Basic Statistics and Pharmaceutical Statistical Applications: 3rd Edition (Hardback) book cover

Basic Statistics and Pharmaceutical Statistical Applications

3rd Edition

By James E. De Muth

Chapman and Hall/CRC

847 pages | 262 B/W Illus.

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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.

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

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.

About the Series

Pharmacy Education Series

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General
MED071000
MEDICAL / Pharmacology
MED072000
MEDICAL / Pharmacy