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3rd Edition

Basic Statistics and Pharmaceutical Statistical Applications




ISBN 9781466596733
Published April 28, 2014 by Chapman and Hall/CRC
847 Pages 262 B/W Illustrations

 
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Book Description

Building on its best-selling predecessors, Basic Statistics and Pharmaceutical Statistical Applications, Third Edition covers statistical topics most relevant to those in the pharmaceutical industry and pharmacy practice. It focuses on the fundamentals required to understand descriptive and inferential statistics for problem solving. Incorporating new material in virtually every chapter, this third edition now provides information on software applications to assist with evaluating data.

New to the Third Edition

  • Use of Excel® and Minitab® for performing statistical analysis
  • Discussions of nonprobability sampling procedures, determining if data is normally distributed, evaluation of covariances, and testing for precision equivalence
  • Expanded sections on regression analysis, chi square tests, tests for trends with ordinal data, and tests related to survival statistics
  • Additional nonparametric procedures, including the one-sided sign test, Wilcoxon signed-ranks test, and Mood’s median test

With the help of flow charts and tables, the author dispels some of the anxiety associated with using basic statistical tests in the pharmacy profession and helps readers correctly interpret their results using statistical software. Through the text’s worked-out examples, readers better understand how the mathematics works, the logic behind many of the equations, and the tests’ outcomes.

Table of Contents

INTRODUCTION
Types of Statistics
Parameters and Statistics
Sampling and Independent Observations
Types of Variables
Independent and Dependent Variables
Selection of the Appropriate Statistical Test
Procedures for Inferential Statistical Tests
Applications of Computer Software

PROBABILITY
Classic Probability
Probability Involving Two Variables
Conditional Probability
Probability Distribution
Counting Techniques
Binomial Distribution
Poisson Distribution

SAMPLING
Random Sampling
Using Minitab or Excel to Generate a Random Sample
Other Probability Sampling Procedures
Nonprobability Sampling Procedure
Random Assignment to Two or More Experimental Levels
Precision, Accuracy, and Bias
Reliability and Validity

PRESENTATION MODES
Tabulation of Data
Visual Displays for Discrete Variables
Visual Displays for Continuous Variables
Visual Displays for Two or More Continuous Variables
Using Excel or Minitab for Visual Displays

MEASURES OF CENTRAL TENDENCY
Centers of a Continuous Distribution
Dispersion Within a Continuous Distribution
Population versus Sample Measures of Central Tendency
Measurements Related to the Sample Standard Deviation
Trimmed Mean
Using Excel or Minitab for Measures of Central Tendency
Alternative Computational Methods for Calculating Central Tendency

THE NORMAL DISTRIBUTION AND DATA TRANSFORMATION
The Normal Distribution
Determining if the Distribution is Normal
Data Transformations: An Overview
Lognormal Transformation and the Geometric Mean
Other Types of Transformations
Using Excel or Minitab to Evaluate Normality

CONFIDENCE INTERVALS AND TOLERANCE LIMITS
Sampling Distribution
Standard Error of the Mean versus the Standard Deviation
Confidence Intervals
Statistical Control Charts
Process Capability
Tolerance Limits
Using Excel or Minitab for Applications Discussed

HYPOTHESIS TESTING
Hypothesis Testing
Types of Errors
Type I Error
Type II Error and Power
Experimental Errors and Propagation of Errors

t-TESTS
Parametric Procedures
The t-distribution
One-tailed vs. Two-tailed Tests
One-Sample t-tests
Two-Sample t-tests
Computer Generated p-values
Corrected Degrees of Freedom for Unequal Variances
One-sample t-test Revisited for Critical Value
Matched Pair t-Test (Difference t-Test)
Using Excel or Minitab for Student t-tests

ONE-WAY ANALYSIS OF VARIANCE (ANOVA)
Hypothesis Testing with the One-way ANOVA
The F-distribution
Test Statistic
ANOVA Definitional Formula
ANOVA Computational Formula
Randomized Block Design
Homogeniety of Variance
Using Excel or Minitab for One-Way ANOVAs

