Skip to main content
SpringerLink
Log in
Book cover

Group Sequential and Confirmatory Adaptive Designs in Clinical Trials pp 241–276Cite as

Applications and Case Studies

  • Chapter
  • First Online:

Part of the book series: Springer Series in Pharmaceutical Statistics ((SSPS))

Abstract

In this chapter the general principle of adaptive combination tests is applied to two important situations in clinical trial. The first is the design with multiple treatment arms where, based on interim results, one or more arms are selected. The second is the design where one or more pre-specified subsets of a population are selected for further investigation, the latter designs are called adaptive enrichment designs. The combination testing principle together with the closed testing principle can be used in both settings. We will describe the procedures in detail, particularly, which intersection tests can be used for specific situations and provide examples for the assessment of these designs. We also provide real trial examples to illustrate how these designs were used in practice. We then discuss other types of adaptations that were discussed in the literature. In the last section of this chapter, we briefly discuss the added logistical and regulatory complexity when performing adaptive designs.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Barker, A., Sigman, C., Kelloff, G., Hylton, N., Berry, D., & Esserman, L. (2009). I–SPY 2: An adaptive breast cancer trial design in the setting ofneoadjuvant chemotherapy. Clinical Pharmacology and Therapeutics, 86, 97–100.

    Article  Google Scholar 

  • Barnes, P. J., Pocock, S. J., & Magnussen, H. (2010). Integrating Indacaterol dose selection in a clinical study in COPD using an adaptive seamless design. Pulmonary Pharmacology & Therapeutics, 23, 165–171.

    Article  Google Scholar 

  • Bauer, P., Bretz, F., Dragalin, V., König, F., & Wassmer, G. (2016). 25 years of confirmatory adaptive designs: Opportunities and pitfalls. Statistics in Medicine, 35, 325–347.

    Article  Google Scholar 

  • Bauer, P., & Einfalt, J. (2006). Application of adaptive designs - a review. Biometrical Journal, 8, 1–16.

    MathSciNet  Google Scholar 

  • Bauer, P., & Kieser, M. (1999). Combining different phases in the development of medical treatments within a single trial. Statistics in Medicine, 34, 1833–1848.

    Article  Google Scholar 

  • Brannath, W., & Bretz, F. (2010). Shortcuts for locally consonant closed test procedures. Journal of the American Statistical Association, 105, 660–669.

    Article  MathSciNet  MATH  Google Scholar 

  • Brannath, W., Burger, H. U., Glimm, E., Stallard, N., Vandemeulebroecke, M., & Wassmer, G. (2010). Comments on the “Draft guidance on adaptive design clinical trials for drugs and biologics” of the U.S. Food and Drug Administration. Journal of Biopharmaceutical Statistics, 20, 1125–1131.

    Article  MathSciNet  Google Scholar 

  • Brannath, W., Zuber, E., Branson, M., Bretz, F., Gallo, P., Posch, M., & Racine-Poon, A. (2009b). Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy on oncology. Statistics in Medicine, 28, 1445–1463.

    Article  MathSciNet  Google Scholar 

  • Bretz, F., König, F., Brannath, W., Glimm, E., & Posch, M. (2009a). Tutorial in biostatistics: Adaptive designs for confirmatory clinical trials. Statistics in Medicine, 28, 1181–1217.

    Article  MathSciNet  Google Scholar 

  • Bretz, F., Maurer, W., Brannath, W., & Posch, M. (2009b). A graphical approach to sequentially rejective multiple test procedures. Statistics in Medicine, 28, 586–604.

    Article  MathSciNet  Google Scholar 

  • Bretz, F., Pinheitro, J. C., & Branson, M. (2005). Combining multiple comparison and modeling techniques in dose-response studies. Biometrics, 61, 738–748.

    Article  MathSciNet  MATH  Google Scholar 

  • Bretz, F., Posch, M., Glimm, E., Klinglmüller, F., Maurer, W., & Rohmeyer, K. (2011). Graphical approaches for multiple comparison procedures using weighted Bonferroni, Simes, or parametric tests. Biometrical Journal, 53, 894–913.

    Article  MathSciNet  MATH  Google Scholar 

  • Bretz, F., Schmidli, H., König, F., Racine, A., & Maurer, W. (2006). Confirmatory seamless phase II/III clinical trials with hypotheses selection at interim: General concepts. Biometrical Journal, 48, 623–634.

