Biostatistics and epidemiology : a primer for health and biomedical professionals : a primer for health and biomedical professionals

ContentsPreface To The Third Edition Acknowledgments Chapter 1. The Scientific Method 1.1 The Logic of Scientific Reasoning 1.2 Variability of Phenomena Requires Statistical Analysis 1.3 Inductive Inference: Statistics as the Technology of the Scientific Method 1.4 Design of Studies 1.5 How to Quantify Variables 1.6 The Null Hypothesis 1.7 Why Do We Test the Null Hypothesis? 1.8 Types of Errors 1.9 Significance Level and Types of Error 1.10 Consequences of Type I and Type II Errors Chapter 2. A Little Bit Of Probability 2.1 What Is Probability? 2.2 Combining Probabilities 2.3 Conditional Probability 2.4 Bayesian Probability 2.5 Odds and Probability 2.6 Likelihood Ratio 2.7 Summary of Probability Chapter 3. Mostly About Statistics 3.1 Chi-Square for 2 x 2 Tables 3.2 McNemar Test 3.3 Kappa 3.4 Description of a Population: Use of the Standard Deviation 3.5 Meaning of the Standard Deviation: The Normal Distribution 3.6 The Difference Between Standard Deviation and Standard Error 3.7 Standard Error of the Difference Between Two Means 3.8 Z Scores and the Standardized Normal Distribution 3.9 The t Statistic 3.10 Sample Values and Population Values Revisited 3.11 A Question of Confidence 3.12 Confidence Limits and Confidence Intervals 3.13 Degrees of Freedom 3.14 Confidence Intervals for Proportions 3.15 Confidence Intervals Around the Difference Between Two Means 3.16 Comparisons Between Two Groups 3.17 Z-Test for Comparing Two Proportions 3.18 t-Test for the Difference Between Means of Two Independent Groups: Principles 3.19 How to Do a t-Test: An Example 3.20 Matched Pair t-Test 3.21 When Not to Do a Lot of t-Tests: The Problem of Multiple Tests of Significance 3.22 Analysis of Variance: Comparison Among Several Groups 3.23 Principles 3.24 Bonferroni Procedure: An Approach to Making Multiple Comparisons 3.25 Analysis of Variance When There Are Two Independent Variables: The Two-Factor ANOVA 3.26 Interaction Between Two Independent Variables 3.27 Example of a Two-Way ANOVA 3.28 Kruskal-Wallis Test to Compare Several Groups 3.29 Association and Causation: The Correlation Coefficient 3.30 How High Is High?3.31 Causal Pathways 3.32 Regression 3.33 The Connection Between Linear Regression and the Correlation Coefficient 3.34 Multiple Linear Regression 3.35 Summary So Far Chapter 4. Mostly About Epidemiology 4.1 The Uses of Epidemiology 4.2 Some Epidemiologic Concepts: Mortality Rates 4.3 Age-Adjusted Rates 4.4 Incidence and Prevalence Rates 4.5 Standardized Mortality Ratio 4.6 Person-Years of Observation 4.7 Dependent and Independent Variables 4.8 Types of Studies 4.9 Cross-Sectional Versus Longitudinal Looks at Data 4.10 Measures of Relative Risk: Inferences From Prospective Studies: the Framingham Study 4.11 Calculation of Relative Risk from Prospective Studies 4.12 Odds Ratio: Estimate of Relative Risk from Case-Control Studies 4.13 Attributable Risk 4.14 Response Bias 4.15 Confounding Variables 4.16 Matching 4.17 Multiple Logistic Regression 4.18 Confounding By Indication 4.19 Survival Analysis: Life Table Methods 4.20 Cox Proportional Hazards Model 4.21 Selecting Variables For Multivariate Models 4.22 Interactions: Additive and Multiplicative Models Summary: Chapter 5. Mostly About Screening 5.1 Sensitivity, Specificity, and Related Concepts 5.2 Cutoff Point and Its Effects on Sensitivity and Specificity Chapter 6. Mostly About Clinical Trials 6.1 Features of Randomized Clinical Trials 6.2 Purposes of Randomization 6.3 How to Perform Randomized Assignment 6.4 Two-Tailed Tests Versus One-Tailed Test 6.5 Clinical Trial as "e;Gold Standard"e; 6.6 Regression Toward the Mean 6.7 Intention-to-Treat Analysis 6.8 How Large Should the Clinical Trial Be? 6.9 What Is Involved in Sample Size Calculation? 6.10 How to Calculate Sample Size ...