate markers. Proceedings of the biopharmaceutical section, Virginia: American Statistical Association, 24–33.
   Google Scholar
• Robins, J. M., and D. M. Finkelstein. 2000. Correcting for noncompliance and dependent censoring in an aids clinical trial with inverse probability of censoring weighted (IPCW) log-rank tests. Biometrics 56 (3):779–788. doi:10.1111/j.0006-341X.2000.00779.x.
   PubMed Web of Science ®Google Scholar
• Robins, J. M., and A. Rotnitzky. 1992. Recovery of information and adjustment for dependent censoring using surrogate markers. In AIDS epidemiology, ed. N. Jewell, K. Dietz, and V. Farewell, pp. 297–331. Boston: Birkhäuser.
   Google Scholar
• Rothmann, M., N. Li, G. Chen, G. Y. H. Chi, R. Temple, and H. H. Tsou. 2003. Design and analysis of non-inferiority mortality trials in oncology. Statistics in Medicine 22 (2):239–264. doi:10.1002/sim.1400.
   PubMed Web of Science ®Google Scholar
• Rubin, D. B., and N. Schenker. 1991. Multiple imputations in health-care database: An overview and some applications. Statistics in Medicine 10 (4):585–598. doi:10.1002/sim.4780100410.
   PubMed Web of Science ®Google Scholar
• Schafer, J. L. 1999. Multiple imputation: A primer. Statistical Methods in Medical Research 8 (1):3–15. doi:10.1177/096228029900800102.
   PubMed Web of Science ®Google Scholar
• Sheng, D., and M. Y. Kim. 2006. The effects of non-compliance on intent-to-treat analysis of equivalence trials. Statistics in Medicine 25 (7):1183–1199. doi:10.1002/sim.2230.
   PubMed Web of Science ®Google Scholar
• Shih, W. J. 2002. Problems in dealing with missing data and informative censoring in clinical trials. Current Controlled Trials in Cardiovascular Medicine 3 (1):4. doi:10.1186/1468-6708-3-4.
   PubMedGoogle Scholar
• Siannis, F. 2004. Applications of a parametric model for informative censoring. Biometrics 60 (3):704–714. doi:10.1111/j.0006-341X.2004.00220.x.
   PubMed Web of Science ®Google Scholar
• Siannis, F., J. Copas, and G. Lu. 2005. Sensitivity analysis for informative censoring in parametric survival models. Biostatistics 6 (1):77–91. doi:10.1093/biostatistics/kxh019.
   PubMed Web of Science ®Google Scholar
• Stallard, N., and A. Whitehead. 1996. An alternative approach to the analysis of animal carcinogenicity studies. Regulatory Toxicology and Pharmacology 23 (3):244–248. doi:10.1006/rtph.1996.0049.
   PubMed Web of Science ®Google Scholar
• Taylor, J. M. G., S. Murray, and C. H. Hsu. 2002. Survival estimation and testing via multiple imputation. Statistics & Probability Letters 58 (3):221–232. doi:10.1016/S0167-7152(02)00030-5.
   Web of Science ®Google Scholar
• Templeton, A. J., E. Amir, and I. F. Tannock. 2020. Informative censoring — a neglected cause of bias in oncology trials. Clinical Oncology 17 (6):327–328. doi:10.1038/s41571-020-0368-0.
   Web of Science ®Google Scholar
• United States Food and Drug Administration: Guidance for Industry NonInferiority Clinical Trials 2010.
   Google Scholar
• US Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER) Center for Biologics Evaluation and Research (CBER). Non-inferiority clinical trials to establish effectiveness guidance for industry. Clinical/Medical, November 2016. https://www.fda.gov/downloads/Drugs/Guidances/UCM202140.pdf.
   Google Scholar
• Willems, S., A. Schat, M. van Noorden, and M. Fiocco. 2018. Correcting for dependent censoring in routine outcome monitoring data by applying the inverse probability censoring weighted estimator. Statistical Methods in Medical Research 27 (2):323–335. doi:10.1177/0962280216628900.
   PubMed Web of Science ®Google Scholar
• Yashin, A. I., J. W. Vaupel, and I. A. Iachine. 1995. Correlated individual frailty: An advantageous approach to survival analysis of bivariate data. Mathematical Population Studies 5 (2):145–159. doi:10.1080/08898489509525394.
   PubMedGoogle Scholar
• Zhang, J., and D. F. Heitjan. 2005. Nonignorable censoring in randomized clinical trials. Clinical Trials 2 (6):488–496. doi:10.1191/1740774505cn128oa.
   PubMed Web of Science ®Google Scholar
• Zhao, Y., A. H. Herring, H. Zhou, M. W. Ali, and G. G. Koch. 2014. A multiple imputation strategy for sensitivity analyses of time-to-event data with possibly informative censoring. Journal of Biopharmaceutical Statistics 24 (2):229–253. doi:10.1080/10543406.2013.860769.
   PubMed Web of Science ®Google Scholar