Optimization of dose selection using multiple surrogates of toxicity as a continuous variable in phase I cancer trial

Lee, Se Yoon; Munafo, Alain; Girard, Pascal; Goteti, Kosalaram

doi:10.1016/j.cct.2021.106657

Cited by 2 publications

(17 citation statements)

References 33 publications

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“…For example, the number of shale oil wells on Eagle Ford Shale Play can be as large as 6000 [31]. As for the pharmaceutical industry, in phase I cancer clinical trials, the number of cancer patients N may be strictly confined to 25 [141], but for phase III trials for non-oncology drug studies, N can be as large as 2000 [142].…”

Section: Statistical Problemmentioning

confidence: 99%

“…Recent years have seen the great success of Bayesian nonlinear mixed effects models, or more generally, Bayesian hierarchical models (BHM), in a variety of disciplines such as biology, medical research, physics, social, and educational sciences [30,31,141,298,299]. This was partly due to the widespread introduction of non-commercial software packages that enabled applied researchers to answer substantive research questions through applications of BHM [17,34,35,47,50,77].…”

Section: Software Developmentmentioning

confidence: 99%

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Bayesian Nonlinear Models for Repeated Measurement Data: An Overview, Implementation, and Applications

Lee

2022

Mathematics

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Nonlinear mixed effects models have become a standard platform for analysis when data is in the form of continuous and repeated measurements of subjects from a population of interest, while temporal profiles of subjects commonly follow a nonlinear tendency. While frequentist analysis of nonlinear mixed effects models has a long history, Bayesian analysis of the models has received comparatively little attention until the late 1980s, primarily due to the time-consuming nature of Bayesian computation. Since the early 1990s, Bayesian approaches for the models began to emerge to leverage rapid developments in computing power, and have recently received significant attention due to (1) superiority to quantify the uncertainty of parameter estimation; (2) utility to incorporate prior knowledge into the models; and (3) flexibility to match exactly the increasing complexity of scientific research arising from diverse industrial and academic fields. This review article presents an overview of modeling strategies to implement Bayesian approaches for the nonlinear mixed effects models, ranging from designing a scientific question out of real-life problems to practical computations.

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Section: Statistical Problemmentioning

confidence: 99%

Section: Software Developmentmentioning

confidence: 99%

Bayesian Nonlinear Models for Repeated Measurement Data: An Overview, Implementation, and Applications

Lee

2022

Mathematics

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“…The other motivation is that various continuous measures of toxicity arise during phase I studies, which can be beneficial for examining dose-response relationships more precisely. For example, if one aims to consider all grades of toxicities from multiple adverse events when allocating doses, it may be necessary to derive a continuous toxicity score, such as the normalized equivalent toxicity score [14,15], or use a weighted average form as discussed by [16], and utilize such scores as the continuous toxicity response.…”

Section: Introductionmentioning

confidence: 99%

“…One drawback of EWOC-NETS is that the dose-finding does not follow the fully Bayesian paradigm, thus failing to describe the uncertainty of the MTD in a fully Bayesian manner. More recently, Lee et al [16] introduced a fully Bayesian design based on constrained linear regression, called the two-parameter linear dosefinder. In this design, the authors aimed to leverage all grade information of toxicities according to the Common Toxicity Criteria for Adverse Events (CTCAE) [37] within a fully Bayesian framework.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, our objective is to bridge the existing gap by introducing a novel, fully Bayesian dose-finding algorithm that provides flexibility in describing the dose-toxicity curve and wider applicability, based on continuous outcomes. The incorporation of a continuous response within our framework accommodates various scenarios, such as using a continuous toxicity score [16], measuring drug concentration in plasma from patients [18], a biomarker response from a molecularly targeted agent [38], and so on.…”

Section: Introductionmentioning

confidence: 99%

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A flexible dose-response modeling framework based on continuous toxicity outcomes in phase I cancer clinical trials

Lee

2023

Trials

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Background The past few decades have seen remarkable developments in dose-finding designs for phase I cancer clinical trials. While many of these designs rely on a binary toxicity response, there is an increasing focus on leveraging continuous toxicity responses. A continuous toxicity response pertains to a quantitative measure represented by real numbers. A higher value corresponds not only to an elevated likelihood of side effects for patients but also to an increased probability of treatment efficacy. This relationship between toxicity and dose is often nonlinear, necessitating flexibility in the quest to find an optimal dose. Methods A flexible, fully Bayesian dose-finding design is proposed to capitalize on continuous toxicity information, operating under the assumption that the true shape of the dose-toxicity curve is nonlinear. Results We conduct simulations of clinical trials across varying scenarios of non-linearity to evaluate the operational characteristics of the proposed design. Additionally, we apply the proposed design to a real-world problem to determine an optimal dose for a molecularly targeted agent. Conclusions Phase I cancer clinical trials, designed within a fully Bayesian framework with the utilization of continuous toxicity outcomes, offer an alternative approach to finding an optimal dose, providing unique benefits compared to trials designed based on binary toxicity outcomes.

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Optimization of dose selection using multiple surrogates of toxicity as a continuous variable in phase I cancer trial

Cited by 2 publications

References 33 publications

Bayesian Nonlinear Models for Repeated Measurement Data: An Overview, Implementation, and Applications

Bayesian Nonlinear Models for Repeated Measurement Data: An Overview, Implementation, and Applications

A flexible dose-response modeling framework based on continuous toxicity outcomes in phase I cancer clinical trials

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