Biostatistics for Medical and Biomedical Practitioners
- 1st Edition - September 8, 2015
- Author: Julien I. E. Hoffman
- Language: English
Biostatistics for Practitioners: An Interpretative Guide for Medicine and Biology deals with several aspects of statistics that are indispensable for researchers and students… Read more
Biostatistics for Practitioners: An Interpretative Guide for Medicine and Biology deals with several aspects of statistics that are indispensable for researchers and students across the biomedical sciences.
The book features a step-by-step approach, focusing on standard statistical tests, as well as discussions of the most common errors.
The book is based on the author’s 40+ years of teaching statistics to medical fellows and biomedical researchers across a wide range of fields.
- Discusses how to use the standard statistical tests in the biomedical field, as well as how to make statistical inferences (t test, ANOVA, regression etc.)
- Includes non-standards tests, including equivalence or non-inferiority testing, extreme value statistics, cross-over tests, and simple time series procedures such as the runs test and Cusums
- Introduces procedures such as multiple regression, Poisson regression, meta-analysis and resampling statistics, and provides references for further studies
professionals, graduate students and trainees across the biomedical sciences.
- About the Author
- Preface
- Acknowledgments
- Part 1. Basic Aspects of Statistics
- Chapter 1. Basic Concepts
- Introduction
- Basic Uses of Statistics
- Data
- General Approach to Study Design
- A Brief History of Statistics
- Chapter 2. Statistical Use and Misuse in Scientific Publications
- Early Use of Statistics
- Current Tests in Common Use
- Statistical Misuse
- Basic Guides to Statistics
- Chapter 3. Some Practical Aspects
- Statistics Programs
- Variables
- Measurement Scales
- Displaying Data Sets
- Accuracy of Measurement
- Notation
- Operators
- Weights
- Statistics Books
- Chapter 4. Exploratory Descriptive Analysis
- Basic Concepts
- Advanced and Alternative Concepts
- Appendix
- Chapter 5. Basic Probability
- Introduction
- Types of Probability
- Basic Principles and Definitions
- Conditional Probability
- Bayes' Theorem
- Chapter 1. Basic Concepts
- Part 2. Continuous Distributions
- Chapter 6. Normal Distribution
- Introduction
- Normal or Gaussian Curve
- Populations and Samples
- Description of the Distribution Shape
- Determining Normality
- Ungrouped Data
- How Important Is Normality?
- Chapter 7. Statistical Limits and the Central Limit Theorem
- Central Limit Theorem
- Tolerance Limits
- Reporting Results
- Chapter 8. Other Continuous Distributions
- Continuous Uniform Distribution
- Exponential Distribution
- Logarithmic Distribution
- Weibull Distribution
- Chi-Square Distribution
- Variance Ratio (F) Distribution
- Chapter 9. Outliers and Extreme Values
- Outliers
- Extreme Values
- Appendix
- Chapter 6. Normal Distribution
- Part 3. Hypothesis Testing
- Chapter 10. Hypothesis Testing
- Hypotheses
- Significance
- Chapter 11. Hypothesis Testing
- Basic Concepts
- Advanced and Alternative Concepts
- Chapter 10. Hypothesis Testing
- Part 4. Discrete and Categorical Distributions
- Chapter 12. Permutations and Combinations
- Permutations
- Combinations
- Chapter 13. Hypergeometric Distribution
- Introduction
- General Formula
- Fisher's Exact Test
- Multiple Groups
- Chapter 14. Categorical and Cross-Classified Data
- Basic Concepts
- Advanced Concepts
- Chapter 15. Categorical and Cross-Classified Data
- Paired Samples: McNemar's Test
- Testing Ordered Categorical Data: Kolmogorov–Smirnov Tests
- Concordance (Agreement) between Observers
- Intraclass Correlation
- Chapter 16. Binomial and Multinomial Distributions
- Basic Concepts
- Advanced or Alternative Concepts
- Appendix
- Chapter 17. Proportions
- Introduction
- Proportions and Binomial Theorem
- Confidence Limits
- Sample and Population Proportions
- Sample Size
- Comparing Proportions
- Pooling Samples
