Bayesian approaches in oncology using R and OpenBUGS Cover Half Title Title Page Copyright Page Dedication Table of Contents Preface Author I: Bayesian in Clinical Research 1: Introduction to R and OpenBUGS 1.1 Introduction to R 1.2 How to Install R 1.3 Packages 1.4 How to Install RStudio 1.5 OpenBUGS 2: Sample Size Determination 2.1 Introduction 2.2 The Sample Size Formula 2.3 Measured Outcomes 2.3.1 Time to Event Endpoints 2.3.2 Sample Size with Event Driven Trials 2.4 Bayesian Sample Size Determination 2.5 Study Objective and Sample Size Determination 2.6 Bayesian Survival Analysis Sample Size Calculation with R 2.7 Illustration 2.8 Posterior Error Approach 2.9 Different Sample Size Determination Packages in R 2.9.1 BAEssd 2.9.2 BayesianPower 2.9.3 NPHMC 2.9.4 PowerTOST 2.9.5 SampleSize4ClinicalTrials 2.9.6 SSRMST 2.9.7 powerSurvEpi 2.9.8 SampleSizeMeans 3: Study Design I 3.1 Introduction 3.2 Bayesian in Early Phase Oncology Trial 3.2.1 Rule Based Designs 3.2.1.1 3+3 Design 3.2.1.2 Pharmacologically Guided Dose Escalation 3.2.1.3 Accelerated Titration Designs 3.2.2 Model Based Designs for Determining the MTD 3.2.2.1 Continual Reassessment Method (CRM) 3.3 Study Design Package Using R 4: Study Design II 4.1 Introduction 4.2 Methods 4.2.1 Estimating Treatment Success 4.2.2 Treatment Difference Testing 4.3 Illustration with Bayesian Using R 4.4 Discussion 5: Optimum Biological Dose Selection 5.1 Introduction 5.2 Illustration with Head and Neck Cancer Data 5.3 Toxicity Profile Testing 5.4 Logistic Response Model 5.5 Dose Selection Algorithm 5.6 Quadratic Logistic Design 5.7 Illustration 5.8 Bayesian Dose Selection Using OpenBUGS 5.9 Discussion 5.10 Dose Finding Package Using R 5.10.1 DoseFinding.