BACKGROUND: As our understanding of the etiology and mechanisms of cancer becomes more sophisticated and the number of therapeutic options increases, phase I oncology trials today have multiple primary objectives. Many such designs are now "seamless," meaning that the trial estimates both the maximum tolerated dose and the efficacy at this dose level. Sponsors often proceed with further study only with this additional efficacy evidence. However, with this increasing complexity in trial design, it becomes challenging to articulate fundamental operating characteristics of these trials, such as (1) what is the probability that the design will identify an acceptable, that is., safe and efficacious, dose level? or (2) how many patients will be assigned to an acceptable dose level on average? METHODS: In this manuscript, we propose a new modular framework for designing and evaluating seamless oncology trials. Each module is comprised of either a dose assignment step or a dose-response evaluation, and multiple such modules can be implemented sequentially. We develop modules from existing phase I/II designs as well as a novel module for evaluating dose-response using a Bayesian isotonic regression scheme. RESULTS: We also demonstrate a freely available R package called seamlesssim to numerically estimate, by means of simulation, the operating characteristics of these modular trials. CONCLUSIONS: Together, this design framework and its accompanying simulator allow the clinical trialist to compare multiple different candidate designs, more rigorously assess performance, better justify sample sizes, and ultimately select a higher quality design.
BACKGROUND: As our understanding of the etiology and mechanisms of cancer becomes more sophisticated and the number of therapeutic options increases, phase I oncology trials today have multiple primary objectives. Many such designs are now "seamless," meaning that the trial estimates both the maximum tolerated dose and the efficacy at this dose level. Sponsors often proceed with further study only with this additional efficacy evidence. However, with this increasing complexity in trial design, it becomes challenging to articulate fundamental operating characteristics of these trials, such as (1) what is the probability that the design will identify an acceptable, that is., safe and efficacious, dose level? or (2) how many patients will be assigned to an acceptable dose level on average? METHODS: In this manuscript, we propose a new modular framework for designing and evaluating seamless oncology trials. Each module is comprised of either a dose assignment step or a dose-response evaluation, and multiple such modules can be implemented sequentially. We develop modules from existing phase I/II designs as well as a novel module for evaluating dose-response using a Bayesian isotonic regression scheme. RESULTS: We also demonstrate a freely available R package called seamlesssim to numerically estimate, by means of simulation, the operating characteristics of these modular trials. CONCLUSIONS: Together, this design framework and its accompanying simulator allow the clinical trialist to compare multiple different candidate designs, more rigorously assess performance, better justify sample sizes, and ultimately select a higher quality design.
Authors: Arif Manji; Irene Brana; Eitan Amir; George Tomlinson; Ian F Tannock; Philippe L Bedard; Amit Oza; Lillian L Siu; Albiruni R Abdul Razak Journal: J Clin Oncol Date: 2013-10-14 Impact factor: 44.544
Authors: Philip S Boonstra; Jincheng Shen; Jeremy M G Taylor; Thomas M Braun; Kent A Griffith; Stephanie Daignault; Gregory P Kalemkerian; Theodore S Lawrence; Matthew J Schipper Journal: J Natl Cancer Inst Date: 2015-02-20 Impact factor: 13.506
Authors: Brian P Hobbs; Pedro C Barata; Yada Kanjanapan; Channing J Paller; Jane Perlmutter; Gregory R Pond; Tatiana M Prowell; Eric H Rubin; Lesley K Seymour; Nolan A Wages; Timothy A Yap; David Feltquate; Elizabeth Garrett-Mayer; William Grossman; David S Hong; S Percy Ivy; Lillian L Siu; Steven A Reeves; Gary L Rosner Journal: J Natl Cancer Inst Date: 2019-02-01 Impact factor: 13.506
Authors: Alexander J Bridges; Ranjit K Mehta; Sushmita Shukla; Matthew J Schipper; Theodore S Lawrence; Mukesh K Nyati Journal: Oncotarget Date: 2021-05-25