Dose-Finding Based on Bivariate Efficacy-Toxicity Outcome Using Archimedean Copula

Tao, Yuxi; Liu, Junlin; Li, Zhihui; Lin, Jin-Guan; Lü, Tao; Yan, Fangrong

doi:10.1371/journal.pone.0078805

Cited by 8 publications

(6 citation statements)

References 19 publications

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“…Modelling these types of responses is a challenging problem and not much work has been done on this topic in the literature. Tao et al (2013) investigated this situation by modelling these multiple endpoints by a joint model constructed with archimedean copula. An equivalence test for these types of outcomes is an interesting topic which we leave for future research.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Testing for similarity of binary efficacy–toxicity responses

2021

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Summary Clinical trials often aim to compare two groups of patients for efficacy and/or toxicity depending on covariates such as dose. Examples include the comparison of populations from different geographic regions or age classes or, alternatively, of different treatment groups. Similarity of these groups can be claimed if the difference in average outcome is below a certain margin over the entire covariate range. In this article, we consider the problem of testing for similarity in the case that efficacy and toxicity are measured as binary outcome variables. We develop a new test for the assessment of similarity of two groups for a single binary endpoint. Our approach is based on estimating the maximal deviation between the curves describing the responses of the two groups, followed by a parametric bootstrap test. Further, using a two-dimensional Gumbel-type model we develop methodology to establish similarity for (correlated) binary efficacy–toxicity outcomes. We investigate the operating characteristics of the proposed methodology by means of a simulation study and present a case study as an illustration.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Testing for similarity of binary efficacy–toxicity responses

2021

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“…, and 𝑢 − 2𝑖𝑗 is the left-hand limit of 𝑢 2𝑖𝑗 ; see Joe (2014) and Tao et al (2013) for further details.…”

Section: Spatial Model For Bivariate Responsesmentioning

confidence: 99%

Bayesian design for minimizing prediction uncertainty in bivariate spatial responses with applications to air quality monitoring

2023

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Model‐based geostatistical design involves the selection of locations to collect data to minimize an expected loss function over a set of all possible locations. The loss function is specified to reflect the aim of data collection, which, for geostatistical studies, could be to minimize the prediction uncertainty at unobserved locations. In this paper, we propose a new approach to design such studies via a loss function derived through considering the entropy about the model predictions and the parameters of the model. The approach includes a multivariate extension to generalized linear spatial models, and thus can be used to design experiments with more than one response. Unfortunately, evaluating our proposed loss function is computationally expensive so we provide an approximation such that our approach can be adopted to design realistically sized geostatistical studies. This is demonstrated through a simulated study and through designing an air quality monitoring program in Queensland, Australia. The results show that our designs remain highly efficient in achieving each experimental objective individually, providing an ideal compromise between the two objectives. Accordingly, we advocate that our approach could be adopted more generally in model‐based geostatistical design.

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“…Many model-based designs for recommending dose levels based upon safety and efficacy have been proposed, including the bivariate CRM bCRM, 12 ‘EffTox,’ 13 the trivariate CRM TriCRM, 14 and others. 15–26 Some of these designs have even been implemented in trial practice, like the use of an EffTox design to assess the performance of sitravatinib and nivolumab in renal cell cancer 27 or a trial of FLAG-IDA and ponatinib in myeloid leukemia, 28 or the use of the bivariate CRM in a trial on T cell co-stimulators for solid tumors. 29 However, these examples aside, the uptake of these advanced designs has been slow, and protocols often try to satisfy these dual objectives under the 3 + 3 framework.…”

Section: Introductionmentioning

confidence: 99%

A modular framework for early-phase seamless oncology trials

2021

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Background: As our understanding of the etiology and mechanisms of cancer becomes more sophisticated and the number of therapeutic options increases, phase I oncology trials today have multiple primary objectives. Many such designs are now “seamless,” meaning that the trial estimates both the maximum tolerated dose and the efficacy at this dose level. Sponsors often proceed with further study only with this additional efficacy evidence. However, with this increasing complexity in trial design, it becomes challenging to articulate fundamental operating characteristics of these trials, such as (1) what is the probability that the design will identify an acceptable, that is., safe and efficacious, dose level? or (2) how many patients will be assigned to an acceptable dose level on average? Methods: In this manuscript, we propose a new modular framework for designing and evaluating seamless oncology trials. Each module is comprised of either a dose assignment step or a dose-response evaluation, and multiple such modules can be implemented sequentially. We develop modules from existing phase I/II designs as well as a novel module for evaluating dose-response using a Bayesian isotonic regression scheme. Results: We also demonstrate a freely available R package called seamlesssim to numerically estimate, by means of simulation, the operating characteristics of these modular trials. Conclusions: Together, this design framework and its accompanying simulator allow the clinical trialist to compare multiple different candidate designs, more rigorously assess performance, better justify sample sizes, and ultimately select a higher quality design.

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Dose-Finding Based on Bivariate Efficacy-Toxicity Outcome Using Archimedean Copula

Cited by 8 publications

References 19 publications

Testing for similarity of binary efficacy–toxicity responses

Testing for similarity of binary efficacy–toxicity responses

Bayesian design for minimizing prediction uncertainty in bivariate spatial responses with applications to air quality monitoring

A modular framework for early-phase seamless oncology trials

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