    Article  MathSciNet  Google Scholar 

  • Bretz, F., & Wang, S. -J. (2010). From adaptive design to modern protocol design for drug development: Part II. success probabilities and effect estimates for phase 3 development programs. Drug Information Journal, 44, 333–342.

    Google Scholar 

  • Burman, C. -F., Sonesson, C., & Guilbaud, O. (2009). A recycling framework for the construction of Bonferroni-based multiple tests. Statistics in Medicine, 28, 739–761.

    Article  MathSciNet  Google Scholar 

  • Carreras, M., Gutjahr, G., & Brannath, W. (2015). Adaptive seamless designs with interim treatment selection: A case study in oncology. Statistics in Medicine, 34, 1261–1440.

    Article  MathSciNet  Google Scholar 

  • Chaturvedi, P. R., Antonijevic, Z., & Mehta, C. R. (2014). Practical considerations for a two-stage confirmatory adaptive clinical trial design and its implementation: ADVENT trial. In W. He, J. Pinheiro, & O. M. Kuznetsova (Eds.), Practical considerations for adaptive trial design and implementation (pp. 77–93). New York: Springer, Science and Business Media.

    Google Scholar 

  • DeMets, D. L., Friedman, L. M., & Furberg, C. D. (2006). Data monitoring in clinical trials. New York: Springer.

    Book  MATH  Google Scholar 

  • Di Scala, L., & Glimm, E. (2011). Time-to-event analysis with treatment arm selection at interim. Statistics in Medicine, 30, 3067–3081. (Correction in 2013 Statistics in Medicine, 32, 1974).

    Google Scholar 

  • Donohue, J. F., Fogarty, C., & Lötvall, J. (2010). Once-daily bronchodilators for chronic obstructive pulmonary disease: Indacaterol versus Tiotropium. American Journal of Respiratory and Critical Care Medicine, 182, 155–162.

    Article  Google Scholar 

  • Dragalin, V., Hsuan, F., & Padmanabhan, S. (2007). Adaptive designs for dose-finding studies based on sigmoid E-max model. Journal of Biopharmaceutical Statistics, 17, 1051–1070.

    Article  MathSciNet  Google Scholar 

  • Ellenberg, S. S., Fleming, T. R., & DeMets, D. L. (2003). Data monitoring committees in clinical trials: A practical perspective. Chichester: Wiley.

    Google Scholar 

  • EMA. (2006). Guideline on clinical trials in small populations (CHMP/EWP/83561/2005). London, UK: European Medicines Agency.

    Google Scholar 

  • EMA. (2007). Reflection paper on methodological issues in confirmatory clinical trials planned with an adaptive design. London, UK: European Medicines Agency.

    Google Scholar 

  • FDA. (2010). Draft guidance for industry. Adaptive design clinical trials for drugs and biologics. Food and Drug Administration. Center for Drug Evaluation and Research (CDER) and Center for Biologics Evaluation and Research (CBER), Rockville, MD.

    Google Scholar 

  • FDA. (2015). Draft guidance for industry and food and drug administration staff. Adaptive designs for medical device clinical studies. Food and Drug Administration. Center for Devices and Radiological Health (CDRH) and Center for Biologics Evaluation and Research (CBER), Rockville, MD.

    Google Scholar 

  • Finner, H., Roters, M., & Strassburger, K. (2015). On the Simes test under dependence. Statistical Papers, published online.

    Google Scholar 

  • Follmann, D. A., Proschan, M. A., & Geller, N. L. (1994). Monitoring pairwise comparisons in multi-armed clinical trials. Biometrics, 50, 325–336.

    Article  MathSciNet  MATH  Google Scholar 

  • Friede, T., Parsons, N., & Stallard, N. (2012). A conditional error function approach for subgroup selection in adaptive clinical trials. Statistics in Medicine, 31, 4309–4320 (Correction in 2014 Statistics in Medicine, 32, 2513–2514).

    Google Scholar 

  • Friede, T., & Stallard, N. (2008). A comparison of methods for adaptive treatment selection. Biometrical Journal, 50, 767–781.

    Article  MathSciNet  Google Scholar 

  • Gallo, P., DeMets, D. L., & LaVange, L. (2014). Considerations for interim analyses in adaptive trials, and perspectives on the use of DMCs. In W. He, J. Pinheiro, & O. M. Kuznetsova (Eds.), Practical considerations for adaptive trial design and implementation (pp. 259–272). New York: Springer, Science and Business Media.

