Daniele De Martini

Success Probability Estimation with Applications to Clinical Trials (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 232
• Erscheinungstermin: 6. Mai 2013
• Englisch
• ISBN-13: 9781118548318
• Artikelnr.: 38401554

Autorenporträt

DANIELE DE MARTINI, PhD, is Assistant Professor in the Department of Statistics and Quantitative Methods at the University of Milano-Bicocca in Italy. He is also a member of the American Statistical Association, Society for Clinical Trials, and Italian Statistical Society.

Inhaltsangabe

Preface xv Acknowledgments xvii Acronyms xix Introduction xxi I.1 Overview
of clinical trials xxii I.2 Success rates of clinical trials xxiv I.3
Success probability xxv I.4 Starting from practice xxvii PART I SUCCESS
PROBABILITY ESTIMATION IN PLANNING AND ANALYZING CLINICAL TRIALS 1 Basic
statistical tools 3 1.1 Pointwise estimation 4 1.2 Confidence interval
estimation, conservative estimation 6 1.3 The statistical hypotheses, the
statistical test and the type I error for one-tailed tests 10 1.4 The power
function and the type II error 11 1.5 The p-value 14 1.6 The success
probability and its estimation 17 1.7 Basic statistical tools for two
tailed tests 19 1.8 Other statistical hypotheses and tests 23 2
Reproducibility Probability Estimation 25 2.1 Pointwise RP estimation 26
2.2 RP-testing 29 2.3 The RP estimate and the p-value 32 2.4 Statistical
lower bounds for the RP 35 2.5 The stability criterion for statistical
significance 37 2.6 Other stability criteria for statistical significance
40 2.7 Comparing stability criteria 43 2.8 Regulatory agencies and the
single study 45 2.9 The RP for two-tailed tests 46 2.10 Discussing
Situation I in Section I.4.1 49 3 Sample Size Estimation 51 3.1 The
classical paradigm of sample size determination 52 3.2 SP estimation for
adapting the sample size 55 3.3 Launching the trial in practice 57 3.4
Practical aspects of SSE 60 3.5 Frequentist conservative SSE 67 3.6 Optimal
frequentist CSSE 70 3.7 Bayesian CSSE 75 3.8 A comparison of CSSE
strategies 80 3.9 Discussing Situations I and II in Section I.4 83 3.10
Sample size estimation for the two-tailed setting 85 4 Robustness and
Corrections in Sample Size Estimation 89 4.1 CSSE strategies with different
effect sizes in phases II and III 90 4.2 Comparing CSSE strategies in
different Scenarios 91 4.3 Corrections for CSSE strategies 94 4.4 A
comparison among Corrected CSSE strategies 97 PART II SUCCESS PROBABILITY
ESTIMATION FOR SOME WIDELY USED STATISTICAL TESTS 5 General parametric SP
estimation 105 5.1 The parametric model 105 5.2 Power, SP and noncentrality
parameter estimation 106 5.3 RP estimation and testing 107 5.4 Sample size
estimation 108 5.5 Statistical tests included in the model 109 6 SP
estimation for Student's t statistical tests 113 6.1 Test for two means
equal variances 114 6.1.1 Power and RP estimation 114 6.2 Test for two
means unequal variances 117 6.3 On Student's t RP estimates 120 7 SP
estimation for Gaussian distributed test statistics 123 7.1 Test for two
proportions 123 7.2 Test for survival: the log-rank test 127 8 SP
estimation for Chi-square statistical tests 133 8.1 Test for two
multinomial distributions: 2 x C comparative trial 133 8.2 Test for S
couples of binomial distributions: the Mantel-Haenszel test 137 8.3 On chi²
RP estimates 141 9 General nonparametric SP estimation with - applications
to the Wilcoxon test 143 9.1 The nonparametric model 144 9.2 General
nonparametric SP estimation 145 9.3 The Wilcoxon rank-sum test 146 A Tables
of quantiles 161 B Tables of RP estimates for the one-tailed Z test 169
References 179 Topic index 185 Author index 193