Theory of drug development
- Eric B. Holmgren.
- Boca Raton : Taylor & Francis, 
- Copyright notice
- Physical description
- 1 online resource : text file, PDF.
- Chapman & Hall/CRC biostatistics series.
- Holmgren, Eric B., 1959- author.
- Includes bibliographical references and index.
- A Theory of Evaluating Drugs Clinical Drug Development Phases 1 through 3 Stages of Clinical Development Bevacizumab Choosing Drugs to Develop Probability of Technical Success Uncertainty Surrounding Expected Future Cash Flows Maximize the Value of the Company Today or Tomorrow? Decision Rules for Phase 2 Phase 2/3 Strategy Model When Is a Phase 2/3 Strategy Better Than a Phase 3 Trial Alone? How Much Can Efficiency Be Improved? Admissible Phase 2 Trial Designs Projects That Are Not Least Attractive Example: Bevacizumab Example: Rituximab Example: TNK Maximize the Minimum Efficiency Single-Arm Phase 2 Trial Phase 2 Trials Based on Surrogate Endpoints Impact of a Surrogate on the Efficiency of Drug Development Estimation of the Potential Impact of a Specific Surrogate on Efficiency Dose Selection and Subgroups: Phase 2 as a Pilot Trial Relative Efficiency for Selecting a Dose Properties of Relative Efficiency for Selecting a Dose Relative Efficiency for Selecting a Subgroup Evaluating the Marker Hypothesis Multistage Screening Efficiency Order of Tests in Drug Development Adverse Events A Theory of Evidence in Drug Development Preference for Simple Tests of Hypotheses over Model-Based Tests Control Maximum Risk Variance of a Model-Based Estimate of Treatment Effect Comparison of a Simple Difference in Means with a Model-Based Estimate of Treatment Effect A Study Design That Permits Data-Driven Model Adjustment of the Treatment Effect Estimate Quantifying the Strength of Evidence from a Study Ratio of True Positives to False Positives Studies with Interim Analyses A Boundary with a Constant Ratio of Power to Type 1 Error O'Brien-Fleming Boundary Bayesian or Frequentist? Quantifying the Strength of Evidence: A Few Additional Comments on Interim Analyses Wald's Likelihood Ratio Test Pocock Boundary Confirmatory Trials Can Evidence from Phase 2 Trials Be Combined with Evidence from Phase 3? Example: Phase 2 in Rheumatoid Arthritis Design a Phase 3 Trial to Account for Evidence against the Global Null Hypothesis Evidence from Phase 3 Trials Example Additional Topics Maximize Efficiency Subject to a Constraint on True+/False+ Power of the Log Rank Test to Detect Improvement in Mean Survival Time and the Impact of Censoring Setup Minimizing the Log Rank Test Examples Censoring Survival Benefit in the Bevacizumab Phase 3 Colorectal Cancer Trial Adaptive Phase 2/3 Designs Impact of Adaptive Designs on Drug Company Behavior Net Effect of Adaptive Phase 2/3 Designs on the Ratio of True to False Positives Size of the Phase 3 Trial Sizing a Phase 3 Trial Based on the Minimum Clinically Meaningful Difference Using Phase 2 Results to Size the Phase 3 Trial Extending the Model of Clinical Drug Development Maximizing Net Present Value (NPV) Picking the Best Dose in Phase 2 Targeted Therapies Appendices References appear at the end of each chapter.
- (source: Nielsen Book Data)9781466507470 20160612
- Publisher's Summary
- Theory of Drug Development presents a formal quantitative framework for understanding drug development that goes beyond simply describing the properties of the statistics in individual studies. It examines the drug development process from the perspectives of drug companies and regulatory agencies. By quantifying various ideas underlying drug development, the book shows how to systematically address problems, such as: * Sizing a phase 2 trial and choosing the range of p-values that will trigger a follow-up phase 3 trial * Deciding whether a drug should receive marketing approval based on its phase 2/3 development program and recent experience with other drugs in the same clinical area * Determining the impact of adaptive designs on the quality of drugs that receive marketing approval * Designing a phase 3 pivotal study that permits the data-driven adjustment of the treatment effect estimate * Knowing when enough information has been gathered to show that a drug improves the survival time for the whole patient population Drawing on his extensive work as a statistician in the pharmaceutical industry, the author focuses on the efficient development of drugs and the quantification of evidence in drug development. He provides a rationale for underpowered phase 2 trials based on the notion of efficiency, which leads to the identification of an admissible family of phase 2 designs. He also develops a framework for evaluating the strength of evidence generated by clinical trials. This approach is based on the ratio of power to type 1 error and transcends typical Bayesian and frequentist statistical analyses.
(source: Nielsen Book Data)9781466507470 20160612
- Publication date
- Copyright date
- Chapman & Hall/CRC biostatistics series
- "A CRC title."
- Also available in print format.
- Available in another form
- Print version: ( 9781466507463 )
- 9781466507470 (e-book : PDF)