    Google Scholar 

  • Gao, P., Liu, L., & Mehta, C. R. (2014). Adaptive sequential testing for multiple comparisons. Journal of Biopharmaceutical Statistics, 24, 1035–1058.

    Article  MathSciNet  Google Scholar 

  • Genz, A., & Bretz, F. (2009). Computation of multivariate normal and t probabilities (Vol. 45. p. 247, 279). New York: Springer.

    Book  MATH  Google Scholar 

  • Genz, A., Bretz, F., Miwa, T., Mi, X., Leisch, F., Scheipl, F., Bornkamp, B., Maechler, M., & Hothorn, T. (2014). mvtnorm: Multivariate normal and t distributions. http://cran.r-project.org/web/packages/mvtnorm. R package version 1.0-2.

  • Götte, H., Donica, M., & Mordenti, G. (2015). Improving probabilities of correct interim decision in population enrichment designs. Journal of Biopharmaceutical Statistics, 25, 1020–1038.

    Article  Google Scholar 

  • Graf, A. C., Posch, M., & König, F. (2015). Adaptive designs for subpopulation analysis optimizing utility functions. Biometrical Journal, 57, 76–89.

    Article  MathSciNet  MATH  Google Scholar 

  • Gutjahr, G., Brannath, W., & Bauer, P. (2011). An approach to the conditional error rate principle with nuisance parameters. Biometrics, 67, 1039–1046.

    Article  MathSciNet  MATH  Google Scholar 

  • Hampson, L. V., & Jennison, C. (2015). Optimizing the data combination rule for seamless phase II/III clinical trials. Statistics in Medicine, 34, 39–58.

    Article  MathSciNet  Google Scholar 

  • Hellmich, M. (2001). Monitoring clinical trials with multiple arms. Biometrics, 57, 892–898.

    Article  MathSciNet  MATH  Google Scholar 

  • Heritier, S., Lô, S. N., & Morgan, C. C. (2011). An adaptive confirmatory trial with treatment selection: practical experiences and unbalanced randomization. Statistics in Medicine, 30, 1541–1554.

    MathSciNet  Google Scholar 

  • Herson, J. (2009). Data and safety monitoring committees in clinical trials. Boca Raton: CRC Press.

    Book  Google Scholar 

  • Hommel, G. (2001). Adaptive modifications of hypotheses after an interim analysis. Biometrical Journal, 43, 581–589.

    Article  MathSciNet  MATH  Google Scholar 

  • Hommel, G., & Kropf, S. (2001). Clinical trials with an adaptive choice of hypotheses. Drug Information Journal, 35, 1423–1429.

    Article  Google Scholar 

  • Hung, H. M. J., Wang, S. -J., & O’Neill, R. T. (2011). Flexible design clinical trial methodology in regulatory applications. Statistics in Medicine, 30, 1519–1527.

    Article  MathSciNet  Google Scholar 

  • Hünseler, C., Balling, G., Röhlig, C., Blickheuser, R., Trieschmann, U., Lieser, U., Dohna-Schwake, C., Gebauer, C., Möller, O., Hering, F., T., H., Schubert, S., Hentschel, R., Huth, R. G., Müller, A., Müller, C., Wassmer, G., Hahn, M., Harnischmacher, U., Behr, J., & Roth, B. (2014). Continuous infusion of clonidine in ventilated newborns and infants: A randomized controlled trial. Pediatric Critical Care Medicine, 15, 511–522.

    Google Scholar 

  • Huque, M. F. (2016). Validity of the Hochberg procedure revisited for clinical trial applications. Statistics in Medicine, 35, 5–20.

    Article  Google Scholar 

  • Irle, S., & Schäfer, H. (2014). Interim design modifications in time-to-event studies. Journal of the American Statistical Association, 107, 341–348.

    Article  MathSciNet  MATH  Google Scholar 

  • Jenkins, M., Stone, A., & Jennison, C. (2011). An adaptive seamless phase II/III design for oncology trials with subpopulation selection using correlated survival endpoints. Pharmaceutical Statistics, 10, 347–356.

    Article  Google Scholar 

  • Jennison, C., & Turnbull, B. W. (2007). Adaptive seamless designs: Selection and prospective testing of hypotheses. Journal of Biopharmaceutical Statistics, 17, 1135–1161.