MULTIPLE COMPARISON TESTS
Error Associated with Multiple t-tests
Overview of Multiple Comparison Tests
The q-statistic 
Planned Multiple Comparisons
Bonferroni Adjustment
Sidák test
Dunn's Multiple Comparisons
Dunnett's Test
Post Hoc Procedures
Tukey HSD Test
Student Newman-Keuls Test
Fisher LSD Test
Scheffé Procedure
Scheffé Procedure for Complex Comparisons
Unbalanced Designs
Lack of Homogeneity
Other Post Hoc Tests
Using Minitab for Multiple Comparisons

FACTORIAL DESIGNS: AN INTRODUCTION
Factorial Designs
Two-Way Analysis of Variance
Computational Formula with Unequal Cell Size
Post Hoc Procedures 
Repeated Measures Design
Repeatability and Reproducibility
Latin Square Designs
Other Designs
Fixed, Random, and Mixed Effect Models
Beyond a Two-way Factorial Design
Using Excel or Minitab for Two-Way ANOVAs

CORRELATION
Graphic Representation of Two Continuous Variables
Covariance
Pearson Product-Moment Correlation Coefficient
Correlation Line
Statistical Significance of a Correlation Coefficient
Correlation and Causality
In vivo and In vitro Correlation
Other Types of Bivariate Correlations
Pair-wise Correlations Involving More Than Two Variables
Multiple Correlations
Partial Correlations
Nonlinear Correlations
Assessing Independence and Randomness
Using Excel or Minitab for Correlation

REGRESSION ANALYSIS 
The Regression Line
Coefficient of Determination
ANOVA Table
Confidence Intervals and Hypothesis Testing for the Population Slope (b)
Confidence Intervals and Hypothesis Testing for the Population Intercept (a)
Confidence Intervals for the Regression Line 
Inverse Prediction
Multiple Data at Various Points on the Independent Variable
Lack-of-Fit Test
Assessing Parallelism of the Slopes of Two Sample
Curvilinear and Nonlinear Regression
Multiple Linear Regression Models
Stepwise Regression
Using Excel or Minitab for Regression

Z-TESTS OF PROPORTIONS
z-test of Proportions - One-sample Case
z-test of Proportions - Two-sample Case
Estimating Power and Desired Sample size for two-sample Z-test of Proportions
z-tests for Proportions - Yates' Correction for Continuity
Proportion Testing for More Than Two Levels of a Discrete
Independent Variable

CHI SQUARE TESTS
Chi Square Statistic
Chi Square for One Discrete Independent Variable
Chi Square Goodness-of-fit Test
Chi Square Test of Independence
Yates' Correction for 2x2 Contingency Table
Comparison of Chi Square to the Z-test of Proportions
Fisher's Exact Test
McNemar's Test
Cochran's Q Test
Mantel-Haenszel Test

MEASURES OF ASSOCIATION
Introduction
Dichotomous Associations
Nominal Associations
Ordinal Associations
Nominal-by-interval Associations
Reliability Measurements

ODDS RATIOS AND RELATIVE RISK RATIOS
Probability, Odds, and Risk
Odds Ratio
Relative Risk
Graphic Display for Odds Ratios and Relative Risk Ratios
Mantel-Haenszel Estimate of Relative Risk
Logistic Regression

EVIDENCE-BASED PRACTICE: AN INTRODUCTION
Sensitivity and Specificity
Two-by-Two Contingency Table
Defining Evidence Based Practice
Frequentist versus Bayesian Approaches to Probability
Predictive Values
Likelihood Ratios

SURVIVAL STATISTICS
Censored Survival Data
Life Table Analysis
Survival Curve
Kaplan-Meier Procedure
Visually Comparison of Two Survival Curves
Tests to Compare Two Levels of an Independent Variable
Hazard Ratio
Multiple Regression with Survival Data:
Proportional Hazards Regression
Other Measures and Tests of Survival

