Development of Interactive Software for Bayesian Optimal Phase 1 Clinical Trial Design

Rosenberger, William F.; Canfield, Gerald C.; Perevozskaya, Inna; Haines, Linda M.; Hausner, P

doi:10.1177/009286150503900112

Cited by 7 publications

(7 citation statements)

References 9 publications

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“…For example, the definition of a toxicity of 21 response may be based on the approach of eliciting a range for the probability of toxicity at the lowest dose level, and the value of the maximum tolerated dose. The prior for both parameters distribution may be considered as a uniform distribution over these ranges [57]. A non-parametric shape function, for a maximum, tolerated dose may also be reported.…”

Section: Categorical Outcomesmentioning

confidence: 99%

Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review

Azzolina

Berchialla

Gregori

et al. 2021

IJERPH

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Bayesian inference is increasingly popular in clinical trial design and analysis. The subjective knowledge derived from an expert elicitation procedure may be useful to define a prior probability distribution when no or limited data is available. This work aims to investigate the state-of-the-art Bayesian prior elicitation methods with a focus on clinical trial research. A literature search on the Current Index to Statistics (CIS), PubMed, and Web of Science (WOS) databases, considering “prior elicitation” as a search string, was run on 1 November 2020. Summary statistics and trend of publications over time were reported. Finally, a Latent Dirichlet Allocation (LDA) model was developed to recognise latent topics in the pertinent papers retrieved. A total of 460 documents pertinent to the Bayesian prior elicitation were identified. Of these, 213 (45.4%) were published in the “Probability and Statistics” area. A total of 42 articles pertain to clinical trial and the majority of them (81%) reports parametric techniques as elicitation method. The last decade has seen an increased interest in prior elicitation and the gap between theory and application getting narrower and narrower. Given the promising flexibility of non-parametric approaches to the experts’ elicitation, more efforts are needed to ensure their diffusion also in applied settings.

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Section: Categorical Outcomesmentioning

confidence: 99%

Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review

Azzolina

Berchialla

Gregori

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…To overcome this limitation, restricted optimal designs, where a restriction on the dose range reflects ethical constraints, have been proposed [45,66,85]. Ethically constrained Bayesian D-and c-optimal designs [45,85] can be viable in practice due to their established theoretical properties [92] and availability of the corresponding statistical software [84].…”

Section: Phase I Dose-escalation Studiesmentioning

confidence: 99%

On Optimal Designs for Clinical Trials: An Updated Review

Sverdlov¹,

Ryeznik

Wong

2019

J Stat Theory Pract

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Optimization of clinical trial designs can help investigators achieve higher quality results for the given resource constraints. The present paper gives an overview of optimal designs for various important problems that arise in different stages of clinical drug development, including phase I dose-toxicity studies; phase I/II studies that consider early efficacy and toxicity outcomes simultaneously; phase II dose-response studies driven by multiple comparisons (MCP), modeling techniques (Mod), or their combination (MCP-Mod); phase III randomized controlled multiarm multi-objective clinical trials to test difference among several treatment groups; and population pharmacokinetics-pharmacodynamics experiments. We find that modern literature is very rich with optimal design methodologies that can be utilized by clinical researchers to improve efficiency of drug development.

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“…Practical problems that need to be addressed in a sequential design procedure include initial patient assignments based solely on the prior distribution and appropriate prior elicitation. These and computational issues are addressed in Rosenberger et al (2005).…”

Section: Application To Phase I Clinical Trialsmentioning

confidence: 99%

Convergence properties of sequential Bayesian D-optimal designs

Ghosal

Rosenberger

2009

Journal of Statistical Planning and Inference

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We establish convergence properties of sequential Bayesian optimal designs. In particular, for sequential D-optimality under a general nonlinear location-scale model for binary experiments, we establish posterior consistency, consistency of the design measure, and the asymptotic normality of posterior following the design. We illustrate our results in the context of a particular application in the design of phase I clinical trials, namely a sequential design of Haines et al. [2003. Bayesian optimal designs for phase I clinical trials. Biometrics 59, 591-600] that incorporates an ethical constraint on overdosing.

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Development of Interactive Software for Bayesian Optimal Phase 1 Clinical Trial Design

Cited by 7 publications

References 9 publications

Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review

Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review

On Optimal Designs for Clinical Trials: An Updated Review

Convergence properties of sequential Bayesian D-optimal designs

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