Literature DB >> 30165594

A benchmark for dose finding studies with continuous outcomes.

Pavel Mozgunov1, Thomas Jaki1, Xavier Paoletti2.   

Abstract

An important tool to evaluate the performance of any design is an optimal benchmark proposed by O'Quigley and others (2002. Non-parametric optimal design in dose finding studies. Biostatistics3, 51-56) that provides an upper bound on the performance of a design under a given scenario. The original benchmark can only be applied to dose finding studies with a binary endpoint. However, there is a growing interest in dose finding studies involving continuous outcomes, but no benchmark for such studies has been developed. We show that the original benchmark and its extension by Cheung (2014. Simple benchmark for complex dose finding studies. Biometrics70, 389-397), when looked at from a different perspective, can be generalized to various settings with several discrete and continuous outcomes. We illustrate and compare the benchmark's performance in the setting of a dose finding Phase I clinical trial with a continuous toxicity endpoint and a Phase I/II trial with binary toxicity and continuous efficacy endpoints. We show that the proposed benchmark provides an accurate upper bound in these contexts and serves as a powerful tool for evaluating designs.
© The Author(s) 2018. Published by Oxford University Press.

Entities:  

Keywords:  Continuous endpoint; Dose finding; Non-parametric optimal design; Phase I; Phase I/II

Mesh:

Year:  2020        PMID: 30165594     DOI: 10.1093/biostatistics/kxy045

Source DB:  PubMed          Journal:  Biostatistics        ISSN: 1465-4644            Impact factor:   5.899


  9 in total

1.  A surface-free design for phase I dual-agent combination trials.

Authors:  Pavel Mozgunov; Mauro Gasparini; Thomas Jaki
Journal:  Stat Methods Med Res       Date:  2020-04-27       Impact factor: 3.021

2.  Seamless phase I/II design for novel anticancer agents with competing disease progression.

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

3.  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

4.  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

5.  A flexible design for advanced Phase I/II clinical trials with continuous efficacy endpoints.

Authors:  Pavel Mozgunov; Thomas Jaki
Journal:  Biom J       Date:  2019-07-12       Impact factor: 2.207

6.  Improving safety of the continual reassessment method via a modified allocation rule.

Authors:  Pavel Mozgunov; Thomas Jaki
Journal:  Stat Med       Date:  2019-12-20       Impact factor: 2.373

7.  A comparison of various aggregation functions in multi-criteria decision analysis for drug benefit-risk assessment.

Authors:  Tom Menzies; Gaelle Saint-Hilary; Pavel Mozgunov
Journal:  Stat Methods Med Res       Date:  2022-01-19       Impact factor: 3.021

8.  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

9.  Practical recommendations for implementing a Bayesian adaptive phase I design during a pandemic.

Authors:  Sean Ewings; Geoff Saunders; Thomas Jaki; Pavel Mozgunov
Journal:  BMC Med Res Methodol       Date:  2022-01-20       Impact factor: 4.615

  9 in total

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