Synopses & Reviews
FUNDAMENTALS OF BIOSTATISTICS leads you through the methods, techniques, and computations necessary for success in the medical field. Every new concept is developed systematically through completely worked out examples from current medical research problems.
About the Author
Bernard Rosner is 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.
Table of Contents
1. GENERAL OVERVIEW. References. 2. DESCRIPTIVE STATISTICS. Introduction. Measures of Location. Some Properties of the Arithmetic Mean. Measures of Spread. Some Properties of the Variance and Standard Deviation. The Coefficient of Variation. Grouped Data. Graphic Methods. Case Study 1: Effects of Lead Exposure on Neurological and Psychological Function in Children. Obtaining Descriptive Statistics on the Computer. Summary. 3. PROBABILITY. Introduction. Definition of Probability. Some Useful Probabilistic Notation. The Multiplication Law of Probability. The Addition Law of Probability. Conditional Probability. Bayes' Rule and Screening Tests. Bayesian Inference. ROC Curves. Prevalence and Incidence. Summary. 4. DISCRETE PROBABILITY DISTRIBUTIONS. Introduction. Random Variables. The Probability Mass Function for a Discrete Random Variable. The Expected Value of a Discrete Random Variable. The Variance of a Discrete Random Variable. The Cumulative-Distribution Function of a Discrete Random Variable. Permutations and Combinations. Expected Value and Variance of the Binomial Distribution. The Poisson Distribution. Computation of Poisson Probabilities. Expected Value and Variance of the Poisson Distribution. Poisson Approximation to the Binomial Distribution. Summary. 5. CONTINUOUS PROBABILITY DISTRIBUTIONS. Introduction. General Concepts. The Normal Distribution. Properties of the Standard Normal Distribution. Conversion from an N([mean], [variance]) Distribution to an N(0, 1) Distribution. Normal Approximation to the Binomial Distribution. Normal Approximation to the Poisson Distribution. Summary. 6. ESTIMATION. Introduction. The Relationship between Population and Sample. Random-Number Tables. Randomized Clinical Trials. Estimation of the Mean of a Distribution. Case Study: Relationship of Cigarette Smoking to Bone Mineral Density (BMD) Among Middle-Aged Women. Estimation of the Variance of a Distribution. Estimation for the Binomial Distribution. Estimation for the Poisson Distribution. One-Sided Confidence Intervals. Summary. 7. HYPOTHESIS TESTING: ONE-SAMPLE INFERENCE. Introduction. General Concepts. One-Sample Test for the Mean of a Normal Distribution: One-Sided Alternatives. One-Sample Test for the Mean of a Normal Distribution: Two-Sided Alternatives. The Power of a Test. Sample-Size Determination. The Relationship between Hypothesis Testing and Confidence Intervals. Bayesian Inference. One-Sample Chi-Squared Test for the Variance of a Normal Distribution. One-Sample Test for a Binomial Proportion. One-Sample Inference for the Poisson Distribution. Case Study: Effects of Tobacco Use on Bone-Mineral Density in Middle-Aged Women. Summary. 8. HYPOTHESIS TESTING: TWO-SAMPLE INFERENCE. Introduction. The Paired t Test. Interval Estimation for the Comparison of Means from Two Paired Samples. Two-Sample t Test for Independent Samples with Equal Variances. Interval Estimation for the Comparison of Means from Two Independent Samples (Equal Variance Case). Testing for the Equality of Two Variances. Two-Sample t Test for Independent Samples with Unequal Variances. Case Study: Effects of Lead Exposure on Neurological and Psychological Function in Children. The Treatment of Outliers. Estimation of Sample Size and Power for Comparing Two Means. Summary. 9. NONPARAMETRIC METHODS. Introduction. The Sign Test. The Wilcoxon Signed-Rank Test. The Wilcoxon Rank-Sum Test. Case Study: Effects of Lead Exposure on Neurological and Psychological Function in Children. Summary. 10. HYPOTHESIS TESTING: CATEGORICAL DATA. Introduction. Two-Sample Test for Binomial Proportions. Fisher's Exact Test. Two-Sample Test for Binomial Proportions for Matched-Pair Data (McNemar's Test). Estimation of Sample Size and Power for Comparing Two Binomial Proportions. R x C Contingency Tables. Chi-Square Goodness-of-Fit Test. The Kappa Statistic. Summary. 11. REGRESSION AND CORRELATION METHODS. Introduction. General Concepts. Fitting Regression Lines-The Method of Least Squares. Inferences About Parameters from Regression Lines. Interval Estimation for Linear Regression. Assessing the Goodness-of-Fit of Regression Lines. Statistical Inference for Correlation Coefficients. Multiple Regression. Case Study: Effects of Lead Exposure on Neurological and Psychological Function in Children. Partial and Multiple Correlation. Rank Correlation. Summary. 12. MULTISAMPLE INFERENCE. Introduction to the One-Way Analysis of Variance. One-Way Analysis of Variance--Fixed-Effects Model. Hypothesis Testing in One-Way ANOVA--Fixed-Effects Model. Comparisons of Specific Groups in One-Way ANOVA. Two-Way Analysis of Variance. The Kruskal-Wallis Test. One-Way ANOVA--Random-Effects Model. The Intraclass Correlation Coefficient. Summary. 13. DESIGN AND ANALYSIS TECHNIQUES FOR EPIDEMIOLOGIC STUDIES. Introduction. Study Design. Confounding and Standardization. Methods of Inference for Stratified Categoric Data--The Mantel-Haenszel Test. Power and Sample-Size Estimations for Stratified Categorical Data. Multiple Logistic Regression. The Cross-Over Design. Missing Data. Summary. 14. HYPOTHESIS TESTING: PERSON-TIME DATA. Measure of Effect for Person-Time Data. Two-Sample Inference for Incidence-Rate Data. Inference for Stratified Person-Time Data. Testing for Trend: Incidence-Rate Data. Introduction to Survival Analysis. Estimation of Survival Curves: The Kaplan-Meier Estimator. The Log-Rank Test. The Proportional-Hazards Model. Summary. Appendix: Tables. Answers to Selected Problems. Flowchart: Methods of Statistical Inference. Index of Data Sets. Index.