Fundamentals of Biostatistics 8th Edition by Bernard Rosner, ISBN-13: 978-1305268920

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Fundamentals of Biostatistics 8th Edition by Bernard Rosner, ISBN-13: 978-1305268920

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• Publisher: ‎ Cengage Learning; 8th edition (August 3, 2015)
• Language: ‎ English
• 927 pages
• ISBN-10: ‎ 130526892X
• ISBN-13: ‎ 978-1305268920

Bernard Rosner’s FUNDAMENTALS OF BIOSTATISTICS is a practical introduction to the methods, techniques, and computation of statistics with human subjects. It prepares students for their future courses and careers by introducing the statistical methods most often used in medical literature. Rosner minimizes the amount of mathematical formulation (algebra-based) while still giving complete explanations of all the important concepts. As in previous editions, a major strength of this book is that every new concept is developed systematically through completely worked out examples from current medical research problems. Most methods are illustrated with specific instructions as to implementation using software either from SAS, Stata, R, Excel or Minitab.

Table of Contents:

Cover Page

Title Page

Copyright Page

Dedication

Preface

Acknowledgments

About the Author

Chapter 1. General Overview

Section Content

Chapter 2. Descriptive Statistics

2.1. Introduction

2.2. Measures of Location

The Arithmetic Mean

The Median

Comparison of the Arithmetic Mean and the Median

The Mode

The Geometric Mean

2.3. Some Properties of the Arithmetic Mean

2.4. Measures Of Spread

The Range

Quantiles

The Variance and Standard Deviation

2.5. Some Properties of the Variance and Standard Deviation

2.6. The Coefficient of Variation

2.7. Grouped Data

2.8. Graphic Methods

Bar Graphs

Stem-and-Leaf Plots

Box Plots

2.9. Case Study 1: Effects of Lead Exposure on Neurological and Psychological Function in Children

2.10. Case Study 2: Effects of Tobacco Use on Bone-Mineral Density in Middle-Aged Women

2.11. Obtaining Descriptive Statistics on the Computer

2.12. Summary

Problems

Chapter 3. Probability

3.1. Introduction

3.2. Definition of Probability

3.3. Some Useful Probabilistic Notation

3.4. The Multiplication Law of Probability

3.5. The Addition Law of Probability

3.6. Conditional Probability

Total-Probability Rule

3.7. Bayes’ Rule and Screening Tests

Bayes’ Rule

3.8. Bayesian Inference

3.9. ROC Curves

3.10. Prevalence and Incidence

3.11. Summary

Problems

Chapter 4. Discrete Probability Distributions

4.1. Introduction

4.2. Random Variables

4.3. The Probability-Mass Function for a Discrete Random Variable

Relationship of Probability Distributions to Frequency Distributions

4.4. The Expected Value of a Discrete Random Variable

4.5. The Variance of a Discrete Random Variable

4.6. The Cumulative-Distribution Function of a Discrete Random Variable

4.7. Permutations and Combinations

4.8. The Binomial Distribution

Using Binomial Tables

Using “Electronic” Tables

4.9. Expected Value and Variance of the Binomial Distribution

4.10. The Poisson Distribution

4.11. Computation of Poisson Probabilities

Using Poisson Tables

Electronic Tables for the Poisson Distribution

4.12. Expected Value and Variance of the Poisson Distribution

4.13. Poisson Approximation to the Binomial Distribution

4.14. Summary

Problems

Chapter 5. Continuous Probability Distributions

5.1. Introduction

5.2. General Concepts

5.3. The Normal Distribution

5.4. Properties of the Standard Normal Distribution

Using Normal Tables

Using Electronic Tables for the Normal Distribution

5.5. Conversion from an N ( μ , σ 2 ) Distribution to an N ( 0 , 1 ) Distribution

5.6. Linear Combinations of Random Variables

5.7. Normal Approximation to the Binomial Distribution

5.8. Normal Approximation to the Poisson Distribution

5.9. Summary

Problems

Chapter 6. Estimation

6.1. Introduction

6.2. The Relationship Between Population and Sample

6.3. Random-Number Tables

6.4. Randomized Clinical Trials

Design Features of Randomized Clinical Trials

6.5. Estimation of the Mean of a Distribution

Point Estimation

Standard Error of the Mean

Central-Limit Theorem

Interval Estimation

t Distribution

6.6. Case Study: Effects of Tobacco Use on Bone-Mineral Density (BMD) in Middle-Aged Women

