Adaptive Bayesian compound designs for dose finding studies

DOI10.1016/j.jspi.2011.12.029zbMath1242.62120OpenAlexW1980578632WikidataQ56994255 ScholiaQ56994255MaRDI QIDQ434557

T. Goggin, J. A. Eccleston, James M. McGree, Kerrie L. Mengersen, M. Helen Thompson, Christopher C. Drovandi, Stephen B. Duffull, Anthony N. Pettitt

Publication date: 16 July 2012

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2011.12.029

zbMATH Keywords

importance samplingMarkov chain Monte Carlosafetyutility functionsoptimal designadaptive designcompound utility

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Probabilistic models, generic numerical methods in probability and statistics (65C20) Numerical analysis or methods applied to Markov chains (65C40)

Related Items

Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data, Evaluation of the Fisher information matrix in nonlinear mixed effect models using adaptive Gaussian quadrature, A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design, Developments of the total entropy utility function for the dual purpose of model discrimination and parameter estimation in Bayesian design, A Review of Modern Computational Algorithms for Bayesian Optimal Design, Bayesian sequential design for copula models, Response-adaptive randomization in clinical trials: from myths to practical considerations, A Laplace-based algorithm for Bayesian adaptive design, Adaptive clinical trial designs for phase I cancer studies

Cites Work

• Unnamed Item
• Continual Reassessment Method: A Practical Design for Phase 1 Clinical Trials in Cancer
• Simulation-based sequential Bayesian design
• Optimal Bayesian-feasible dose escalation for cancer phase I trials
• Markov chains for exploring posterior distributions. (With discussion)
• Compound optimal designs for percentile estimation in dose-response models with restricted design intervals
• Adaptive designs for selecting drug combinations based on efficacy-toxicity response
• Bayesian Optimal Designs for Phase I Clinical Trials
• Statistical Methods for Dose‐Finding Experiments
• Patient‐Specific Dose Finding Based on Bivariate Outcomes and Covariates
• Sequential Experimental Designs for Generalized Linear Models
• Selection of Dose Levels for Estimating a Percentage Point of a Logistic Quantal Response Curve
• Miscellanea. A stopping rule for the continual reassessment method
• Easy-to-implement Bayesian methods for dose-escalation studies in healthy volunteers
• Bayesian decision procedures based on logistic regression models for dose-finding studies
• Nonlinear Experiments: Optimal Design and Inference Based on Likelihood
• Implementation of a Bayesian Design in a Dose‐Escalation Study of an Experimental Agent in Healthy Volunteers
• A Method for Obtaining and Analyzing Sensitivity Data
• A Stochastic Approximation Method