Literature DB >> 23592713

An adaptive clinical trials procedure for a sensitive subgroup examined in the multiple sclerosis context.

Corinne A Riddell1, Yinshan Zhao2, John Petkau3.   

Abstract

The biomarker-adaptive threshold design (BATD) allows researchers to simultaneously study the efficacy of treatment in the overall group and to investigate the relationship between a hypothesized predictive biomarker and the treatment effect on the primary outcome. It was originally developed for survival outcomes for Phase III clinical trials where the biomarker of interest is measured on a continuous scale. In this paper, generalizations of the BATD to accommodate count biomarkers and outcomes are developed and then studied in the multiple sclerosis (MS) context where the number of relapses is a commonly used outcome. Through simulation studies, we find that the BATD has increased power compared with a traditional fixed procedure under varying scenarios for which there exists a sensitive patient subgroup. As an illustration, we apply the procedure for two hypothesized markers, baseline enhancing lesion count and disease duration at baseline, using data from a previously completed trial. MS duration appears to be a predictive marker relationship for this dataset, and the procedure indicates that the treatment effect is strongest for patients who have had MS for less than 7.8 years. The procedure holds promise of enhanced statistical power when the treatment effect is greatest in a sensitive patient subgroup.
© The Author(s) 2013.

Entities:  

Keywords:  adaptive designs; biomarkers; clinical trials; negative binomial regression

Mesh:

Substances:

Year:  2013        PMID: 23592713     DOI: 10.1177/0962280213480576

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  3 in total

1.  Baseline EDSS proportions in MS clinical trials affect the overall outcome and power: A cautionary note.

Authors:  Guoqiao Wang; Gary R Cutter; Stacey S Cofield; Fred Lublin; Jerry S Wolinsky; Tarah Gustafson; Stephen Krieger; Amber Salter
Journal:  Mult Scler       Date:  2016-09-28       Impact factor: 6.312

Review 2.  Clinical trial designs incorporating predictive biomarkers.

Authors:  Lindsay A Renfro; Himel Mallick; Ming-Wen An; Daniel J Sargent; Sumithra J Mandrekar
Journal:  Cancer Treat Rev       Date:  2016-01-05       Impact factor: 12.111

3.  Constructing treatment selection rules based on an estimated treatment effect function: different approaches to take stochastic uncertainty into account have a substantial effect on performance.

Authors:  Maren Eckert; Werner Vach
Journal:  BMC Med Res Methodol       Date:  2019-08-01       Impact factor: 4.615

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.