A benchmark for dose finding studies with continuous outcomes

Biostatistics
Pavel MozgunovXavier Paoletti

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.

References

Nov 17, 1998·Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology·H S FriedmanS C Schold
Aug 23, 2003·Biostatistics·John O'QuigleyJean Maccario
May 4, 2011·Annals of Oncology : Official Journal of the European Society for Medical Oncology·S M LeeY K Cheung
Jul 23, 2013·The Journal of Allergy and Clinical Immunology·Michel LavioletteNestor A Molfino
Feb 28, 2014·Biometrics·Ying Kuen Cheung
Nov 20, 2014·Pharmaceutical Statistics·Yunfei Wang, Anastasia Ivanova
Jun 27, 2017·Clinical Trials : Journal of the Society for Clinical Trials·Nolan A Wages, Nikole Varhegyi

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Citations

Jul 13, 2019·Biometrical Journal. Biometrische Zeitschrift·Pavel Mozgunov, Thomas Jaki
Dec 21, 2019·Statistics in Medicine·Pavel Mozgunov, Thomas Jaki
Nov 24, 2020·Journal of Biopharmaceutical Statistics·Anaïs AndrillonLucie Biard

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