Literature DB >> 24571185

Simple benchmark for complex dose finding studies.

Ying Kuen Cheung1.   

Abstract

While a general goal of early phase clinical studies is to identify an acceptable dose for further investigation, modern dose finding studies and designs are highly specific to individual clinical settings. In addition, as outcome-adaptive dose finding methods often involve complex algorithms, it is crucial to have diagnostic tools to evaluate the plausibility of a method's simulated performance and the adequacy of the algorithm. In this article, we propose a simple technique that provides an upper limit, or a benchmark, of accuracy for dose finding methods for a given design objective. The proposed benchmark is nonparametric optimal in the sense of O'Quigley et al. (2002, Biostatistics 3, 51-56), and is demonstrated by examples to be a practical accuracy upper bound for model-based dose finding methods. We illustrate the implementation of the technique in the context of phase I trials that consider multiple toxicities and phase I/II trials where dosing decisions are based on both toxicity and efficacy, and apply the benchmark to several clinical examples considered in the literature. By comparing the operating characteristics of a dose finding method to that of the benchmark, we can form quick initial assessments of whether the method is adequately calibrated and evaluate its sensitivity to the dose-outcome relationships.
© 2014, The International Biometric Society.

Entities:  

Keywords:  Combination therapy; Efficacy-toxicity tradeoff; Multiple toxicities; Phase I trials; Phase I/II trials; Utility scores

Mesh:

Substances:

Year:  2014        PMID: 24571185      PMCID: PMC4061271          DOI: 10.1111/biom.12158

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  17 in total

1.  Dose-finding designs for HIV studies.

Authors:  J O'Quigley; M D Hughes; T Fenton
Journal:  Biometrics       Date:  2001-12       Impact factor: 2.571

2.  Statistical properties of the traditional algorithm-based designs for phase I cancer clinical trials.

Authors:  Y Lin; W J Shih
Journal:  Biostatistics       Date:  2001-06       Impact factor: 5.899

3.  Sequential designs for logistic phase I clinical trials.

Authors:  Guohui Liu; William F Rosenberger; Linda M Haines
Journal:  J Biopharm Stat       Date:  2006       Impact factor: 1.051

4.  Continual reassessment method with multiple toxicity constraints.

Authors:  Shing M Lee; Bin Cheng; Ying Kuen Cheung
Journal:  Biostatistics       Date:  2010-09-28       Impact factor: 5.899

5.  A random walk rule for phase I clinical trials.

Authors:  S D Durham; N Flournoy; W F Rosenberger
Journal:  Biometrics       Date:  1997-06       Impact factor: 2.571

6.  Stochastic Approximation and Modern Model-based Designs for Dose-Finding Clinical Trials.

Authors:  Ying Kuen Cheung
Journal:  Stat Sci       Date:  2010-05       Impact factor: 2.901

7.  A strategy for dose-finding and safety monitoring based on efficacy and adverse outcomes in phase I/II clinical trials.

Authors:  P F Thall; K E Russell
Journal:  Biometrics       Date:  1998-03       Impact factor: 2.571

8.  Quantile estimation following non-parametric phase I clinical trials with ordinal response.

Authors:  Ranjan K Paul; William F Rosenberger; Nancy Flournoy
Journal:  Stat Med       Date:  2004-08-30       Impact factor: 2.373

9.  The bivariate continual reassessment method. extending the CRM to phase I trials of two competing outcomes.

Authors:  Thomas M Braun
Journal:  Control Clin Trials       Date:  2002-06

10.  The continual reassessment method for multiple toxicity grades: a Bayesian quasi-likelihood approach.

Authors:  Z Yuan; R Chappell; H Bailey
Journal:  Biometrics       Date:  2007-03       Impact factor: 2.571

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  6 in total

1.  Scientific Review of Phase I Protocols With Novel Dose-Escalation Designs: How Much Information Is Needed?

Authors:  Alexia Iasonos; Mithat Gönen; George J Bosl
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2.  Dose-finding designs for cumulative toxicities using multiple constraints.

Authors:  Shing M Lee; Moreno Ursino; Ying Kuen Cheung; Sarah Zohar
Journal:  Biostatistics       Date:  2019-01-01       Impact factor: 5.899

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Authors:  Lucie Biard; Shing M Lee; Bin Cheng
Journal:  Stat Med       Date:  2021-07-02       Impact factor: 2.497

4.  Operating characteristics are needed to properly evaluate the scientific validity of phase I protocols.

Authors:  Nolan A Wages; Bethany Jablonski Horton; Mark R Conaway; Gina R Petroni
Journal:  Contemp Clin Trials       Date:  2021-07-25       Impact factor: 2.261

5.  A benchmark for dose-finding studies with unknown ordering.

Authors:  Pavel Mozgunov; Xavier Paoletti; Thomas Jaki
Journal:  Biostatistics       Date:  2022-07-18       Impact factor: 5.279

6.  CFO: Calibration-free odds design for phase I/II clinical trials.

Authors:  Huaqing Jin; Guosheng Yin
Journal:  Stat Methods Med Res       Date:  2022-03-03       Impact factor: 2.494

  6 in total

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