597
Views
11
CrossRef citations to date
0
Altmetric
Original Articles

Some Challenges With Statistical Inference in Adaptive Designs

, &
Pages 1059-1072 | Received 01 Jul 2013, Accepted 23 Sep 2013, Published online: 11 Aug 2014

REFERENCES

  • Bauer, P., Köhne, K. (1994). Evaluations of experiments with adaptive interim analyses. Biometrics 50:1029–1041.
  • Bauer, P., König, F. (2006). The reassessment of trial perspectives from interim data—A critical view. Statistics in Medicine 25:23–36.
  • Brannath, W., Posch, M., Bauer, P. (2002). Recursive combination tests. Journal of the American Statistical Association 97:236–244.
  • Brannath, W., Zuber, E., Branson, M., Bretz, F., Gallo, P., Posch, M., Racine-Poon, A. (2009). Confirmatory Adaptive designs with Bayesian decision tools for a targeted therapy in oncology. Statistics in Medicine 28:1445–1463.
  • Branson, M., Brannah, W., Dunger-Baldauf, C., Bauer, P. (2005). Testing and estimation in flexible group sequential design with adaptive treatment selection. Statistics in Medicine 24:3697–3714.
  • Chen, Y. H., DeMets, D. L., Lan, K. K. (2004). Increasing the sample size when the unblinded interim result is promising. Statistics in Medicine 2004; 23:1023–1038.
  • Chow, S. C., Chang, M. (2006). Adaptive Design Methods in Clinical Trials. New York, NY: Chapman and Hall/CRC Press, Taylor & Francis.
  • Cui, L., Hung, H. M. J., Wang, S. J. (1999). Modification of sample size in group sequential clinical trials. Biometrics 55:321–324.
  • Denne, J. S. (2001). Sample size recalculation using conditional power. Statistics in Medicine 20:2645–2660.
  • Emerson, S. S., Levin, G. P., Emerson, S.C. (2011). Comments on ‘Adaptive increase in sample size when interim results are promising:a practical guide with examples.’ Statistics in Medicine 30:3285–3301.
  • Food and Drug Administration. (2010). FDA draft guidance for industry: Adaptive design clinical trials for drugs and biologics. Available at www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM201790.pdf ( released for public comments February 25).
  • Gao, P., Ware, J. H., Mehta, C. R. (2008). Sample size re-estimation for adaptive sequential design in clinical trials. Journal of Biopharmaceutical Statistics 18:1184–1196.
  • Hung, H. M. J., O’Neill, R., Wang, S. J., Lawrence, J. (2006). A regulatory view on adaptive/flexible clinical trial design (with rejoinder). Biometrical Journal 48: 565–573, 613–615.
  • Hung, H. M. J., Wang, S. J. (2012). Sample size adaptation in fixed-dose combination drug trial. Journal of Biopharmaceutical Statistics 22:679–686.
  • Hung, H. M. J., Wang, S. J., O’Neill, R. (2006). Methodological issues with adaptation of clinical trial design. Pharmaceutical Statistics 5:99–107.
  • Hung, H. M. J., Wang, S. J., O’Neill, R. (2011). Flexible design clinical trial methodology in regulatory applications. Statistics in Medicine 30:1519–1527.
  • König, F., Brannah,W., Bretz, F., Posch, M. (2008). Adaptive Dunnett tests for treatment selection. Statistics in Medicine 27:1612–1625
  • Lan, K. K. G., Wittes, J. (1988). The B-value: Maryland tool for monitoring data. Biometrics 44:579–585.
  • Lawrence, J., Hung, H. M. J. (2003). Estimation and confidence intervals after adjusting the maximum information. Biometrical Journal 45:143–152.
  • Lehmacher, W., Wassmer, G. (1999). Adaptive sample size calculation in group sequential trials. Biometrics 55:1286–1290.
  • Liu, Q., Andersen, K. M. (2008). On adaptive extensions of group sequential trials for clinical investigations. Journal of the American Statistical Association 103:3267–3284.
  • Liu, Q., Proschan, M. A., Pledger, G W. (2002). A unified theory of two-stage adaptive designs. Journal of the American Statistical Association 97:1034–1041.
  • 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.
  • 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 30:3267–3284.
  • Müller, H. H., Shäfer, H. (2001). Adaptive group sequential designs for clinical trials: Combining the advantages of adaptive and of classical group sequential approaches. Biometrics 57:886–891.
  • Posch, M., Bauer, P., Brannath, W. (2003). Issues in designing flexible trials. Statistics in Medicine 22:953–969.
  • Posch, M., Maurer, W., Bretz, F. (2010). Type I error rate control in adaptive designs for confirmatory clinical trials with treatment selection at interim. Pharmaceutical Statistics 10:96–104.
  • Proschan, M. A., Hunsberger, S. A. (1995). Designed extension of studies based on conditional power. Biometrics 51:1315–1324.
  • Shi, W. J. (2001). Comment on Type I error in sample size re-estimation based on observed treatment difference ( p 497–513). Statistics in Medicine 20: 515–518.
  • Shun, Z., Yuan, W., Brady, W. E., Hsu, H. (2001). Type I error in sample size re-estimations based on observed treatment difference. Statistics in Medicine 20:497–513.
  • Wang, S. J., Brannath, W., Brückner, M., Hung, H. M. J., Koch, A. (2013). Unblinded adaptive information design Based on clinical endpoint or biomarker. Statistics in Biopharmaceutical Research 5:293–310.
  • Wang, S. J., Hung, H. M. J. (2013). Adaptive enrichment with subpopulation selection at interim: methodologies, applications and design considerations. Contemporary Clinical Trials 36:673–681.
  • Wang, S. J., Hung, H. M. J., O’Neill, R. T. (2009). Adaptive patient enrichment designs in therapeutic trials. Biometrical Journal 51:358–374.
  • Wang, S. J., Hung, H. M. J., O’Neill, R. (2010). Adaptive design clinical trials in CNS drug development. European Neuropsychopharmacology Journal 21:159–166.
  • Wang, S. J., Hung, H. M. J., O’Neill, R. T. (2010). Impacts of Type I error rate with inappropriate use of learn for confirm in adaptive designs. Biometrical Journal 52:798–810.
  • Wang, S. J., Hung, H. M. J., O’Neill, R. T. (2012). Paradigms for adaptive statistical information designs: practical experiences and strategies. Statistics in Medicine 31:3011–3023.
  • 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 6:227–224.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.