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E-raamat: Pharmaceutical Statistics Using SAS: A Practical Guide

(Quintiles Inc Overland Park Kansas USA), ,
  • Formaat: 460 pages
  • Sari: SAS Press
  • Ilmumisaeg: 14-Oct-2015
  • Kirjastus: SAS Institute
  • Keel: eng
  • ISBN-13: 9781629590301
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  • Formaat: 460 pages
  • Sari: SAS Press
  • Ilmumisaeg: 14-Oct-2015
  • Kirjastus: SAS Institute
  • Keel: eng
  • ISBN-13: 9781629590301

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This volume covers statistical methods used in drug discovery, animal toxicology studies, and clinical trials, for use by biostatisticians, pharmaceutical researchers, regulatory scientists, academic researchers, and graduate students. The 14 chapters, written by scientists from pharmaceutical companies from Europe and the US, discuss methodological issues, traditional and recently developed approaches to data analysis, and reviews of regulatory guidelines. They demonstrate how to implement the algorithms presented using built-in SAS procedures or custom SAS macros, illustrated using case studies from pre-clinical experiments and clinical trials. Focus is on methods to support research and early drug development activities, and not much emphasis is placed on technical details. Dmitrienko is a research scientist, and Chuang-Stein works in the pharmaceutical industry. D'Agostino is professor of mathematics, statistics, and public health at Boston U. Annotation ©2007 Book News, Inc., Portland, OR (booknews.com)