    Article  MathSciNet  Google Scholar 

  • Kelly, P. J., Stallard, N., & Todd, S. (2005). An adaptive group sequential design for phase II/III clinical trials that select a single treatment from several. Journal of Biopharmaceutical Statistics, 15, 641–658.

    Article  MathSciNet  Google Scholar 

  • Kieser, M. (2005). A note on adaptively changing the hierarchy of hypotheses in clinical trials with flexible design. Drug Information Journal, 39, 2215–2222.

    Google Scholar 

  • Kieser, M., Schneider, B., & Friede, T. (2002). A bootstrap procedure for adaptive selection of the test statistic in flexible two-stage designs. Biometrical Journal, 44, 641–652.

    Article  MathSciNet  Google Scholar 

  • Klinglmüller, F., Posch, M., & König, F. (2014). Adaptive graph-based multiple testing procedures. Pharmaceutical Statistics, 13, 345–346.

    Article  Google Scholar 

  • König, F., Brannath, W., Bretz, F., & Posch, M. (2008). Adaptive Dunnett tests for treatment selection. Statistics in Medicine, 27, 1612–1625.

    Article  MathSciNet  Google Scholar 

  • Krisam, J., & Kieser, M. (2014). Decision rules for subgroup selection based on a predictive biomarker. Journal of Biopharmaceutical Statistics, 24, 188–202.

    Article  MathSciNet  Google Scholar 

  • Kropf, S., Hommel, G., Schmidt, U., Brickwedel, J., & Jepsen, M. S. (2000). Multiple comparison of treatments with stable multivariate tests in a two-stage adaptive design, including a test for non-inferiority. Biometrical Journal, 42, 951–965.

    Article  MathSciNet  MATH  Google Scholar 

  • Lang, T., Auterith, A., & Bauer, P. (2000). Trendtests with adaptive scoring. Biometrical Journal, 42, 1007–1020.

    Article  MathSciNet  MATH  Google Scholar 

  • Lawrence, D., & Bretz, F. (2014). Approaches for optimal dose selection for adaptive design trials. In W. He, J. Pinheiro & O. M. Kuznetsova (Eds.), Practical considerations for adaptive trial design and implementation (pp. 125–137). New York: Springer, Science and Business Media.

    Google Scholar 

  • Lawrence, D., Bretz, F., & Pocock, S. (2014). Inhance: An adaptive confirmatory study with dose selection at interim. In A. Trifilieff (Ed.), Indacaterol - the first once-daily long-acting Beta2 Agonist for COPD (pp. 77–92). New York: Springer, Science and Business Media.

    Google Scholar 

  • Lawrence, J. (2002). Strategies for changing the test statistic during a clinical trial. Journal of Biopharmaceutical Statistics, 12, 193–205.

    Article  MathSciNet  Google Scholar 

  • Lehmacher, W., Kieser, M., & Hothorn, L. (2000). Sequential and multiple testing for dose-response analysis. Drug Information Journal, 34, 591–597.

    Google Scholar 

  • Lehmacher, W., Wassmer, G., & Reitmeir, P. (1991). Procedures for two-sample comparisons with multiple endpoints controlling the experimentwise error rate. Biometrics, 47, 511–521.

    Article  Google Scholar 

  • Maca, J., Bhattacharya, S., Dragalin, V., Gallo, P., & Krams, M. (2006). Adaptive seamless phase II/III designs — background, operational aspects, and examples. Drug Information Journal, 40, 463–473.

    Google Scholar 

  • Magirr, D., Jaki, T., König, F., & Posch, M. (2014a). Adaptive survival trials. arXiv preprint arXiv:1405.1569.

    Google Scholar 

  • Magirr, D., Jaki, T., Posch, M., & Klinglmüller, F. (2013). Simultaneous confidence intervals that are compatible with closed testing in adaptive designs. Biometrika, 100, 985–996.

    Article  MathSciNet  MATH  Google Scholar 

  • Magirr, D., Stallard, N., & Jaki, T. (2014b). Flexible sequential designs for multi-arm clinical trials. Statistics in Medicine, 33, 3269–3279.

    Article  MathSciNet  Google Scholar 

  • Mehta, C. R., & Gao, P. (2011). Population enrichment designs: Case study of a large multinational trial. Journal of Biopharmaceutical Statistics, 21, 831–845.

