Statistical Methods for Survival Trial Design

Jianrong (John) Wu is a professor in the Division of Cancer Biostatistics, Department of Biostatistics, Markey Cancer Center, University of Kentucky. He has more than 15 years’ experience of designing and conducting cancer clinical trials at St. Jude Children’s Research Hospital and has developed several novel statistical methods for designing phase II and phase III survival trials.

Preface List of Figures List of Tables 1. Introduction to Cancer Clinical Trials General Aspects of Cancer Clinical Trial Design Study Objectives Treatment Plan Eligibility Criteria Statistical Considerations Statistical Aspects of Cancer Survival Trial Design Randomization Stratification Blinding Sample Size Calculation 2. Survival Analysis Survival Distribution Exponential Distribution Weibull Distribution Gamma Distribution Gompertz Distribution Log-Normal Distribution Log-Logistic Distribution Survival Data Fitting the Parametric Survival Distribution Kaplan-Meier Estimates Median Survival Time Log-Rank Test Cox Regression Model 3. Counting Process and Martingale_ Basic Convergence Concepts Counting Process Definition Martingale Central Limit Theorem Counting Process Formulation of Censored Survival Data 4. Survival Trial Design Under the Parametric Model Introduction Weibull Model Test Statistic Distribution of the MLE test Sample Size Formula Sample Size Calculation Accrual Duration Calculation Example and R code 5. Survival Trial Design Under the Proportional Hazards Model Introduction Proportional Hazards Model Asymptotic Distribution of the Log-rank Test Schoenfeld Formula Rubinstein Formula Freedman Formula Comparison Sample Size Calculation Under Various Models Example Optimal Properties of the Log-Rank Test_ Optimal Sample Size Allocation Optimal Power Precise Formula