Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials

Overview of Drug Development 
Introduction 
Drug Discovery 
Target Identi_cation and Validation 
Irrational Approach 
Rational Approach 
Biologics 
NanoMedicine 
Preclinical Development 
Objectives of Preclinical Development 
Pharmacokinetics 
Pharmacodynamics 
Toxicology 
Intraspecies and Interspecies Scaling 
Clinical Development 
Overview of Clinical Development 
Classical Clinical Trial Paradigm 
Adaptive Trial Design Paradigm 
New Drug Application 
Summary 
Clinical Development Plan and Clinical Trial Design 
Clinical Development Program 
Unmet Medical Needs & Competitive Landscape 
Therapeutic Areas 
Value proposition 
Prescription Drug Global Pricing 
Clinical Development Plan 
Clinical Trials 
Placebo, Blinding and Randomization 
Trial Design Type 
Confounding Factors 
Variability and Bias 
Randomization Procedure 
Clinical Trial Protocol 
Target Population 
Endpoint Selection 
Proof of Concept Trial 
Sample Size and Power 
Bayesian Power for Classical Design 
Summary 
Clinical Development Optimization 
Benchmarks in Clinical Development 
Net Present Value and Risk-Adjusted NPV Method 
Clinical Program Success Rates 
Failure Rates by Reason 
Costs of Clinical Trials 
Time-to-Next Phase, Clinical Trial Length and
Regulatory Review Time 
Rates of Competitor Emerging 
Optimization of Clinical Development Program 
Local Versus Global Optimizations 
Stochastic Decision Process for Drug Development 
Time Dependent Gain g, 
Determination of Transition Probabilities 
Example of CDP Optimization 
Updating Model Parameters 
Clinical Development Program with Adaptive Design 
Summary 
Globally Optimal Adaptive Trial Designs 
Common Adaptive Designs 
Group Sequential Design 
Test Statistics 
Commonly Used Stopping Boundaries 
Sample Size Reestimation Design 
Test Statistic 
Rules of Stopping and Sample-Size Adjustment 
Simulation Examples 
Pick-Winner-Design 
Shun-Lan-Soo Method for Three-Arm Design 
K-Arm Pick-Winner Design 
Global Optimization of Adaptive Design - Case Study 
Medical Needs for COPD 
COPD Market 
Indacaterol Trials 
US COPD Phase II Trial Results 
Optimal Design 
Summary & Discussions 
Trial Design for Precision Medicine 
Introduction 
Overview of Classical Designs with Biomarkers 
Biomarker-enrichment Design 
Biomarker-Stratified Design 
Sequential Testing Strategy Design 
Marker-based Strategy Design 
Hybrid Design 
Overview of Biomarker-Adaptive Designs 
Adaptive Accrual Design 
Biomarker-Informed Group Sequential Design 
Biomarker-Adaptive Threshold Design 
Adaptive Signature Design 
Cross-Validated Adaptive Signature Design 
Trial Design Method with Biomarkers 
Impact of Assay Sensitivity and Specificity 
Biomarker-Stratified Design 
Biomarker-Adaptive Winner Design 
Biomarker-Informed Group Sequential Design 
Basket and Population-Adaptive Designs 
Basket Design Method with Familywise Error Control
Basket Design for Cancer Trial with Imatinib 
Methods based on Similarity Principle 
Summary 
Clinical Trial with Survival Endpoint 
Overview of Survival Analysis 
Basic Taxonomy 
Nonparametric Approach 
Proportional Hazard Model 
Accelerated Failure Time Model 
Frailty Model 
Maximum Likelihood Method 
Landmark Approach and Time-Dependent Covariate
Multistage Models for Progressive Disease 
Introduction 
Progressive Disease Model 
Piecewise Model for Delayed Drug Effect 
Introduction 
Piecewise Exponential Distribution 
Mean and Median Survival Times 
Weighted LogRank Test for Delayed Treatment Effect
Oncology Trial with Treatment Switching 
Descriptions of the Switching Problem 
Treatment Switching 
Inverse Probability of Censoring Weighted LogRank Test 
Removing Treatment Switch Issue by Design 
Competing Risks 
Competing Risks as Bivariate Random Variable 
Solution to Competing Risks Model 
Competing Progressive Disease Model 
Hypothesis Test Method 
Threshold Regression with First-Hitting-Time Model 
Multivariate Model with Biomarkers 
Summary 
Practical Multiple Testing Methods in Clinical Trials 
Multiple-Testing Problems 
Sources of Multiplicity 
Multiple-Testing Taxonomy 
Union-Intersection Testing 
Single-Step Procedure 
Stepwise Procedures 
Single-Step Progressive Parametric Procedure 
Power Comparison of Multiple Testing Methods 
Application to Armodafinil Trial 
Intersection-Union Testing 
The Need for Coprimary Endpoints 
Conventional Approach 
Average Error Method 
Li-Huque`s Method 
Application to a Glaucoma Trial 
Priority Winner Test for Multiple Endpoints 
Finkelstein-Schoenfeld`s Method 
Win-Ratio Test 
Application to Charm Trial 
Summary 
Missing Data Handling in Clinical Trials 
Missing Data Problems 
Missing Data Issue and Its Impact 
Missing Mechanism 
Implementation of Analysis Methods 
Trial Data Simulation 
Single Imputation Methods 
Methods without Specified Mechanics of Missing 
Inverse-Probability Weighting Method 
Multiple Imputation Method 
Tipping Point Analysis for MNAR 
Mixture of Paired and Unpaired Data 
Comparisons of Different Methods 
Regulatory and Operational Perspective 
Special Issues and Resolutions 
Overview 
Drop-Loser Design Based on Efficacy and Safety 
Multi-stage Design with Treatment Selection 
Dunnett Test with Drop-losers 
Drop-Loser Design with Gatekeeping Procedure 
Drop-loser Design with Adjustable Sample Size 
Drop-Loser Rules in Term of Efficacy and Safety 
Simulation Study 
Clinical Trial Interim Analysis with Survival Endpoint 
Hazard Ratio versus Number of Deaths 
Conditional Power 
Prediction of Timing for Target Number of Events 
Power and Sample Size for One-Arm Survival Trial Design 
Estimation of Treatment Effect with Interim Blinded Data 
Likelihood 
MLE Method 
Bayesian Posterior 
Analysis of Toxicology Study with Unexpected Deaths 
Fisher versus Barnard`s Exact Test Methods 
Wald statistic 
Fisher`s Conditional Exact Test p-value 
Barnard`s Unconditional Exact Test p-value 
Power Comparisons of Fisher`s versus Barnard`s Tests
Adaptive Design with Mixed Endpoints 
Summary 
Issues and Concepts of Data Monitoring Committees 
Overview of the DMC 
Operation of the DMC 
Role of the DMC Biostatistician 
Requirement for a DMC 
Use of a DMC in Rare Disease Studies 
Statistical methods for Safety Monitoring 
Statistical methods for interim efficacy analysis 
Summary and Discussion 
Controversies in Statistical Science 
What is a Science? 
Similarity Principle 
Simpson`s Paradox 
Causality 
Type-I Error Rate and False Discovery Rate 
Multiplicity Challenges 
Regression with Time-Dependent Variables 
Hidden Confounders 
Controversies in Dynamic Treatment Regime 
Paradox of Understanding 
Summary and Recommendations