Skip to main content
SpringerLink
Log in

Adaptive designs for drug combination informed by longitudinal model for the response

  • Regular Article
  • Published:
Statistical Papers Aims and scope Submit manuscript

A Publisher Correction to this article was published on 12 September 2019

This article has been updated

Abstract

Objectives in Phase II drug combination studies are to estimate the efficacy response surface for the combination of doses of different drugs and to select the most efficient combination for the final Phase III clinical trial. One problem is to find an optimal design that allocates subjects to the dose-combinations which will maximize the information obtained in the trial. Adaptive designs help in these situations to ensure high efficiency of the study design. We are using a binary efficacy endpoint and consider the practical situation when the timing of the endpoint assessment period on the subject level is considerably longer relative to the inter-arrival time of subjects. This poses implementation challenges for the adaptive design. A solution to the adaptive design problem by using time-to-event models as longitudinal model will be presented.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Change history

  • 12 September 2019

    Unfortunately, due to a technical error, the articles published in issues 60:2 and 60:3 received incorrect pagination. Please find here the corrected Tables of Contents. We apologize to the authors of the articles and the readers.

References

  • Atkinson AC, Donev AN, Tobias RD (2007) Optimum Experimental Designs, with SAS. Oxoford University Press, Oxford

    MATH  Google Scholar 

  • Atkinson AC, Fedorov VV, Herzberg AM, Zhang R (2014) Elemental information matrices and optimal experimental design for generalized regression models. J Stat Plan Inference 144:81–91

    Article  MathSciNet  MATH  Google Scholar 

  • Cai C, Liu S, Yuan Y (2014) A Bayesian design for phase II clinical trials with delayed responses based on multiple imputation. Stat Med 33:4017–4028

    Article  MathSciNet  Google Scholar 

  • Fedorov VV, Leonov SL (2013) Optimal Design for Nonlinear Response Models. Chapman & Hall/CRC Biostatistics Series, Boca Raton

    Book  MATH  Google Scholar 

  • Finney D (1971) Probit Analysis. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  • Kiefer J, Wolfowitz J (1960) The equivalence of two extremum problems. Can J Math 12:363–366

    Article  MathSciNet  MATH  Google Scholar 

  • Lehmann EL, Casella G (1998) Theory of Point Estimation, 2nd edn. Springer, New York

    MATH  Google Scholar 

  • Liu S, Ning J (2013) A Bayesian dose-finding design for drug combination trials with delayed toxicities. Bayesian Anal 8:703–722

    Article  MathSciNet  MATH  Google Scholar 

  • Pronzato L, Pazman A (2013) Design of Experiments in Nonlinear Models. Springer, New York

    Book  MATH  Google Scholar 

  • Wu CFJ (1988) Optimal design for percentile estimation of a quantal response curve. In: Dodge Y, Fedorov VV, Wynn HP (eds) Optimal Design and Analysis of Experiments. Elsevier, North Holland, pp 213–223

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Janssen, Johnson & Johnson Platz 1, 41470, Neuss, Germany

    Tobias Mielke

  2. Janssen, 1400 McKean Rd, Spring House, PA, 19477, USA

    Vladimir Dragalin

Authors
  1. Tobias Mielke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vladimir Dragalin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tobias Mielke.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mielke, T., Dragalin, V. Adaptive designs for drug combination informed by longitudinal model for the response. Stat Papers 60, 355–371 (2019). https://doi.org/10.1007/s00362-018-01073-9

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00362-018-01073-9

Keywords

Search

Navigation