6.7. Estimation of the Variance of a Distribution

Point Estimation

The Chi-Square Distribution

Interval Estimation

6.8. Estimation for the Binomial Distribution

Point Estimation

Interval Estimation—Normal-Theory Methods

Interval Estimation—Exact Methods

6.9. Estimation for the Poisson Distribution

Point Estimation

Interval Estimation

6.10. One-Sided Confidence Intervals

6.11. The Bootstrap

6.12. Summary

Problems

Chapter 7. Hypothesis Testing: One-Sample Inference

7.1. Introduction

7.2. General Concepts

7.3. One-Sample Test for the Mean of a Normal Distribution: One-Sided Alternatives

7.4. One-Sample Test for the Mean of a Normal Distribution: Two-Sided Alternatives

Using the Computer to Perform the One-Sample t Test

One-Sample z Test

7.5. The Relationship between Hypothesis Testing and Confidence Intervals

7.6. The Power of a Test

One-Sided Alternatives

Two-Sided Alternatives

Using the Computer to Estimate Power

7.7. Sample-Size Determination

One-Sided Alternatives

Sample-Size Determination (Two-Sided Alternatives)

Using the Computer to Estimate Sample Size

Sample-Size Estimation Based on CI Width

7.8. One-Sample χ 2 Test for the Variance of a Normal Distribution

7.9. One-Sample Inference for the Binomial Distribution

Normal-Theory Methods

Using the Computer to Perform the One-Sample Binomial Test (Normal Theory Method)

Exact Methods

Using the Computer to perform the One-Sample Binomial Test (Exact Version)

Power and Sample-Size Estimation

Using the Computer to Estimate Power and Sample Size for the One-Sample Binomial Test

7.10. One-Sample Inference for the Poisson Distribution

7.11. Case Study: Effects of Tobacco Use on Bone-Mineral Density in Middle-Aged Women

7.12. Derivation of Selected Formulas

Derivation of Equation 7.23

7.13. Summary

Problems

Chapter 8. Hypothesis Testing: Two-Sample Inference

8.1. Introduction

8.2. The Paired t Test

8.3. Interval Estimation for the Comparison of Means from Two Paired Samples

8.4. Two-Sample t Test for Independent Samples with Equal Variances

8.5. Interval Estimation for the Comparison of Means from Two Independent Samples (Equal Variance Case)

8.6. Testing for the Equality of Two Variances

The F Distribution

Using the Computer to Obtain Percentiles and Areas for the F Distribution

The F Test

Using the Computer to Perform the F Test for the Equality of Two Variances

8.7. Two-Sample t Test for Independent Samples with Unequal Variances

Using the Computer to Perform the Two-Sample t Test with Unequal Variances

8.8. Case Study: Effects of Lead Exposure on Neurologic and Psychological Function in Children

8.9. Estimation of Sample Size and Power for Comparing Two Means

Estimation of Sample Size

Using the Computer to Estimate Sample Size for Comparing Means from Two Independent Samples

Estimation of Power

Using the Computer to Estimate Power for Comparing Means from Two Independent Samples

8.10. The Treatment of Outliers

8.11. Derivation of Equation 8.13

8.12. Summary

Problems

Chapter 9. Nonparametric Methods

9.1. Introduction

9.2. The Sign Test

Normal-Theory Method

Using the Computer to Perform the Sign Test (Normal Theory Method)

Exact Method

9.3. The Wilcoxon Signed-Rank Test

Using the Computer to Perform the Wilcoxon Signed Rank Test

9.4. The Wilcoxon Rank-Sum Test

Using the Computer to Perform the Wilcoxon Rank Sum Test

9.5. Case Study: Effects of Lead Exposure on Neurological and Psychological Function in Children

9.6. Permutation Tests

Using the Computer to Perform a Permutation Test

9.7. Summary

Problems

Chapter 10. Hypothesis Testing: Categorical Data

10.1. Introduction

10.2. Two-Sample Test for Binomial Proportions

Normal-Theory Method

Contingency-Table Method

Significance Testing Using the Contingency-Table Approach

Using the Computer to Perform the Chi-Square Test for 2 × 2 Tables

10.3. Fisher’s Exact Test

The Hypergeometric Distribution

Using the Computer to Perform Fisher’s Exact Test for 2 × 2 Tables

10.4. Two-Sample Test for Binomial Proportions for Matched-Pair Data (McNemar’s Test)

Normal-Theory Test

Exact Test

Using the Computer to Perform McNemar’s Test for Correlated Proportions

10.5. Estimation of Sample Size and Power for Comparing Two Binomial Proportions

Independent Samples

Using the Computer to Estimate Sample Size and Power for Comparing Two Binomial Proportions

