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Pharmaceutical Statistics Using SAS

Book Description

Pharmaceutical Statistics Using SAS: A Practical Guide offers extensive coverage of cutting-edge biostatistical methodology used in drug development and the practical problems facing today's drug developers. Written by well-known experts in the pharmaceutical industry Alex Dmitrienko, Christy Chuang-Stein, and Ralph D'Agostino, it provides relevant tutorial material and SAS examples to help readers new to a certain area of drug development quickly understand and learn popular data analysis methods and apply them to real-life problems. Step-by-step, the book introduces a wide range of data analysis problems encountered in drug development and illustrates them using a wealth of case studies from actual pre-clinical experiments and clinical studies. The book also provides SAS code for solving the problems. Among the topics addressed are these:

  • drug discovery experiments to identify promising chemical compounds

  • animal studies to assess the toxicological profile of these compounds

  • clinical pharmacology studies to examine the properties of new drugs in healthy human subjects

  • Phase II and Phase III clinical trials to establish therapeutic benefits of experimental drugs.

Additional features include a discussion of methodological issues, practical advice from subject-matter experts, and review of relevant regulatory guidelines. Most chapters are self-contained and include a fair amount of high-level introductory material to make them accessible to a broad audience of pharmaceutical scientists. This book will also serve as a useful reference for regulatory scientists as well as academic researchers and graduate students.

Table of Contents

  1. Praise from the Experts
  2. Copyright
  3. Preface
    1. Introduction
    2. Outline of the Book
    3. List of Contributors
    4. Feedback
    5. Acknowledgments
  4. Statistics in Drug Development
    1. Introduction
    2. Statistical support to non-clinical activities
    3. Statistical support to clinical testing
    4. Battling a high Phase III failure rate
    5. Do statisticians count?
    6. Emerging opportunities
    7. Summary
    8. References
  5. Modern Classification Methods for Drug Discovery
    1. Introduction
    2. Motivating Example
    3. Boosting
    4. Model Building
    5. Partial Least Squares for Discrimination
    6. Summary
    7. References
  6. Model Building Techniques in Drug Discovery
    1. Introduction
    2. Example: Solubility Data
    3. Training and Test Set Selection
    4. Variable Selection
    5. Statistical Procedures for Model Building
    6. Determining When a New Observation Is Not in a Training Set
    7. Using SAS Enterprise Miner
    8. Summary
    9. References
  7. Statistical Considerations in Analytical Method Validation
    1. Introduction
    2. Validation Criteria
    3. Response Function or Calibration Curve
    4. Linearity
    5. Accuracy and Precision
    6. Decision Rule
    7. Limits of Quantification and Range of the Assay
    8. Limit of Detection
    9. Summary
    10. References
  8. Some Statistical Considerations in Nonclinical Safety Assessment
    1. Overview of Nonclinical Safety Assessment
    2. Key Statistical Aspects of Toxicology Studies
    3. Randomization in Toxicology Studies
    4. Power Evaluation in a Two-Factor Model for Qt Interval
    5. Statistical Analysis of a One-Factor Design with Repeated Measures
    6. Summary
    7. Acknowledgements
    8. References
  9. Nonparametric Methods in Pharmaceutical Statistics
    1. Introduction
    2. Two Independent Sample Setting
    3. The One-Way Layout
    4. Power Determination in a Purely Nonparametric Sense
    5. Acknowledgements
    6. References
  10. Optimal Design of Experiments in Pharmaceutical Applications
    1. Optimal Design problem
    2. Quantal Dose-Response Models
    3. Nonlinear Regression Models with a Continuous Response
    4. Regression Models with Unknown Parameters in the Variance Function
    5. Models with a Bounded Response (Beta Models
    6. Models with a Bounded Response (Logit Link)
    7. Bivariate Probit Models for Correlated Binary Responses
    8. Pharmacokinetic Models with Multiple Measurements per Patient
    9. Models with Cost Constraints
    10. Summary
    11. References
  11. Analysis of Human Pharmacokinetic Data
    1. Introduction
    2. Bioequivalence Testing
    3. Assessing Dose Linearity
    4. Summary
    5. References
  12. Allocation in Randomized Clinical Trials
    1. Introduction
    2. Permuted Block Randomization
    3. Variations of Permuted Block Randomization
    4. Allocations Balanced on Baseline Covariates
    5. Summary
    6. Acknowledgements
    7. References
  13. Sample-Size Analysis for Traditional Hypothesis Testing: Concepts and Issues
    1. Introduction
    2. Research Question 1: Does "QCA" Decrease Mortality in Children with Severe Malaria?
    3. p-Values, α, β and Power
    4. A Classical Power Analysis
    5. Beyond α and β: Crucial Type I and Type II Error Rates
    6. Research Question 1, Continued: Crucial Error Rates for Mortality Analysis
    7. Research Question 2: Does "QCA" Affect the "Elysemine: Elysemate" Ratios (EER)?
    8. Crucial Error Rates When the Null Hypothesis Is Likely to Be True
    9. Table of Crucial Error Rates
    10. Summary
    11. Acknowledgments
    12. References
    13. Appendix A Guidelines for "Statistical Considerations" Sections
    14. Appendix B SAS Macro Code to Automate the Programming
  14. Design and Analysis of Dose-Ranging Clinical Studies
    1. Introduction
    2. Design Considerations
    3. Detection of Dose-Response Trends
    4. Regression Modeling
    5. Dose-Finding Procedures
    6. Summary
    7. References
  15. Analysis of Incomplete Data
    1. Introduction
    2. Case Studies
    3. Data Setting and Modeling Framework
    4. Simple Methods and MCAR
    5. MAR Methods
    6. Categorical Data
    7. MNAR Modeling
    8. Sensitivity Analysis
    9. Summary
    10. References
  16. Reliability and Validity: Assessing the Psychometric Properties of Rating Scales
    1. Introduction
    2. Reliability
    3. Validity and other topics
    4. Summary
    5. References
  17. Decision Analysis in Drug Development
    1. Introduction
    2. Introductory example: stop or go?
    3. The Structure of a Decision Analysis
    4. The Go/No Go Problem Revisited
    5. Optimal Sample Size
    6. Sequential Designs in Clinical Trials
    7. Selection of an Optimal Dose
    8. Project Prioritization
    9. Summary
    10. Acknowledgements
    11. References