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Statistics in Medicine

ISBN-10: 0120887703

ISBN-13: 9780120887705

Edition: 2nd 2006 (Revised)

Authors: Robert H. Riffenburgh

List price: $74.95
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Medicine deals with treatments that work often but not always, so treatment success must be based on probability. Statistical methods lift medical research from the anecdotal to measured levels of probability. This book presents the common statistical methods used in 90% of medical research, along with the underlying basics, in two parts: a textbook section for use by students in health care training programs, e.g., medical schools or residency training, and a reference section for use by practicing clinicians in reading medical literature and performing their own research. The book does not require a significant level of mathematical knowledge and couches the methods in multiple examples drawn from clinical medicine, giving it applicable context. *Easy-to-follow format includes medical examples, step-by-step methods, and check yourself exercises *Includes a summary of formulas, method algorithms, and check lists in a back section. *Thorough discussion on required sample size *Covers germane topics honed through multiple lecture series to hospital staff and residents. *Facilitates scanning and review by leading off each paragraph with its topic or gist in italics *Covers important topics omitted in most biostatistics texts *Multiple, curvlinear, and logarithmic regression *Survival methods
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Book details

List price: $74.95
Edition: 2nd
Copyright year: 2006
Publisher: Elsevier Science & Technology Books
Publication date: 9/7/2005
Binding: Hardcover
Pages: 672
Size: 7.01" wide x 10.00" long x 0.56" tall
Weight: 3.014
Language: English

Robert H. Riffenburgh, Ph.D. provides advice on experimental design, statistical analysis, and scientific integrity on the approximately 140 new studies beginning each year at the Naval Medical Center San Diego. He is former professor and Head, Dept. of Statistics, Univ. of Connecticut, and has been faculty at Va Tech., Univ. Hawaii, Univ. Maryland, Univ. California San Diego, San Diego State Univ., and in Leidin (The Netherlands). He has been president of his own consulting firm and performed and directed operations research for the U.S. Government and NATO. He has consulted periodically on medical statistics throughout his career and full-time for the last eight years. He has received numerous awards and has published more than 125 professional papers.