Paired Samples

Sample Size and Power in a Clinical Trial Setting

10.6. R × C Contingency Tables

Tests for Association for R × C Contingency Tables

Using the Computer to Perform the Chi-Square Test for r × c Tables

Chi-Square Test for Trend in Binomial Proportions

Using the Computer to Perform the Chi-Square Test for Trend

Relationship Between the Wilcoxon Rank-Sum Test and the Chi-Square Test for Trend

10.7. Chi-Square Goodness-of-Fit Test

Using the Computer to Perform the Chi-Square Goodness-of-Fit Test

10.8. The Kappa Statistic

Using the Computer to Estimate Kappa

10.9. Derivation of Selected Formulas

Derivation of Equation 10.17

10.10. Summary

Problems

Chapter 11. Regression and Correlation Methods

11.1. Introduction

11.2. General Concepts

11.3. Fitting Regression Lines—The Method of Least Squares

11.4. Inferences About Parameters from Regression Lines

F Test for Simple Linear Regression

Using the Computer to Perform the F Test for Simple Linear Regression

t Test for Simple Linear Regression

Using the Computer to Perform the t Test for Simple Linear Regression

11.5. Interval Estimation for Linear Regression

Interval Estimates for Regression Parameters

Interval Estimation for Predictions Made from Regression Lines

Using the Computer to Obtain Confidence Limits for Predictions from Linear Regression Models

11.6. Assessing the Goodness of Fit of Regression Lines

11.7. The Correlation Coefficient

Relationship Between the Sample Correlation Coefficient ( r ) and the Population Correlation Coefficient ( ρ )

Relationship Between the Sample Regression Coefficient ( b ) and the Sample Correlation Coefficient ( r )

