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Interim Analyses: Design and Analysis Considerations for Survival Trials When Hazards May Be Nonproportional

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Biopharmaceutical Applied Statistics Symposium

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

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Abstract

In the decade following the introduction of the group-sequential concept by Pocock (1977), many publications emerged investigating and furthering this concept. In the late 1980s publications by Bauer (Biometrie und Informatik in Medizin und Biologie 20(4):130–148, 1989) (“Multistage Testing with Adaptive Design”) , Wittes and Brittain (1990) (“The role of internal pilot studies in increasing the efficiency of clinical trials”), as well as Gould and Shih (1992), Gould (1992) and Shih (1993) ushered in the era of adaptive designs—statistical research in this area flourished for the next two decades. Group-sequential methods became viewed as part of the broader category of adaptive methods.

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References

  • Bauer, P. (1989). Multistage testing with adaptive designs. Biometrie und Informatik in Medizin und Biologie, 20(4), 130–148.

    MathSciNet  Google Scholar 

  • Fleming, T. R., & Harrington, D. P. (1991). Counting processes and survival analysis. Hoboken, NJ: Wiley-Interscience.

    MATH  Google Scholar 

  • Gill, R. D. (1980). Censoring and stochastic integrals. Amsterdam: Mathematisch Centrum.

    MATH  Google Scholar 

  • Gould, A. L. (1992). Interim analyses for monitoring clinical trials that do not materially affect the type I error rate. Statistics in Medicine, 11(1), 55–66.

    Google Scholar 

  • Gould, A. L. & Pecore, V. J. (1982). Group sequential methods for clinical trials allowing early acceptance of Ho and incorporating costs. Biometrika, 69(1), 75–80.

    Google Scholar 

  • Gould, A. L., & Shih, W. J. (1992). Sample size re-estimation without unblinding for normally distributed outcomes with unknown variance. Communications in Statistics-Theory and Methods, 21(10), 2833–2853.

    Google Scholar 

  • Halperin, M., Rogot, E., Gurian, J., & Ederer, F. (1968). Sample sizes for medical trials with special reference to long-term therapy. Journal of chronic diseases, 21(1), 13–24.

    Article  Google Scholar 

  • Harrington, D. P., & Fleming, T. R. (1982). A class of rank test procedures for censored survival data. Biometrika, 69, 553–566.

    Google Scholar 

  • Hwang, I. K., Shih, W. J., & DeCani, J. S. (1990). Group sequential designs using a family of type I error probability spending functions. Statistics in Medicine, 9, 1439–1445. Cited on p. 144.

    Google Scholar 

  • Jennison, C., & Turnbull, B. W. (2000). Group sequential methods with applications to clinical trials. Boca Raton: Chapman & Hall.

    MATH  Google Scholar 

  • Kim, K., & Demets, D. L. (1987). Design and analysis of group sequential tests based on the type I error spending rate function. Biometrika, 74, 149–154.

    Google Scholar 

  • Lakatos, E. (1986). Sample size determination in clinical trials with time-dependent rates of losses and noncompliance. Controlled Clinical Trials, 7(3), 189–199.

    Article  Google Scholar 

  • Lakatos, E., (1988). Sample sizes based on the log-rank statistic in complex clinical trials. Biometrics, 229–241.

    Google Scholar 

  • Lakatos, E. (2002). Designing complex group sequential survival trials. Statistics in Medicine, 21(14), 1969–1989.

    Article  Google Scholar 

  • Lakatos, E. (2015). Optimizing group-sequential designs with focus on adaptability: Implications of nonproportional hazards in clinical trials. In W. R. Young, & D.-G Chen (Eds.), Clinical trial biostatistics and biopharmaceutical applications (Chapter 7, pp. 138–178). Boca Raton: Chapman & Hall/CRC.

    Google Scholar 

  • Lakatos, E. (2016). Sample size for survival trials in cancer. In S. L. George, X. Wang, & H. Pang (Eds.), Cancer clinical trials: Current and controversial issues in design and analysis (Chapter 8, pp. 235–277). Boca Raton: CRC Press, Chapman & Hall.

    Google Scholar 

  • Lan, K. G., & DeMets, D. L. (1983). Discrete sequential boundaries for clinical trials. Biometrika, 70(3), 659–663.

