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Basic Statistics and Pharmaceutical Statistical Applications
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Book Description
Building on its bestselling 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 onesided sign test, Wilcoxon signedranks 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 workedout 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
tTESTS
Parametric Procedures
The tdistribution
Onetailed vs. Twotailed Tests
OneSample ttests
TwoSample ttests
Computer Generated pvalues
Corrected Degrees of Freedom for Unequal Variances
Onesample ttest Revisited for Critical Value
Matched Pair tTest (Difference tTest)
Using Excel or Minitab for Student ttests
ONEWAY ANALYSIS OF VARIANCE (ANOVA)
Hypothesis Testing with the Oneway ANOVA
The Fdistribution
Test Statistic
ANOVA Definitional Formula
ANOVA Computational Formula
Randomized Block Design
Homogeniety of Variance
Using Excel or Minitab for OneWay ANOVAs
MULTIPLE COMPARISON TESTS
Error Associated with Multiple ttests
Overview of Multiple Comparison Tests
The qstatistic
Planned Multiple Comparisons
Bonferroni Adjustment
Sidák test
Dunn's Multiple Comparisons
Dunnett's Test
Post Hoc Procedures
Tukey HSD Test
Student NewmanKeuls 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
TwoWay 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 Twoway Factorial Design
Using Excel or Minitab for TwoWay ANOVAs
CORRELATION
Graphic Representation of Two Continuous Variables
Covariance
Pearson ProductMoment Correlation Coefficient
Correlation Line
Statistical Significance of a Correlation Coefficient
Correlation and Causality
In vivo and In vitro Correlation
Other Types of Bivariate Correlations
Pairwise 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
LackofFit 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
ZTESTS OF PROPORTIONS
ztest of Proportions  Onesample Case
ztest of Proportions  Twosample Case
Estimating Power and Desired Sample size for twosample Ztest of Proportions
ztests 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 Goodnessoffit Test
Chi Square Test of Independence
Yates' Correction for 2x2 Contingency Table
Comparison of Chi Square to the Ztest of Proportions
Fisher's Exact Test
McNemar's Test
Cochran's Q Test
MantelHaenszel Test
MEASURES OF ASSOCIATION
Introduction
Dichotomous Associations
Nominal Associations
Ordinal Associations
Nominalbyinterval 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
MantelHaenszel Estimate of Relative Risk
Logistic Regression
EVIDENCEBASED PRACTICE: AN INTRODUCTION
Sensitivity and Specificity
TwobyTwo 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
KaplanMeier 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
MannWhitney U Test
Median Test
Wilcoxon MatchedPairs Test
Sign Test
KruskalWallis Test
Post hoc Comparisons using KruskalWallis
Friedman TwoWay Analysis of Variance
Spearman RankOrder Correlation
Theil's Incomplete Method
KolmogorovSmirnov GoodnessofFit Test
AndersonDarling Test
Runs Tests
Range Tests
STATISTICAL TESTS FOR EQUIVALENCE
Bioequivalence Testing
Experimental Designs for Bioequivalence Studies
Twosample ttest Example
Power in Bioequivalence Tests
Rules for Bioequivalence
Creating Confidence Intervals
Comparison Using Two Onesided ttests
Clinical Equivalence and Noninferiority
Dissolution Testing
SUPACIR 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.
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 goto 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, 2015Praise 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 nonstatistician 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 nonstatistician 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 entrylevel 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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