-23%
76,99 €
Statt 99,99 €**
76,99 €
inkl. MwSt.
**Preis der gedruckten Ausgabe (Gebundenes Buch)
Sofort per Download lieferbar
0 °P sammeln
-23%
76,99 €
Statt 99,99 €**
76,99 €
inkl. MwSt.
**Preis der gedruckten Ausgabe (Gebundenes Buch)
Sofort per Download lieferbar

Alle Infos zum eBook verschenken
0 °P sammeln
Als Download kaufen
Statt 99,99 €**
-23%
76,99 €
inkl. MwSt.
**Preis der gedruckten Ausgabe (Gebundenes Buch)
Sofort per Download lieferbar
0 °P sammeln
Jetzt verschenken
Statt 99,99 €**
-23%
76,99 €
inkl. MwSt.
**Preis der gedruckten Ausgabe (Gebundenes Buch)
Sofort per Download lieferbar

Alle Infos zum eBook verschenken
0 °P sammeln
  • Format: PDF


Provides an introduction to the various statistical techniquesinvolved in medical research and drug development with a focus onestimating the success probability of an experiment Success Probability Estimation with Applications to ClinicalTrials details the use of success probability estimation inboth the planning and analyzing of clinical trials and in widelyused statistical tests. Devoted to both statisticians and non-statisticians who areinvolved in clinical trials, Part I of the book presents newconcepts related to success probability estimation and theirusefulness in clinical trials, and…mehr

Produktbeschreibung
Provides an introduction to the various statistical techniquesinvolved in medical research and drug development with a focus onestimating the success probability of an experiment Success Probability Estimation with Applications to ClinicalTrials details the use of success probability estimation inboth the planning and analyzing of clinical trials and in widelyused statistical tests. Devoted to both statisticians and non-statisticians who areinvolved in clinical trials, Part I of the book presents newconcepts related to success probability estimation and theirusefulness in clinical trials, and each section begins with anon-technical explanation of the presented concepts. Part II delvesdeeper into the techniques for success probability estimation andfeatures applications to both reproducibility probabilityestimation and conservative sample size estimation. Success Probability Estimation with Applications to ClinicalTrials: * Addresses the theoretical and practical aspects of thetopic and introduces new and promising techniques in thestatistical and pharmaceutical industries * Features practical solutions for problems that are oftenencountered in clinical trials * Includes success probability estimation for widely usedstatistical tests, such as parametric and nonparametric models * Focuses on experimental planning, specifically the sample sizeof clinical trials using phase II results and data for planningphase III trials * Introduces statistical concepts related to success probabilityestimation and their usefulness in clinical trials Success Probability Estimation with Applications to ClinicalTrials is an ideal reference for statisticians andbiostatisticians in the pharmaceutical industry as well asresearchers and practitioners in medical centers who are activelyinvolved in health policy, clinical research, and the design andevaluation of clinical trials.

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

  • Produktdetails
  • Verlag: John Wiley & Sons
  • Seitenzahl: 232
  • Erscheinungstermin: 1. August 2013
  • Englisch
  • ISBN-13: 9781118548288
  • Artikelnr.: 39477434
Autorenporträt
DANIELE DE MARTINI, PhD, is Assistant Professor in theDepartment of Statistics and Quantitative Methods at the Universityof Milano-Bicocca in Italy. He is also a member of the AmericanStatistical Association, Society for Clinical Trials, and ItalianStatistical 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