11.8. Statistical Inference for Correlation Coefficients

One-Sample t Test for a Correlation Coefficient

Using the Computer to Perform the One-Sample t Test for Correlation

One-Sample z Test for a Correlation Coefficient

Interval Estimation for Correlation Coefficients

Using the Computer to Obtain Confidence Limits for a Correlation Coefficient

Sample-Size Estimation for Correlation Coefficients

Two-Sample Test for Correlations

11.9. Multiple Regression

Estimation of the Regression Equation

Hypothesis Testing

Criteria for Goodness of Fit

11.10. Case Study: Effects of Lead Exposure on Neurologic and Psychological Function in Children

11.11. Partial and Multiple Correlation

Partial Correlation

Multiple Correlation

11.12. Rank Correlation

11.13. Interval Estimation for Rank-Correlation Coefficients

11.14. Derivation of Equation 11.26

11.15. Summary

Problems

Chapter 12. Multisample Inference

12.1. Introduction to the One-Way Analysis of Variance

12.2. One-Way ANOVA—Fixed-Effects Model

12.3. Hypothesis Testing in One-Way ANOVA—Fixed-Effects Model

F Test for Overall Comparison of Group Means

Using the Computer to Perform One-Way ANOVA

12.4. Comparisons of Specific Groups in One-Way ANOVA

t Test for Comparison of Pairs of Groups

Linear Contrasts

Multiple Comparisons—Bonferroni Approach

Multiple-Comparisons Procedures for Linear Contrasts

The False-Discovery Rate

12.5. Case Study: Effects of Lead Exposure on Neurologic and Psychological Function in Children

Application of One-Way ANOVA

Relationship Between One-Way ANOVA and Multiple Regression

One-Way Analysis of Covariance

12.6. Two-Way Anova

Hypothesis Testing in Two-Way ANOVA

Two-Way ANCOVA

12.7. The Kruskal-Wallis Test

Using the Computer to Perform the Kruskal-Wallis Test

Comparison of Specific Groups Under the Kruskal-Wallis Test

12.8. One-Way ANOVA—The Random-Effects Model

12.9. The Intraclass Correlation Coefficient

Using the Computer to Estimate Intraclass Correlation

12.10. Mixed Models

12.11. Derivation of Equation 12.30

12.12. Summary

Problems

Chapter 13. Design and Analysis Techniques for Epidemiologic Studies

13.1. Introduction

13.2. Study Design

13.3. Measures of Effect for Categorical Data

The Risk Difference

The Risk Ratio

The Odds Ratio

Interval Estimation for the Odds Ratio

Using the Computer to Estimate the Risk Difference, Risk Ratio, and Odds Ratio

13.4. Attributable Risk

Using the Computer to Estimate Attributable Risk

13.5. Confounding and Standardization

Confounding

Standardization

13.6. Methods of Inference for Stratified Categorical Data—The Mantel-Haenszel Test

Estimation of the Odds Ratio for Stratified Data

Effect Modification

Estimation of the OR in Matched-Pair Studies

Using the Computer to Perform the Mantel-Haenszel Test and Estimate the Mantel-Haenszel Odds Ratio

Testing for Trend in the Presence of Confounding—Mantel-Extension Test

13.7. Multiple Logistic Regression

Introduction

General Model

Interpretation of Regression Parameters

Hypothesis Testing

Prediction with Multiple Logistic Regression

Assessing Goodness of Fit of Logistic-Regression Models

13.8. Extensions to Logistic Regression

Conditional Logistic Regression

Polychotomous Logistic Regression

Ordinal Logistic Regression

13.9. Sample Size Estimation for Logistic Regression

13.10. Meta-Analysis

Test of Homogeneity of Odds Ratios

13.11. Equivalence Studies

Introduction

Inference Based on Confidence-Interval Estimation

Sample-Size Estimation for Equivalence Studies

13.12. The Cross-Over Design

Assessment of Treatment Effects

Assessment of Carry-Over Effects

Sample-Size Estimation for Cross-Over Studies

13.13. Clustered Binary Data

Introduction

Hypothesis Testing

Power and Sample Size Estimation for Clustered Binary Data

Regression Models for Clustered Binary Data

13.14. Longitudinal Data Analysis

13.15. Measurement-Error Methods

Introduction

Measurement-Error Correction with Gold-Standard Exposure

Measurement-Error Correction Without a Gold-Standard Exposure

13.16. Missing Data

13.17. Derivation of Selected Formulas

13.18. Summary

Problems

Chapter 14. Hypothesis Testing: Person-Time Data

14.1. Measure of Effect for Person-Time Data

14.2. One-Sample Inference for Incidence-Rate Data

Large-Sample Test

Exact Test

Confidence Limits for Incidence Rates

14.3. Two-Sample Inference for Incidence-Rate Data

Hypothesis Testing—General Considerations

Normal-Theory Test

Exact Test

The Rate Ratio

14.4. Power and Sample-Size Estimation for Person-Time Data

Estimation of Power

Sample-Size Estimation

14.5. Inference for Stratified Person-Time Data

Hypothesis Testing

Estimation of the Rate Ratio

Testing the Assumption of Homogeneity of the Rate Ratio across Strata

14.6. Power and Sample-Size Estimation for Stratified Person-Time Data

Sample-Size Estimation

Estimation of Power

14.7. Testing for Trend: Incidence-Rate Data

14.8. Introduction to Survival Analysis

14.9. Estimation of Survival Curves: The Kaplan-Meier Estimator

The Treatment of Censored Data

Other Types of Censoring

Interval Estimation of Survival Probabilities

Using the Computer to Obtain Kaplan-Meier Survival Probabilities

Estimation of the Hazard Function: The Product-Limit Method

14.10. The Log-Rank Test

14.11. The Proportional-Hazards Model

Testing the Assumptions of the Cox Proportional-Hazards Model

14.12. Power and Sample-Size Estimation Under the Proportional-Hazards Model

Estimation of Power

Estimation of Sample Size

14.13. Parametric Survival Analysis

Weibull Survival Model

Estimation of the Parameters of the Weibull Model

Estimation of Percentiles of the Weibull Survival Function

Assessing Goodness of Fit of the Weibull Model

14.14. Parametric Regression Models for Survival Data

Estimation of Percentiles for the Weibull Survival Distribution

14.15. Derivation of Selected Formulas

(a). Derivation of Equation 14.12

(b). Derivation of Equation 14.17

14.16. Summary

Problems

Appendix

Flowchart

Bernard Rosner is a professor in the Department of Medicine, Harvard Medical School, and the Department of Biostatistics at the Harvard School of Public Health. Dr. Rosner’s research activities currently include longitudinal data analysis, analysis of clustered continuous, binary and ordinal data, methods for the adjustment of regression models for measurement error and modeling of cancer incidence data.

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