A Guide to Design, Analysis and Discovery. To order this title, and for more information, click here Second Edition
By Ronald Forthofer, Boulder County, Colorado, U.S.A. Eun Lee, Oregon Health Science University, Portland, U.S.A. Mike Hernandez, Anderson Cancer Center, Houston, Texas, U.S.A.
Description Today, mathematics, biology, medicine, and statistics are closing the interdisciplinary gap in an unprecedented way and many of the important
unanswered questions now emerge at the interface of these disciplines. Now in its Second Edition, this user-friendly guide on biostatistics
focuses on the proper use and interpretation of statistical methods. This textbook does not require extensive background in mathematics,
making it user-friendly for all students in the public health sciences field. Instead of highlighting derivations of formulas, the authors
provide rationales for the formulas, allowing students to grasp a better understanding of the link between biology and statistics. The
material on life tables and survival analysis allows students to better understand the recent literature in the health field, particularly
in the study of chronic disease treatment. Biostatistics now includes a companion website to demonstrate the different
applications of computer packages for performing the various analyses presented in this text.
Audience
Students in the health sciences, public health professionals, practitioners.
Contents 1. INTRODUCTION
1.1 What is Biostatistics?
1.2 Data – The Key Component of a Study
1.3 Design – The Road to Relevant Data
1.4
Replication – Part of the Scientific Method
1.5 Applying Statistical Methods
Concluding Remarks
Exercises
References
2. DATA
AND NUMBERS
2.1 Data: Numerical Representation
2.2 Observations and Variables
2.3 Scales Used with Variables
2.4 Reliability and
Validity
2.5 Randomized Response Technique
2.6 Common Data Problems
Concluding Remarks
Exercises
References
3. DESCRIPTIVE
METHODS
3.1 Introduction to Descriptive Methods
3.2 Tabular and Graphic Presentation of Data
3.2.1 Frequency Tables
3.2.2 Line
Graphs
3.2.3 Bar Charts
3.2.4 Histograms
3.2.5 Stem-and-Leaf Plots
3.2.6 Dot Plots
3.2.7 Scatter Plots
3.3 Measures of Central
Tendency
3.3.1 Mean, Median, and Mode
3.3.2 Use of the Measures of Central Tendency
3.3.3 The Geometric Mean
3.4 Measures of Variability
3.4.1 Ranges and Percentiles
3.4.2 Box Plots
3.4.3 Variance and Standard Deviation
3.5 Rates and Ratios
3.5.1 Crude and Specific
Rates
3.5.2 Adjusted Rates
3.6 Measures of Change Over Time
3.6.1 Linear Growth
3.6.2 Geometric Growth
3.6.3 Exponential Growth
3.7 Correlation Coefficients
3.7.1 Pearson Correlation Coefficient
3.7.2 Spearman Rank Correlation Coefficient
Concluding Remarks
Exercises
References
4. PROBABILITY AND LIFE TABLES
4.1 A Definition of Probability
4.2 Rules for Calculating Probabilities
4.2.1 Addition Rule for Probabilities
4.2.2 Conditional Probabilities
4.2.3 Independent Events
4.3 Definitions from Epidemiology
4.3.1 Rates and Probabilities
4.3.2 Sensitivity, Specificity, and Predicted Value Positive and Negative
4.3.3 Receiver Operating
Characteristic Plot
4.4 Bayes' Theorem
4.5 Probability in Sampling
4.5.1 Sampling With Replacement
4.5.2 Sampling Without Replacement
4.6 Estimating Probabilities by Simulation
4.7 Probability and the Life Table
4.7.1 The First Four Columns of the Life Table
4.7.2
Some Uses of the Life Table
4.7.3 Expected Values in the Life Table
4.7.4 Other Expected Values in the Life Table
Concluding Remarks
Exercises
References
5. PROBABILITY DISTRIBUTIONS
5.1 The Binomial Distribution
5.1.1 Binomial Probabilities
5.1.2 Mean
and Variance of the Binomial Distribution
5.1.3. Shapes of the Binomial Distribution
5.2 The Poisson Distribution
5.2.1 Poisson
Probabilities
5.2.2 Mean and Variance of the Poisson Distribution
5.2.3 Finding Poisson Probabilities
5.3 The Normal Distribution
5.3.1 Normal Probabilities
5.3.2 Transforming to the Standard Normal Distribution
5.3.3 Calculation of Normal Probabilities
5.3.4 Normal
Probability Plot
5.4 The Central Limit Theorem
5.5 Approximations to the Binomial and Poisson Distributions
5.5.1 Normal
Approximation to the Binomial Distribution
5.5.2 Normal Approximation to the Poisson Distribution
Concluding Remarks
Exercises
References
6. STUDY DESIGNS
6.1 Design: Putting Chance to Work
6.2 Sample Surveys and Experiments
6.3 Sampling and Sample Designs
6.3.1
Sampling Frame
6.3.2 Importance of Probability Sampling
