Applied Biostatistics for the Health Sciences

Abstract

Preface. Chapter 1 Introduction to Biostatistics. 1.1 What is Biostatistics? 1.2 Populations, Samples, and Statistics. 1.3 Clinical Trials. 1.4 Data Set Descriptions. Glossary. Exercises. Chapter 2 Describing Populations. 2.1 Populations and Variables. 2.2 Population Distributions and Parameters. 2.3 Probability. 2.4 Probability Models. Glossary. Exercises. Chapter 3 Random Sampling. 3.1 Obtaining Representative Data. 3.2 Commonly Used Sampling Plans. 3.3 Determining the Sample Size. Glossary. Exercises. Chapter 4 Summarizing Random Samples. 4.1 Samples and Inferential Statistics. 4.2 Inferential Graphical Statistics. 4.3 Numerical Statistics for Univariate Datasets. 4.4 Statistics for Multivariate Data Sets. Glossary. Exercises. Chapter 5 Measuring the Reliability of Statistics. 5.1 Sampling Distributions. 5.2 The Sampling Distribution of a Sample Proportion. 5.3 The Sampling Distribution of x . 5.4 Comparisons Based on Two Samples. 5.5 Bootstrapping the Sampling Distribution of a Statistic. Glossary. Exercises. Chapter 6 Confidence Intervals. 6.1 Interval Estimation. 6.2 Confidence Intervals. 6.3 Single Sample Confidence Intervals. 6.4 Bootstrap Confidence Intervals. 6.5 Two Sample Comparative Confidence Intervals. Glossary. Exercises. Chapter 7 Testing Statistical Hypotheses. 7.1 Hypothesis Testing. 7.2 Testing Hypotheses about Proportions. 7.3 Testing Hypotheses about Means. 7.4 Some Final Comments on Hypothesis Testing. Glossary. Exercises. Chapter 8 Simple Linear Regression. 8.1 Bivariate Data, Scatterplots, and Correlation. 8.2 The Simple Linear Regression Model. 8.3 Fitting a Simple Linear Regression Model. 8.4 Assessing the Assumptions and Fit of a Simple Linear Regression Model. 8.5 Statistical Inferences based on a Fitted Model. 8.6 Inferences about the Response Variable. 8.7 Some Final Comments on Simple Linear Regression. Glossary. Exercises. Chapter 9 Multiple Regression. 9.1 Investigating Multivariate Relationships. 9.2 The Multiple Linear Regression Model. 9.3 Fitting a Multiple Linear Regression Model. 9.4 Assessing the Assumptions of a Multiple Linear Regression Model. 9.5 Assessing the Adequacy of Fit of a Multiple Regression Model. 9.6 Statistical Inferences Based Multiple Regression Model. 9.7 Comparing Multiple Regression Models. 9.8 Multiple Regression Models with Categorical Variables. 9.9 Variable Selection Techniques. 9.10 Some Final Comments on Multiple Regression. Glossary. Exercises. Chapter 10 Logistic Regression. 10.1 Odds and Odds Ratios. 10.2 The Logistic Regression Model. 10.3 Fitting a Logistic Regression Model. 10.4 Assessing the Fit of a Logistic Regression Model. 10.5 Statistical Inferences Based on a Logistic Regression Model. 10.6 Variable Selection. 10.7 Some Final Comments on Logistic Regression. Glossary. Exercises. Chapter 11 Design of Experiments. 11.1 Experiments versus Observational Studies. 11.2 The Basic Principles of Experimental Design. 11.3 Experimental Designs. 11.4 Factorial Experiments. 11.5 Models for Designed Experiments. 11.6 Some Final Comments of Designed Experiments. Glossary. Exercises. Chapter 12 Analysis of Variance. 12.1 Single-Factor Analysis of Variance. 12.2 Randomized Block Analysis of Variance. 12.3 Multifactor Analysis of Variance. 12.4 Selecting the Number of Replicates in Analysis of Variance. 12.5 Some Final Comments on Analysis of Variance. Glossary. Exercises. Chapter 13 Survival Analysis. 13.1 The Kaplan Meier Estimate of the Survival Function. 13.2 The Proportional Hazards Model. 13.3 Logistic Regression and Survival Analysis. 13.4 Some Final Comments on Survival Analysis. Glossary. Exercises. References. Appendix A. Problem Solutions. Index.