    Article  MathSciNet  Google Scholar 

  • Mehta, C. R., Gao, P., Bhatt, D. L., Harrington, R. A., Skerjanec, S., & Ware, J. H. (2009). Optimizing trial design sequential, adaptive, and enrichment strategies. Circulation, 119, 597–605.

    Article  Google Scholar 

  • Mehta, C. R., Schäfer, H., Daniel, H., & Irle, S. (2014). Biomarker driven population enrichment for adaptive oncology trials with time to event endpoints. Statistics in Medicine, 33, 4515–4531.

    Article  MathSciNet  Google Scholar 

  • Morgan, C. C., Huyck, S., Jenkins, M., Chen, L., Bedding, A., Coffey, C. S., Gaydos, B., & Wathen, J. K. (2014). Adaptive design: Results of 2012 survey on perception and use. Therapeutic Innovation & Regulatory Science, 48, 473–481.

    Article  Google Scholar 

  • Neuhäuser, M. (2001). An adaptive location-scale test. Biometrical Journal, 43, 809–819.

    Article  MathSciNet  MATH  Google Scholar 

  • O’Brien, P. C. (1984). Procedures for comparing samples with multiple endpoints. Biometrics, 40, 1079–1087.

    Article  MathSciNet  Google Scholar 

  • Ondra, T., Dmitrienko, A., Friede, T., Graf, A., Miller, F., Stallard, N., & Posch, M. (2016). Methods for identification and confirmation of targeted subgroups in clinical trials: A systematic review. Journal of Biopharmaceutical Statistics, 26, 99–119.

    Article  Google Scholar 

  • Posch, M., König, F., Branson, M., Brannath, W., Dunger-Baldauf, C., & Bauer, P. (2005). Testing and estimating in flexible group sequential designs with adaptive treatment selection. Statistics in Medicine, 24, 3697–3714.

    Article  MathSciNet  Google Scholar 

  • Posch, M., Timmesfeld, N., König, F., & Müller, H. -H. (2004). Conditional rejection probabilities of Student’s t-test and design adaptation. Biometrical Journal, 46, 389–403.

    Article  MathSciNet  Google Scholar 

  • Proschan, M. A., Follmann, D. A., & Geller, N. L. (1994). Monitoring multi-armed trials. Statistics in Medicine, 13, 1441–1452.

    Article  MATH  Google Scholar 

  • Rosenblum, M. (2015). Adaptive randomized trial designs that cannot be dominated by any standard design at the same total sample size. Biometrika, 102, 191–202.

    Article  MathSciNet  MATH  Google Scholar 

  • Rosenblum, M., & van der Laan, M. J. (2011). Optimizing randomized trial designs to distinguish which subpopulations benefit from treatment. Biometrika, 98, 845–860.

    Article  MathSciNet  MATH  Google Scholar 

  • Sarkar, S. K., & Chang, C. -K. (1997). The Simes method for multiple hypothesis testing with positively dependent test statistics. Journal of the American Statistical Association, 92, 1601–1608.

    Article  MathSciNet  MATH  Google Scholar 

  • Schmidli, H., Bretz, F., Racine, A., & Maurer, W. (2006). Confirmatory seamless phase II/III clinical trials with hypotheses selection at interim: Applications and practical considerations. Biometrical Journal, 48, 635–643.

    Article  MathSciNet  Google Scholar 

  • Senn, S., & Bretz, F. (2007). Power and sample size when multiple endpoints are considered. Pharmaceutical Statistics, 6, 161–170.

    Article  Google Scholar 

  • Spiessens, B., & Debois, M. (2010). Adjusted significance levels for subgroup analysis in clinical trials. Contemporary Clinical Trials, 31, 647–656.

    Article  Google Scholar 

  • Stallard, N. (2010). A confirmatory seamless phase II/III clinical trial design incorporating short-term endpoint information. Statistics in Medicine, 29, 959–971.

    MathSciNet  Google Scholar 

  • Stallard, N., & Friede, T. (2008). A group-sequential design for clinical trials with treatment selection. Statistics in Medicine, 27, 6209–6227.

    Article  MathSciNet  Google Scholar 

  • Stallard, N., Hamborg, T., Parsons, N., & Friede, T. (2014). Adaptive designs for confirmatory clinical trials with subgroup selection. Journal of Biopharmaceutical Statistics, 24, 168–187.

