Literature DB >> 32926652

Continual reassessment method with regularization in phase I clinical trials.

Xiang Li1, Anastasia Ivanova2, Hong Tian1, Pilar Lim3, Kevin Liu4.   

Abstract

Many Phase I trial designs have been developed to improve upon the standard 3+3 design. These designs can be classified as long-memory designs, for example, the continual reassessment method (CRM), and short-memory designs such as the modified toxicity probability interval (mTPI) design. Long-term memory designs use all data but their performance can be negatively affected by the model misspecification. Short-term memory designs only use data at the current dose and might lose efficiency as a result. To overcome these issues, we propose a regularized CRM (rCRM). The rCRM offers a trade-off between long-term memory and short-term memory methods. The rCRM gives more weight to data obtained at the doses with the estimated probability of toxicity closer to the target toxicity rate. The addition of a regularization term has an effect of shrinking the dimension of the model and leads to improved performance of the 2-parameter CRM. The rCRM is a good design choice to guide assignments in an expansion cohort phase of a dose-finding trial since dose assignments do not seem to change as often as in corresponding CRMs.

Entities:  

Keywords:  Continual reassessment method; dose-expansion; dose-finding; regularization

Mesh:

Year:  2020        PMID: 32926652      PMCID: PMC7954799          DOI: 10.1080/10543406.2020.1818251

Source DB:  PubMed          Journal:  J Biopharm Stat        ISSN: 1054-3406            Impact factor:   1.051


  16 in total

1.  Non-parametric optimal design in dose finding studies.

Authors:  John O'Quigley; Xavier Paoletti; Jean Maccario
Journal:  Biostatistics       Date:  2002-03       Impact factor: 5.899

2.  Performance of toxicity probability interval based designs in contrast to the continual reassessment method.

Authors:  Bethany Jablonski Horton; Nolan A Wages; Mark R Conaway
Journal:  Stat Med       Date:  2016-07-19       Impact factor: 2.373

3.  Critical aspects of the Bayesian approach to phase I cancer trials.

Authors:  Beat Neuenschwander; Michael Branson; Thomas Gsponer
Journal:  Stat Med       Date:  2008-06-15       Impact factor: 2.373

4.  Continual reassessment method: a likelihood approach.

Authors:  J O'Quigley; L Z Shen
Journal:  Biometrics       Date:  1996-06       Impact factor: 2.571

5.  Modified toxicity probability interval design: a safer and more reliable method than the 3 + 3 design for practical phase I trials.

Authors:  Yuan Ji; Sue-Jane Wang
Journal:  J Clin Oncol       Date:  2013-04-08       Impact factor: 44.544

6.  Rendering the 3 + 3 Design to Rest: More Efficient Approaches to Oncology Dose-Finding Trials in the Era of Targeted Therapy.

Authors:  Lei Nie; Eric H Rubin; Nitin Mehrotra; José Pinheiro; Laura L Fernandes; Amit Roy; Stuart Bailey; Dinesh P de Alwis
Journal:  Clin Cancer Res       Date:  2016-06-01       Impact factor: 12.531

7.  Regularization Paths for Generalized Linear Models via Coordinate Descent.

Authors:  Jerome Friedman; Trevor Hastie; Rob Tibshirani
Journal:  J Stat Softw       Date:  2010       Impact factor: 6.440

8.  Performance of two-stage continual reassessment method relative to an optimal benchmark.

Authors:  Nolan A Wages; Mark R Conaway; John O'Quigley
Journal:  Clin Trials       Date:  2013-10-01       Impact factor: 2.486

9.  Dose expansion cohorts in Phase I trials.

Authors:  Alexia Iasonos; John O'Quigley
Journal:  Stat Biopharm Res       Date:  2016-06-02       Impact factor: 1.452

10.  A comprehensive comparison of the continual reassessment method to the standard 3 + 3 dose escalation scheme in Phase I dose-finding studies.

Authors:  Alexia Iasonos; Andrew S Wilton; Elyn R Riedel; Venkatraman E Seshan; David R Spriggs
Journal:  Clin Trials       Date:  2008       Impact factor: 2.486

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