Foreword to the Second Edition
Foreword to the First Edition
A Study Course of Fundamentals
Data, Notation, and Some Basic Terms
About This Book
Stages of Scientific Knowledge
Quantification and Accuracy
Data Types
Notation (or Symbols)
Samples, Populations, and Randomness
Frequency Distributions
Relative Frequencies and Probabilities
Characteristics of a Distribution
What Is Typical?
The Spread About the Typical
The Shape
Statistical Inference
Distributions Commonly Used in Statistics
Standard Error of the Mean
Joint Distributions of Two Variables
Summary Statistics
Numerical Summaries, One Variable
Numerical Summaries, Two Variables
Pictorial Summaries, One Variable
Pictorial Summaries, Two Variables
Good Graphing Practices
Confidence Intervals and Probability
The Normal Distribution
Confidence Interval on an Observation from an Individual Patient
Concept of a Confidence Interval on a Descriptive Statistic
Confidence Interval on a Mean, Known Standard Deviation
The t Distribution
Confidence Interval on a Mean, Estimated Standard Deviation
The Chi-square Distribution
Confidence Interval on a Variance or Standard Deviation
Other Frequently Seen Confidence Intervals and Probabilities
Hypothesis Testing: Concept and Practice
Hypotheses in Inference
Error Probabilities
Two Policies of Testing
Organizing Data for Inference
Evolving a Way to Answer Your Data Question
Statistical Testing, Risks, and Odds in Medical Decisions
Categorical Data: Basics
Categorical Data: Tests on 2 x 2 Tables
Categorical Data: Risks and Odds
Rank Data: Basics
Rank Data: The Rank-Sum Test to Compare Two Samples
Continuous Data: Basics of Means
Continuous Data: Normal (z) and t Tests to Compare Two Sample Means
Other Tests of Hypotheses
Sample Size Required for a Study
Is the Estimate of Minimum Required Sample Size Adequate?
Sample Size in Means Testing
Minimum Sample Size Estimation for a Test of Two Means
Other Situations in Which Minimum Sample Size Estimation Is Used
Statistical Prediction
What Is a "Model"?
Straight-Line Models
What Is "Regression" (and Its Relation to Correlation)?
Assessing and Predicting Relationships by Regression
Other Questions That Can Be Answered by Regression
Clinical Decisions and Outcomes Analysis
The Nature of Epidemiology
Some Key Stages in the History of Epidemiology
Concept of Disease Transmission
Descriptive Measures
Types of Epidemiologic Studies
An Informal Approach to Public Health Problems
Analysis of Survival and Causal Factors
Reading Medical Articles
Assessing Medical Information from an Article
Keep in Mind How a Study Is Constructed
Study Types
Sampling Bias
Statistical Aspects Where Articles May Fall Short
Evolving Terms: Meta-analysis, Multivariable Analysis, and Others
Selection of Statistical Tests to Use in a Study
Answers to Chapter Exercises, Part I
A Reference Guide
Using the Reference Guide
How to Use This Guide
Basic Concepts Needed to Use This Guide
Planning Medical Studies
The Science Underlying Clinical Decision Making
The Objective of Statistics
Concepts in Study Design
Sampling Schemes
How to Randomize a Sample
How to Plan and Conduct a Study
Mechanisms to Improve Your Study Plan
How to Manage Data
Setting Up a Test Within a Study
Choosing the Right Test
Statistical Ethics in Medical Studies
Finding Probabilities of Error
The Normal Distribution
The t Distribution
The Chi-square Distribution
The F Distribution
The Binomial Distribution
The Poisson Distribution
Confidence Intervals
Confidence Interval on a Mean, Known Standard Deviation
Confidence Interval on a Mean, Estimated Standard Deviation
Confidence Interval on a Variance or Standard Deviation
Confidence Interval on a Proportion
Confidence Interval on a Correlation Coefficient
Tests on Categorical Data
Categorical Data Summary
2 x 2 Tables: Contingency Tests
r x c Tables: Contingency Tests
Risks and Odds in Medical Decisions
2 x 2 Tables: Tests of Association
Tests of Proportion
Tests of a Small Proportion (Close to Zero)
Matched Pair Test (McNemar's Test)
Tests on Ranked Data
Basics of Ranks
Single or Paired Small Samples: The Signed-Rank Test
Two Small Samples: The Rank-Sum Test
Three or More Independent Samples: The Kruskal-Wallis Test
Three or More Matched Samples: The Friedman Test
Single Large Samples: Normal Approximation to Signed-Rank Test
Two Large Samples: Normal Approximation to Rank-Sum Test
Tests on Means of Continuous Data
Summary of Means Testing
Normal (z) and t Tests for Single or Paired Means
Post Hoc Confidence and Power
Normal (z) and t Tests for Two Means
Three or More Means: One-Way Analysis of Variance
Multifactor Tests on Means of Continuous Data
Concepts of Experimental Design
Two-Factor Analysis of Variance
Repeated-Measures Analysis of Variance
Analysis of Covariance
Three- and Higher-Factor Analysis of Variance
More Specialized Designs and Techniques
Tests on Variances of Continuous Data
Basics of Tests on Variability
Single Samples
Two Samples
Three or More Samples
Tests on the Distribution Shape of Continuous Data
Objectives of Tests on Distributions
Test of Normality of a Distribution
Test of Equality of Two Distributions
Equivalence Testing
Concepts and Terms
Basics Underlying Equivalence Testing
Methods for Nonsuperiority Testing
Methods for Equivalence Testing
Sample Size Required in a Study
Relation of Sample Size Calculated to Sample Size Needed
Sample Size for Tests on Means
Sample Size for Confidence Intervals on Means
Sample Size for Tests on Rates (Proportions)
Sample Size for a Confidence Interval on a Rate (Proportion)
Sample Size for Significance of a Correlation Coefficient
Sample Size for Tests on Ranked Data
Sample Size for Tests on Variances, Analysis of Variance, and Regression
Modeling and Clinical Decisions
Overview of Modeling
Straight-Line Models
Curved Models
Constants of Fit for Any Model
Multiple-Variable Models
Clinical Decision Based on Recursive Partitioning
Number Needed to Treat or to Benefit
Clinical Decision Based on Measures of Effectiveness: Outcomes Analysis
Regression and Correlation Methods
Regression Concepts and Assumptions
Correlation Concepts and Assumptions
Simple Regression
Correlation Coefficients
Tests and Confidence Intervals on Regression Parameters
Tests and Confidence Intervals on Correlation Coefficients
Curved Regression
Multiple Regression
Types of Regression
Logistic Regression
Survival and Time-Series Analysis
Time-Dependent Data
Survival Curves: Estimation
Survival Curves: Testing
Sequential Analysis
Time Series: Detecting Patterns
Time-Series Data: Testing
Methods You Might Meet, But Not Every Day
Analysis of Variance Issues
Regression Issues
Multivariate Methods
Nonparametric Tests
Imputation of Missing Data
Resampling Methods
Agreement Measures and Correlation
Bonferroni "Correction"
Logit and Probit
Adjusting for Outliers
Curve Fitting to Data
Tests of Normality
Chapter Summaries
References and Data Sources
Tables of Probability Distributions
Normal Distribution
t Distribution
Chi-square Distribution, Right Tail
Chi-square Distribution, Left Tail
F Distribution
Binomial Distribution
Poisson Distribution
Signed-Rank Probabilities
Rank-Sum U Probabilities
Symbol Index
Subject Index