This essential new book 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, 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, and 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.
Statistics in Drug Development
1(6)
Christy Chuang-Stein
Ralph D'Agostino
Introduction
1(1)
Statistical Support to Non-Clinical Activities
2(1)
Statistical Support to Clinical Testing
3(1)
Battling a High Phase III Failure Rate
4(1)
Do Statisticians Count?
5(1)
Emerging Opportunities
5(1)
Summary
6(1)
References
6(1)
Modern Classification Methods for Drug Discovery
7(38)
Kjell Johnson
William Rayens
Introduction
7(2)
Motivating Example
9(1)
Boosting
10(17)
Model Building
27(6)
Partial Least Squares for Discrimination
33(9)
Summary
42(3)
References
42(3)
Model Building Techniques in Drug Discovery
45(24)
Kimberly Crimin
Thomas Vidmar
Introduction
45(1)
Example: Solubility Data
46(1)
Training and Test Set Selection
47(4)
Variable Selection
51(7)
Statistical Procedures for Model Building
58(3)
Determining When a New Observation Is Not in a Training Set
61(2)
Using SAS Enterprise Miner
63(4)
Summary
67(2)
References
67(2)
Statistical Considerations in Analytical Method Validation
69(28)
Bruno Boulanger
Viswanath Devanaryan
Walthere Dewe
Wendell Smith
Introduction
69(4)
Validation Criteria
73(1)
Response Function or Calibration Curve
74(9)
Linearity
83(2)
Accuracy and Precision
85(3)
Decision Rule
88(4)
Limits of Quantification and Range of the Assay
92(1)
Limit of Detection
93(1)
Summary
93(1)
Terminology
94(3)
References
94(3)
Some Statistical Considerations in Nonclinical Safety Assessment
97(20)
Wherly Hoffman
Cindy Lee
Alan Chiang
Kevin Guo
Daniel Ness
Overview of Nonclinical Safety Assessment
97(1)
Key Statistical Aspects of Toxicology Studies
98(1)
Randomization in Toxicology Studies
99(3)
Power Evaluation in a Two-Factor Model for QT Interval
102(4)
Statistical Analysis of a One-Factor Design with Repeated Measures
106(7)
Summary
113(4)
Acknowledgments
115(1)
References
115(2)
Nonparametric Methods in Pharmaceutical Statistics
117(34)
Paul Juneau
Introduction
117(1)
Two Independent Samples Setting
118(11)
The One-Way Layout
129(15)
Power Determination in a Purely Nonparametric Sense
144(7)
Acknowledgments
149(1)
References
149(2)
Optimal Design of Experiments in Pharmaceutical Applications
151(46)
Valerii Fedorov
Robert Gagnon
Sergei Leonov
Yuehui Wu
Optimal Design Problem
152(7)
Quantal Dose-Response Models
159(6)
Nonlinear Regression Models with a Continuous Response
165(4)
Regression Models with Unknown Parameters in the Variance Function
169(3)
Models with a Bounded Response (Beta Models)
172(4)
Models with a Bounded Response (Logit Link)
176(5)
Bivariate Probit Models for Correlated Binary Responses
181(3)
Pharmacokinetic Models with Multiple Measurements per Patient
184(6)
Models with Cost Constraints
190(2)
Summary
192(5)
References
193(4)
Analysis of Human Pharmacokinetic Data
197(16)
Scott Patterson
Brian Smith
Introduction
197(2)
Bioequivalence Testing
199(5)
Assessing Dose Linearity
204(5)
Summary
209(4)
References
209(4)
Allocation in Randomized Clinical Trials
213(24)
Olga Kuznetsova
Anastasia Ivanova
Introduction
213(1)
Permuted Block Randomization
214(3)
Variations of Permuted Block Randomization
217(11)
Allocations Balanced on Baseline Covariates
228(5)
Summary
233(4)
Acknowledgments
233(1)
References
233(4)
Sample-Size Analysis for Traditional Hypothesis Testing: Concepts and Issues
237(36)
Ralph G. O'Brien
John Castelloe
Introduction
238(2)
Research Question 1: Does ``QCA'' Decrease Mortality in Children with Severe Malaria?
240(1)
p-Values, α, β and Power
241(2)
A Classical Power Analysis
243(6)
Beyond α and β: Crucial Type I and Type II Error Rates
249(2)
Research Question 1, Continued: Crucial Error Rates for Mortality Analysis
251(2)
Research Question 2: Does ``QCA'' Affect the ``Elysemine: Elysemate'' Ratios (EER)?
253(9)
Crucial Error Rates When the Null Hypothesis Is Likely to Be True
262(1)
Table of Crucial Error Rates
263(1)
Summary
263(10)
Acknowledgments
264(1)
References
264(1)
Appendix A Guidelines for ``Statistical Considerations'' Sections
264(1)
Appendix B SAS Macro Code to Automate the Programming
265(8)
Design and Analysis of Dose-Ranging Clinical Studies
273(40)
Alex Dmitrienko
Kathleen Fritsch
Jason Hsu
Stephen Ruberg
Introduction
273(4)
Design Considerations
277(3)
Detection of Dose-Response Trends
280(9)
Regression Modeling
289(5)
Dose-Finding Procedures
294(15)
Summary
309(4)
References
310(3)
Analysis of Incomplete Data
313(48)
Geert Molenberghs
Caroline Beunckens
Herbert Thijs
Ivy Jansen
Geert Verbeke
Michael Kenward
Kristel Van Steen
Introduction
314(2)
Case Studies
316(2)
Data Setting and Modeling Framework
318(1)
Simple Methods and MCAR
319(1)
MAR Methods
320(2)
Categorical Data
322(18)
MNAR Modeling
340(7)
Sensitivity Analysis
347(9)
Summary
356(5)
References
356(5)
Reliability and Validity: Assessing the Psychometric Properties of Rating Scales
361(24)
Douglas Faries
Ilker Yalcin
Introduction
361(1)
Reliability
362(14)
Validity and Other Topics
376(6)
Summary
382(3)
References
383(2)
Decision Analysis in Drug Development
385(44)
Carl-Fredrik Burman
Andy Grieve
Stephen Senn
Introduction
385(1)
Introductory Example: Stop or Go?
386(6)
The Structure of a Decision Analysis
392(2)
The Go/No Go Problem Revisited
394(3)
Optimal Sample Size
397(9)
Sequential Designs in Clinical Trials
406(6)
Selection of an Optimal Dose
412(9)
Project Prioritization
421(5)
Summary
426(3)
Acknowledgments
426(1)
References
426(3)
Index 429