Theory of Drug Development: 1st Edition (Paperback) book cover

Theory of Drug Development

1st Edition

By Eric B. Holmgren

Chapman and Hall/CRC

261 pages | 50 B/W Illus.

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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.


"’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

Table of Contents

A Theory of Evaluating Drugs

Clinical Drug Development Phases 1 through 3

Stages of Clinical Development


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


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


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


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


Minimizing the Log Rank Test



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


References appear at the end of each chapter.

About the Author

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.

About the Series

Chapman & Hall/CRC Biostatistics Series

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General
MEDICAL / Pharmacology