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Success Probability Estimation with Applications to Clinical Trials

Book Description

Provides an introduction to the various statistical techniques involved in medical research and drug development with a focus on estimating the success probability of an experiment

Success Probability Estimation with Applications to Clinical Trials details the use of success probability estimation in both the planning and analyzing of clinical trials and in widely used statistical tests.

Devoted to both statisticians and non-statisticians who are involved in clinical trials, Part I of the book presents new concepts related to success probability estimation and their usefulness in clinical trials, and each section begins with a non-technical explanation of the presented concepts. Part II delves deeper into the techniques for success probability estimation and features applications to both reproducibility probability estimation and conservative sample size estimation.

Success Probability Estimation with Applications to Clinical Trials:

  • Addresses the theoretical and practical aspects of the topic and introduces new and promising techniques in the statistical and pharmaceutical industries

    • Features practical solutions for problems that are often encountered in clinical trials

    • Includes success probability estimation for widely used statistical tests, such as parametric and nonparametric models

    • Focuses on experimental planning, specifically the sample size of clinical trials using phase II results and data for planning phase III trials

    • Introduces statistical concepts related to success probability estimation and their usefulness in clinical trials

Success Probability Estimation with Applications to Clinical Trials is an ideal reference for statisticians and biostatisticians in the pharmaceutical industry as well as researchers and practitioners in medical centers who are actively involved in health policy, clinical research, and the design and evaluation of clinical trials.

Table of Contents

  1. Cover
  2. Half Title page
  3. Title page
  4. Copyright page
  5. Dedication
  6. Preface
  7. Acknowledgments
  8. Acronyms
  9. Introduction: Clinical Trials, Success Rates, and Success Probability
    1. I.1 Overview of clinical trials
    2. I.2 Success rates of clinical trials
    3. I.3 Success probability
    4. I.4 Starting from practice
  10. Part I: Success Probability Estimation in Planning and Analyzing Clinical Trials
    1. Chapter 1: Basic Statistical Tools
      1. 1.1 Pointwise estimation
      2. 1.2 Confidence interval estimation, conservative estimation
      3. 1.3 The statistical hypotheses, the statistical test and the type I error for one-tailed tests
      4. 1.4 The power function and the type II error
      5. 1.5 The p-value
      6. 1.6 The success probability and its estimation
      7. 1.7 Basic statistical tools for two-tailed tests
      8. 1.8 Other statistical hypotheses and tests
    2. Chapter 2: Reproducibility Probability Estimation
      1. 2.1 Pointwise RP estimation
      2. 2.2 RP-testing
      3. 2.3 The RP estimate and the p-value
      4. 2.4 Statistical lower bounds for the RP
      5. 2.5 The γ-stability criterion for statistical significance
      6. 2.6 Other stability criteria for statistical significance
      7. 2.7 Comparing stability criteria
      8. 2.8 Regulatory agencies and the single study
      9. 2.9 The RP for two-tailed tests
      10. 2.10 Discussing Situation I in Section 1.4.1
    3. Chapter 3: Sample Size Estimation
      1. 3.1 The classical paradigm of sample size determination
      2. 3.2 SP estimation for adapting the sample size
      3. 3.3 Launching the trial in practice
      4. 3.4 Practical aspects of SSE
      5. 3.5 Frequentist conservative SSE
      6. 3.6 Optimal frequentist CSSE
      7. 3.7 Bayesian CSSE
      8. 3.8 A comparison of CSSE strategies
      9. 3.9 Discussing Situations I and II in Section 1.4
      10. 3.10 Sample size estimation for the two-tailed setting
    4. Chapter 4: Robustness and Corrections in Sample Size Estimation
      1. 4.1 CSSE strategies with different effect sizes in phases II and III
      2. 4.2 Comparing CSSE strategies in different scenarios
      3. 4.3 Corrections for CSSE strategies
      4. 4.4 A comparison among corrected CSSE strategies
  11. Part II: Success Probability Estimation for Some Widely Used Statistical Tests
    1. Chapter 5: General Parametric SP Estimation
      1. 5.1 The parametric model
      2. 5.2 Power, SP and noncentrality parameter estimation
      3. 5.3 RP estimation and testing
      4. 5.4 Sample size estimation
      5. 5.5 Statistical tests included in the model
    2. Chapter 6: SP Estimation for Student’s t Statistical Tests
      1. 6.1 Test for two means − equal variances
      2. 6.2 Test for two means − unequal variances
      3. 6.3 On Student’s t RP estimates
    3. Chapter 7: SP Estimation for Gaussian Distributed test Statistics
      1. 7.1 Test for two proportions
      2. 7.2 Test for survival: the log-rank test
    4. Chapter 8: SP Estimation for Chi-Square Statistical Tests
      1. 8.1 Test for two multinomial distributions: 2 × C comparative trial
      2. 8.2 Test for S couples of binomial distributions: the Mantel-Haenszel test
      3. 8.3 On chi-square RP estimates
    5. Chapter 9: General Nonparametric SP Estimation - with Applications to the Wilcoxon Test
      1. 9.1 The nonparametric model
      2. 9.2 General nonparametric SP estimation
      3. 9.3 The Wilcoxon rank-sum test
  12. Appendix A: Tables of Quantiles
  13. Appendix B: Tables of RP Estimates for the One-Tailed Z-Test
  14. References
  15. Topic Index
  16. Author Index