1st Edition

Theory of Drug Development

By Eric B. Holmgren Copyright 2014
    261 Pages 50 B/W Illustrations
    by Chapman & Hall

    262 Pages 50 B/W Illustrations
    by Chapman & Hall

    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.

    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.

    Biography

    Eric B. Holmgren is a consultant and statistical scientist. He previously worked at Genentech and Hoechst Roussel Pharmaceuticals. He received a Ph.D. in mathematical statistics from Stanford University.

    "’In each chapter, author provides appropriate statistical formulas that readers can actually utilize. Since this book handles many mathematical formulas, and contains many real good examples, this book would be very useful for statisticians who work at pharmaceutical companies and are deeply involved with drug development … Overall, this book covers necessary and important aspects for drug development, and would be quite useful to clinical statisticians."’
    —Byung-Ho Nam, PhD, Department of Cancer Control and Policy, Graduate School of Cancer Science and Policy, National Cancer Center, Korea, in Biometrics

    "The given book presents a theory of drug development that is based on maximizing the efficiency with which drugs that truly provide clinical benefits are identified. The author shows how to optimize the drug development process at its three main stages (Phases 1, 2, 3), and at some transitional sub-stages, so that the number of molecules that result in a positive final Phase 3 clinical trial per investment is maximized."
    —Fatima T. Adylova in Zentralblatt MATH