    Article  MathSciNet  Google Scholar 

  • Lan, K. G., & Wittes, J. (1988). The B-value: A tool for monitoring data. Biometrics, 579–585.

    Google Scholar 

  • Lan, K. K., & Zucker, D. M. (1993). Sequential monitoring of clinical trials: The role of information and Brownian motion. Statistics in Medicine, 12(8), 753–765.

    Article  Google Scholar 

  • Mantel, N. (1966). Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemotherapy Reports, 50, 163–170.

    Google Scholar 

  • Mehta, C. R., & Pocock, S. J. (2011). Adaptive increase in sample size when interim results are promising: A practical guide with examples. Statistics in Medicine, 30(28), 3267–3284.

    Article  MathSciNet  Google Scholar 

  • O’Brien, P. C., & Fleming, T. R. (1979). A multiple testing procedure for clinical trials. Biometrics, 549–556.

    Google Scholar 

  • Perren, T. J., Swart, A. M., Pfisterer, J., Ledermann, J. A., Pujade-Lauraine, E., Kristensen, G., et al. (2011). A phase 3 trial of bevacizumab in ovarian cancer. New England Journal of Medicine, 365(26), 2484–2496.

    Article  Google Scholar 

  • Pocock, S. J. (1977). Group sequential methods in the design and analysis of clinical trials. Biometrika, 64(2), 191–199.

    Google Scholar 

  • Proschan, M. A., Follmann, D. A., & Waclawiw, M. A. (1992). Effects of assumption violations on type I error rate in group sequential monitoring. Biometrics, 1131–1143.

    Google Scholar 

  • Proschan, M. A., & Hunsberger, S. A. (1995). Designed extension of studies based on conditional power. Biometrics, 1315–1324.

    Google Scholar 

  • Sacks, F. M., Pfeffer, M. A., Moye, L. A., Rouleau, J. L., Rutherford, J. D., Cole, T. G., et al. (1996). The effect of pravastatin on coronary events after myocardial infarction in patients with average cholesterol levels. New England Journal of Medicine, 335(14), 1001–1009.

    Article  Google Scholar 

  • Shih, W. J. (1993). Sample size reestimation for triple blind clinical trials. Drug Information Journal, 27(3), 761–764.

    Google Scholar 

  • Snapinn, S. M. (1992). Monitoring clinical trials with a conditional probability stopping rule. Statistics in Medicine, 11(5), 659–672.

    Article  Google Scholar 

  • Schoenfeld, D. (1981). The asymptotic properties of nonparametric tests for comparing survival distributions. Biometrika, 68, 316–319.

    Google Scholar 

  • Self, S. G. (1991). An adaptive weighted log-rank test with application to cancer prevention and screening trials. Biometrics, 975–986.

    Google Scholar 

  • Tarone, R. E., & Ware, J. (1977). On distribution-free tests for equality of survival distributions. Biometrika, 64, 156–160.

    Google Scholar 

  • Whitehead, J. (1997). The design and analysis of sequential clinical trials. Chichester: Wiley.

    MATH  Google Scholar 

  • Wittes, J. & Brittain, E. (1990). The role of internal pilot studies in increasing the efficiency of clinical trials. Statistics in Medicine, 9(1–2), 65–72.

    Google Scholar 

  • Yang, S., & Prentice, R. (2010). Improved logrank-type tests for survival data using adaptive weights. Biometrics, 66(1), 30–38.

    Article  MathSciNet  Google Scholar 

  • Zucker, D. M., & Lakatos, E. (1990). Weighted log rank type statistics for comparing survival curves when there is a time lag in the effectiveness of treatment. Biometrika, 77, 853–864.

    Google Scholar 

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Author information

Authors and Affiliations

  1. BiostatHaven, Inc., Croton-on-Hudson, USA

    Edward Lakatos

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  1. Edward Lakatos
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Correspondence to Edward Lakatos .

Editor information

Editors and Affiliations

  1. Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia, USA

    Karl E. Peace

  2. School of Social Work & Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA

    Ding-Geng Chen

  3. Boston University, Cambridge, Massachusetts, USA

    Sandeep Menon

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Lakatos, E. (2018). Interim Analyses: Design and Analysis Considerations for Survival Trials When Hazards May Be Nonproportional. In: Peace, K., Chen, DG., Menon, S. (eds) Biopharmaceutical Applied Statistics Symposium . ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-7829-3_14

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