    Article  MathSciNet  Google Scholar 

  • Stallard, N., & Todd, S. (2003). Sequential designs for phase III clinical trials incorporating treatment selection. Statistics in Medicine, 22, 689–703.

    Article  Google Scholar 

  • Sugitani, T., Bretz, F., & Maurer, W. (2014). A simple and flexible graphical approach for adaptive group-sequential clinical trials. Journal of Biopharmaceutical Statistics, 55, 341–359.

    Google Scholar 

  • Sugitani, T., Hamasaki, T., & Hamada, C. (2013). Partition testing in confirmatory adaptive designs with structured objectives. Biometrical Journal, 55, 341–359.

    Article  MathSciNet  MATH  Google Scholar 

  • Temple, R. (1994). Special study designs: Early escape, enrichment, studies in non-responders. Communications in Statistics - Theory and Methods, 23, 499–531.

    Article  MATH  Google Scholar 

  • Timmesfeld, N., Schäfer, H., & Müller, H. -H. (2007). Increasing the sample size during clinical trials with t-distributed test statistics without inflating the Type I error rate. Statistics in Medicine, 26, 2449–2464.

    Article  MathSciNet  Google Scholar 

  • Tournoux-Facon, C., De Ryckee, Y., & Tubert-Bitter, P. (2011a). How a new stratified adaptive phase II design could improve targeting population. Statistics in Medicine, 30, 1555–1562.

    Article  MathSciNet  Google Scholar 

  • Tournoux-Facon, C., De Ryckee, Y., & Tubert-Bitter, P. (2011b). Targeting population entering phase III trials: A new stratified adaptive phase II design. Statistics in Medicine, 30, 801–811.

    Article  MathSciNet  Google Scholar 

  • Wang, S. -J. (2014). A commentary on the US FDA adaptive design draft guidance and EMA reflection paper from a regulatory perspective and regulatory experiences. In W. He, J. Pinheiro, & O. M. Kuznetsova (Eds.), Practical considerations for adaptive trial design and implementation (pp. 43–68). New York: Springer, Science and Business Media.

    Google Scholar 

  • Wang, S. -J., Hung, H. M. J., & O’Neill, R. T. (2009). Adaptive patient enrichment designs in therapeutic trials. Biometrical Journal, 51, 358–374.

    Article  MathSciNet  Google Scholar 

  • Wang, S.-J., O’Neill, R. T., & Hung, H. M. J. (2007). Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset. Pharmaceutical Statistics, 6, 227–244.

    Article  Google Scholar 

  • Wassmer, G. (2006). Planning and analyzing adaptive group sequential survival trials. Biometrical Journal, 48, 714–729.

    Article  MathSciNet  Google Scholar 

  • Wassmer, G. (2011). On sample size determination in multi-armed confirmatory adaptive designs. Journal of Biopharmaceutical Statistics, 21, 802–817.

    Article  MathSciNet  Google Scholar 

  • Wassmer, G., & Dragalin, V. (2015). Designing issues in confirmatory adaptive population enrichment trials. Journal of Biopharmaceutical Statistics, 25, 651–669.

    Article  Google Scholar 

  • Wassmer, G., Reitmeir, P., Kieser, M., & Lehmacher, W. (1999). Procedures for testing multiple endpoints in clinical trials: An overview. Journal of Statistical Planning and Inference, 82, 69–81.

    Article  MathSciNet  MATH  Google Scholar 

  • Zeymer, U., Suryapranata, H., Monassier, J. P., Opolski, G., Davies, J., Rasmanis, G., Linssen, G., Tebbe, U., Schröder, R., Tiemann, R., Machnig, T., & Neuhaus, K. L. (2001). The Na+/H+ exchange inhibitor eniporide as an adjunct to early reperfusion therapy for acute myocardial infarction. Results of the evaluation of the safety and cardioprotective effects of eniporide in acute myocardial infarction (ESCAMI) trial. Journal of the American College of Cardiology, 38, E1644–E1650.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Cologne, Cologne, Germany

    Gernot Wassmer

  2. University of Bremen, Bremen, Germany

    Werner Brannath

Authors
  1. Gernot Wassmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Werner Brannath
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Wassmer, G., Brannath, W. (2016). Applications and Case Studies. In: Group Sequential and Confirmatory Adaptive Designs in Clinical Trials. Springer Series in Pharmaceutical Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-32562-0_11

Download citation

Publish with us

Policies and ethics

Search

Navigation