Approximate dynamic programming and its applications to the design of Phase I cancer trials

DOI10.1214/10-STS317zbMath1328.62581arXiv1011.6509MaRDI QIDQ903295

Jay Bartroff, Tze Leung Lai

Publication date: 5 January 2016

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1011.6509

zbMATH Keywords

stochastic optimizationdynamic programmingMonte Carlomaximum tolerated doserollout

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Stochastic programming (90C15) Dynamic programming (90C39) Medical applications (general) (92C50)

Related Items (14)

Optimal sequential designs in phase I studies ⋮ Approximating the operating characteristics of Bayesian uncertainty directed trial designs ⋮ Control Theory and Experimental Design in Diffusion Processes ⋮ Optimal designs for contingent response models with application to toxicity-efficacy studies ⋮ Information in a two-stage adaptive optimal design ⋮ Nonmyopic and pseudo-nonmyopic approaches to optimal sequential design in the presence of covariates ⋮ A Bayesian adaptive dose-finding algorithm for balancing individual- and population-level ethics in Phase I clinical trials ⋮ A continual reassessment method without undue risk of toxicity ⋮ Stochastic approximation and modern model-based designs for dose-finding clinical trials ⋮ Adaptive clinical trial designs for phase I cancer studies ⋮ A new characterization of Elfving's method for high dimensional computation ⋮ Incorporating Individual and Collective Ethics into Phase I Cancer Trial Designs ⋮ The treatment versus experimentation dilemma in dose finding studies ⋮ Unnamed Item

Uses Software

• ElemStatLearn

Cites Work

• Unnamed Item
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