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Dose-finding with two agents in Phase I oncology trials

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TLDR
An adaptive two‐stage Bayesian design for finding one or more acceptable dose combinations of two cytotoxic agents used together in a Phase I clinical trial is proposed and a simulation study is presented.
Abstract
Nous proposons un dispositif bayesien adaptatif a deux etapes pour la recherche d'une ou plusieurs combinaisons acceptables de doses de deux produits cytotoxiques utilises conjointement dans un essai clinique de phase I. Cette methode necessite que chacun des deux produits ait ete etudie separement au prealable, ce qui est presque toujours le cas en pratique. De surcroit on fait l'hypothese d'un modele parametrique pour l'evaluation de la probabilite de toxicite en fonction des deux doses. Des a priori informatifs pour les parametres caracterisant les courbes de probabilite de la toxicite de chaque produit pris isolement sont soit obtenus du (des) clinicien(s) preparant l'essai, soit issus de donnees historiques, tandis qu'on ne definit que des a priori vagues pour les parametres caracterisant les interactions entre produits. Une methode d'obtention des a priori non informatifs est decrite. Le schema est applique a un essai sur la gemcitabine et le cyclophosphamide, et on presente egalement une etude de simulation.

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Dose Escalation Methods in Phase I Cancer Clinical Trials

TL;DR: Dose escalation methods for phase I trials are reviewed, including the rule-based and model-based dose escalation methods that have been developed to evaluate new anticancer agents and specific methods for drug combinations as well as methods that use a time-to-event endpoint or both toxicity and efficacy as endpoints.
Journal ArticleDOI

Critical aspects of the Bayesian approach to phase I cancer trials.

TL;DR: The Bayesian approach to finding the maximum-tolerated dose in phase I cancer trials is discussed and a comparison with the continual reassessment method (CRM) is performed with data from an actual trial and a simulation study.
Journal ArticleDOI

Determining the effective sample size of a parametric prior

TL;DR: The approach first constructs a prior chosen to be vague in a suitable sense, and updates this prior to obtain a sequence of posteriors corresponding to each of a range of sample sizes, and compute a distance between each posterior and the parametric prior.
Journal ArticleDOI

Adaptive clinical trials in oncology

TL;DR: A wholly new paradigm for drug development exemplifying personalized medicine is evinced by an adaptive trial called I-SPY2, in which drugs from many companies are evaluated in the same trial—a phase II screening process.
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Continual reassessment method for partial ordering.

TL;DR: A new two-dimensional dose-finding method for multiple-agent trials that simplifies to the continual reassessment method (CRM), introduced by O'Quigley, Pepe, and Fisher (1990, Biometrics 46, 33-48), when the ordering is fully known, enables the assumption of a monotonic dose-toxicity curve to be relaxed.
References
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TL;DR: In this paper, three sampling-based approaches, namely stochastic substitution, the Gibbs sampler, and the sampling-importance-resampling algorithm, are compared and contrasted in relation to various joint probability structures frequently encountered in applications.
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Sampling-based approaches to calculating marginal densities

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Computing Bayes Factors by Combining Simulation and Asymptotic Approximations

TL;DR: In this article, the authors compare several methods of estimating Bayes factors when it is possible to simulate observations from the posterior distributions, via Markov chain Monte Carlo or other techniques, provided that each posterior distribution is well behaved in the sense of having a single dominant mode.
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TL;DR: A new approach to the design and analysis of Phase 1 clinical trials in cancer and a particularly simple model is looked at that enables the use of models whose only requirements are that locally they reasonably well approximate the true probability of toxic response.
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Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window

TL;DR: In this article, the authors consider the problem of model selection and accounting for model uncertainty in high-dimensional contingency tables, motivated by expert system applications, and propose a panacea by the standard Bayesian formalism that averages the posterior distributions of the quantity of interest under each of the models, weighted by their posterior model probabilities.
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