NONPARAMETRIC TESTS 
Use of Nonparametric Tests
Ranking of Information
Mann-Whitney U Test
Median Test
Wilcoxon Matched-Pairs Test
Sign Test
Kruskal-Wallis Test
Post hoc Comparisons using Kruskal-Wallis
Friedman Two-Way Analysis of Variance
Spearman Rank-Order Correlation
Theil's Incomplete Method
Kolmogorov-Smirnov Goodness-of-Fit Test
Anderson-Darling Test 
Runs Tests
Range Tests

STATISTICAL TESTS FOR EQUIVALENCE
Bioequivalence Testing
Experimental Designs for Bioequivalence Studies
Two-sample t-test Example
Power in Bioequivalence Tests
Rules for Bioequivalence
Creating Confidence Intervals
Comparison Using Two One-sided t-tests
Clinical Equivalence and Non-inferiority
Dissolution Testing
SUPAC-IR Guidance

OUTLIER TESTS
Regulatory Considerations
Outliers on a Single Continuum
Plotting and Number of Standard Deviations from the Center
The "Huge" Rule
Grubbs' Test for Outlying Observations
Dixon Q Test
Hampel's Rule
Multiple Outliers
Bivariate Outliers in Correlation and Regression Analysis

STATISTICAL ERRORS IN THE LITERATURE
Errors and the Peer Review Process
Problems with Experimental Design
Standard Deviations versus Standard Error of the Mean
Problems with Hypothesis Testing
Problems with Parametric Statistics
Errors with the Chi Square Test of Independence

Appendix A: Flow Chart for the Selection of Appropriate Tests
Appendix B: Statistical Tables
Appendix C: Summary of Commands for Excel
and Minitab
Appendix D: Answers to Example Problems

Index

References, Supplemental Readings, and Example Problems appear at the end of each chapter.

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Author(s)

Biography

James E. De Muth

Reviews

"The book’s coverage … is immense and very impressive. The book also well describes introductory statistics, and coverage of normal outcomes was exemplary. The multiple comparisons and nonparametric statistics chapters in particular were outstanding. The third edition has made notable improvements over the second edition in several chapters; there are too many to describe here. … very well written and easy to read. … very useful and unique reading, given its wide practical coverage and the approaches taken. I have added this book to my go-to reference sources … a very good teaching introduction to statistics for undergraduate and graduate students … This book may be most useful for persons involved in preclinical and Phase 1 studies where standard normal, binomial, and nonparametric methods are used."
Journal of Biopharmaceutical Statistics, 2015

Praise for the Second Edition:
"Dr. De Muth writes clearly about a very complex subject … The second edition has been expanded and is an even more comprehensive description of the statistics used within the pharmaceutical industry and the health care system. … a very useful reference tool for the pharmaceutical scientist and clinician…"
—Frank J. Ascione, University of Michigan College of Pharmacy

"De Muth has written a book that is both elegant and simple … [it] enables the reader to clearly understand how to appropriately use statistics in designing studies and just as importantly determine when statistics should not be used … an excellent reference book that will enable the non-statistician to appropriately use statistical approaches … A unique attribute of this statistical textbook is the acknowledgement of how statistical tests can be misused … useful information helps the non-statistician avoid some of the common errors that are made when using statistical approaches in the analysis of data."
—Mark N. Milton, Millennium Pharmaceuticals, Inc.

"The book is laid out well, and the organization follows an intuitive path, beginning with an introduction to statistics that is appropriate for entry-level students. I found the appendix that focuses on statistical errors commonly encountered in the literature particularly enlightening. In summary, this book is an excellent choice for beginning or intermediate researchers interested in designing, implementing, and reporting statistically sound studies."
—Dawn Boothe, DVM, PhD, DACVIM, DACVCP, Auburn University, Auburn, Alabama, Journal of the American Veterinary Medical Association, March 2015

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