6.3.3 Simple Random Sampling
6.3.4 Systematic Sampling
6.3.5 Stratified
Random Sampling
6.3.6 Cluster Sampling
6.3.7 Problems Due to Unintended Sampling
6.4 Designed Experiments
6.4.1 Comparison Groups
and Randomization
6.4.2 Random Assignment
6.4.3 Sample Size
6.4.4 Single and Double Blind Experiments
6.4.5 Blocking and Extraneous
Variables
6.4.6 Limitations of Experiments
6.5 Variations in Study Designs
6.5.1 The Cross-Over Design
6.5.2 The Case Control
Design
6.5.3 The Cohort Study Design
Concluding Remarks
Exercises
References
7. INTERVAL ESTIMATION
7.1 Prediction, Confidence,
and Tolerance Intervals
7.2 Distribution-Free Intervals
7.2.1 Prediction Interval
7.2.2 Confidence Interval
7.2.3 Tolerance
Interval
7.3 Confidence Intervals Based on the Normal Distribution
7.3.1 Confidence Interval for the Mean
7.3.2 Confidence Interval
for a Proportion
7.3.3 Confidence Interval for Crude and Adjusted Rates
7.4 Confidence Interval for the Difference of Two Means
and Proportions
Difference of Two Independent Means
7.4.1 Difference of Two Dependent Means
7.4.2 Difference of Two Independent Proportions
7.4.3 Difference of Two Dependent Proportions
7.5 Confidence Interval and Sample Size
7.6 Confidence Interval for Other Measures
7.6.1
Confidence Interval for the Variance
7.6.2 Confidence Interval for Pearson Correlation Coefficient
7.7 Prediction and Tolerance Intervals
Based on the Normal Distribution
7.7.1 Prediction Interval
7.7.2 Tolerance Interval
Concluding Remarks
Exercises
References
8. TESTS OF HYPOTHESES
8.1 Preliminaries in Tests of Hyppotheses
8.1.1 Definitions of Terms Used in Hypothesis Testing
8.1.2
Determination of Decision Rule
8.1.3 Relationship of the Decision Rule, and
8.1.4 Conducting the Test
8.2 Testing Hypotheses
about the Mean
8.2.1 Known Variance
8.2.2 Unknown Varinace
8.3 Testing Hypotheses about the Proportion and Rates
8.4 Testing Hypotheses
about the Variance
8.5 Testing Hypotheses about the Pearson Correlation Coefficient
8.6 Testing Hypotheses about the Difference of
Two Means
8.6.1 Difference of Two Independent Means
8.6.2 Difference of Two Dependent Means
8.7 Testing Hypotheses about the Difference
of Two Proportions
8.7.1 Difference of Two Independent Proportions
8.7.2 Difference of Two Dependent Means
8.8 Tests of Hypotheses
and Sample Size
8.9 Statistical and Practical Significance
Concluding Remarks
Exercises
References
9. NONPARAMETRIC TESTS
9.1 Why Nonparametric Tests?
9.2 The Sign Test
9.3 The Wilcoxon Signed Rank Test
9.4 The Wilcoxon Rank Sum Test
9.5 The Kruskal-Wallis
Test
9.6 The Friedman Test
Concluding Remarks
Exercises
References
10. ANALYSIS OF CATEGORICAL DATA
10.1 Goodness-of-Fit Test
10.2 The 2 by 2 Contingency Table
10.2.1 Comparing Two Independent Binomial Proportions
10.2.2 Expected Cell Counts Assuming No
Association
10.2.3 The Odds Ratio – a Measure of Association
10.2.4 The Fisher's Exact Test
10.2.5 Analysis of Paired Data: The
McNemar Test
10.3 The r by c Contingency Table
10.3.1 Testing Hypothesis of Non Association: The Chi-Square Test
10.3.2 Testing
Hypothesis of No Trend
10.4 Multiple 2 by 2 Tables
10.4.1 Analyzing the Tables Separately
10.4.2 The Cochran-Mantel-Haenszel Test
104.3 The Mantel-Haenszel Common Odds Ratio
Concluding Remarks
Exercises
References
11. ANALYSIS OF SURVIVAL DATA
11.1 Data
Collection in Follow-Up Studies
11.2 The Life Table Method
11.3 The Product-Limit Method
11.4 Comparison of Two Survival Distributions
11.4.1 The Cochran-Mantel-Haenszel Test
11.4.2 The Log-Rank Test
Concluding Remarks
Exercises
References
12. ANALYSIS OF VARIANCE
12.1 Assumptions for the Use of the ANOVA
12.2 One-Way ANOVA
12.2.1 Sums of Squares and Mean Squares
12.2.2 The F Statistics
12.2.3 The ANOVA Table
12.3 Multiple Comparisons
12.3.1 Error Rates: Individual and Family
12.3.2 Tukey-Kramer Method
12.3.3
Fisher's Least Significant Difference Method
12.3.4 Dunnett's Method
12.4 Two-Way ANOVA for the Randomized Block Design with m Replicates
12.5 Two-Way ANOVA with Interaction
12.6 Linear Model Representation of the ANOVA
12.6.1 The Completely Randomized Design
12.6.2
The Randomized Block Design with m Replicates
12.6.3 Two-Way ANOVA with Interaction
12.7 ANOVA with Unequal Numbers of Observations
in Subgroups
Concluding Remarks
Exercises
References
13. LINEAR REGRESSION
13.1 Simple Linear Regression
13.1.1 Estimation
of Coefficients
13.1.2 The Variance of Y
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