Literature DB >> 26083135

Characterization of dose-response for count data using a generalized MCP-Mod approach in an adaptive dose-ranging trial.

Francois Mercier1, Bjoern Bornkamp2, David Ohlssen3, Erik Wallstroem2.   

Abstract

Understanding the dose-response relationship is a key objective in Phase II clinical development. Yet, designing a dose-ranging trial is a challenging task, as it requires identifying the therapeutic window and the shape of the dose-response curve for a new drug on the basis of a limited number of doses. Adaptive designs have been proposed as a solution to improve both quality and efficiency of Phase II trials as they give the possibility to select the dose to be tested as the trial goes. In this article, we present a 'shapebased' two-stage adaptive trial design where the doses to be tested in the second stage are determined based on the correlation observed between efficacy of the doses tested in the first stage and a set of pre-specified candidate dose-response profiles. At the end of the trial, the data are analyzed using the generalized MCP-Mod approach in order to account for model uncertainty. A simulation study shows that this approach gives more precise estimates of a desired target dose (e.g. ED70) than a single-stage (fixed-dose) design and performs as well as a two-stage D-optimal design. We present the results of an adaptive model-based dose-ranging trial in multiple sclerosis that motivated this research and was conducted using the presented methodology.
Copyright © 2015 John Wiley & Sons, Ltd.

Entities:  

Keywords:  Emax model; dose-finding; multiple sclerosis; non-linear regression; optimal design; two-stage design

Mesh:

Substances:

Year:  2015        PMID: 26083135     DOI: 10.1002/pst.1693

Source DB:  PubMed          Journal:  Pharm Stat        ISSN: 1539-1604            Impact factor:   1.894


  3 in total

1.  Model selection and averaging of nonlinear mixed-effect models for robust phase III dose selection.

Authors:  Yasunori Aoki; Daniel Röshammar; Bengt Hamrén; Andrew C Hooker
Journal:  J Pharmacokinet Pharmacodyn       Date:  2017-11-04       Impact factor: 2.745

2.  Optimal adaptive allocation using deep reinforcement learning in a dose-response study.

Authors:  Kentaro Matsuura; Junya Honda; Imad El Hanafi; Takashi Sozu; Kentaro Sakamaki
Journal:  Stat Med       Date:  2021-11-07       Impact factor: 2.497

3.  Advanced Methods for Dose and Regimen Finding During Drug Development: Summary of the EMA/EFPIA Workshop on Dose Finding (London 4-5 December 2014).

Authors:  F T Musuamba; E Manolis; N Holford; Sya Cheung; L E Friberg; K Ogungbenro; M Posch; Jwt Yates; S Berry; N Thomas; S Corriol-Rohou; B Bornkamp; F Bretz; A C Hooker; P H Van der Graaf; J F Standing; J Hay; S Cole; V Gigante; K Karlsson; T Dumortier; N Benda; F Serone; S Das; A Brochot; F Ehmann; R Hemmings; I Skottheim Rusten
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2017-07-19
  3 in total

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