- Chapter 18. The Poisson Distribution
- Introduction
- Relationship to the Binomial Distribution
- Goodness of Fit to a Poisson Distribution
- The Ratio of the Variance to the Mean of a Poisson Distribution
- Setting Confidence Limits
- The Square Root Transformation
- Cumulative Poisson Probabilities
- Differences between Means of Poisson Distributions
- Determining the Required Sample Size
- Appendix
- Chapter 19. Negative Binomial Distribution
- Introduction
- Probability of r Successes
- Overdispersed Distribution
- Uses of the Negative Binomial
- Chapter 12. Permutations and Combinations
- Part 5. Probability in Epidemiology and Medical Diagnosis
- Chapter 20. Some Epidemiological Considerations
- Basic Concepts
- Advanced Concepts
- Chapter 21. Probability, Bayes' Theorem, Medical Diagnostic Evaluation, and Screening
- Bayes' Theorem Applied
- Sensitivity and Specificity
- Likelihood Ratios
- Cutting Points
- Receiver Operating Characteristic Curves
- Some Comments on Screening Tests
- Chapter 20. Some Epidemiological Considerations
- Part 6. Comparing Means
- Chapter 22. Comparison of Two Groups
- Basic Concepts
- Advanced Concepts
- Appendix
- Chapter 23. t-Test Variants
- Crossover Trials
- Equivalence and Noninferiority Testing
- Chapter 24. Multiple Comparisons
- Introduction
- Bonferroni Correction and Equivalent Tests
- Group Sequential Boundaries
- Sequential Analysis
- Adaptive Methods
- Chapter 25. Analysis of Variance I. One-Way
- Basic Concepts
- Advanced Concepts
- Chapter 26. Analysis of Variance II. More Complex Forms
- Basic Concepts
- Advanced and Alternative Concepts
- Appendix
- Chapter 22. Comparison of Two Groups
- Part 7. Regression and Correlation
- Chapter 27. Linear Regression
- Basic Concepts
- Advanced or Alternative Concepts
- Appendix
- Chapter 28. Variations Based on Linear Regression
- Transforming the Y Variate
- Inverse Prediction
- Line of Best Fit Passes through Zero
- Errors in the X Variate
- Break Points
- Resistant Lines
- Appendix
- Chapter 29. Correlation
- Basic Concepts
- Advanced and Alternative Concepts
- Appendix
- Chapter 30. Multiple Regression
- Basic Concepts
- Advanced Concepts and Examples
- Chapter 31. Serial Measurements
- Introduction
- Serial Correlation
- Control Charts
- Cumulative Sum Techniques (Cusums)
- Serial Measurements
- Chapter 32. Dose–Response Analysis
- General Principles
- Quantal Dose–Response Curves
- Chapter 33. Logistic Regression
- Introduction
- Single Explanatory Variable
- Multiple Explanatory Variables
- Appropriateness of Model
- Chapter 34. Poisson Regression
- Introduction
- Suitability of Poisson Regression
- Detecting Overdispersion
- Correcting for Overdispersion
- Chapter 27. Linear Regression
- Part 8. Miscellaneous Topics
- Chapter 35. Survival Analysis
- Basic Concepts
- Advanced Concepts
- Chapter 36. Meta-analysis
- Introduction
- Forest Graphs
- Funnel Plots
- Radial Plots
- L'Abbé Plots
- Criticisms of Meta-analysis
- Chapter 37. Resampling Statistics
- Introduction
- Bootstrap
- Permutations
- Jackknife
- Monte Carlo Methods
- Chapter 38. Study Design
- Sampling Problems
- Historical Controls
- Randomization
- Clinical Trials
- Placebo Effect
- Alternatives to Randomized Clinical Trials
- Chapter 35. Survival Analysis
- Part 9. End Texts
- Answers to Problems
- Glossary
- Index
- Edition: 1
- Published: September 8, 2015
- Language: English
JH
Julien I. E. Hoffman
Julien I E Hoffman, M.D., F.R.C.P (London) was born and educated in Salisbury (now Harare) in Southern Rhodesia (now Zimbabwe). He received a Bsc (Hons) in 1945 from the University of the Witwatersrand in South Africa, and his M.B., B.Ch. degree there in 1949. After working in the Departments of Medicine in Johannesburg General Hospital and in the Central Middlesex Hospital in London, he worked for the Medical Research Council at the Royal Postgraduate School in Hammersmith, London. Then he spent two years training in Pediatric Cardiogy at Boston Children’s Hospital, followed by 15 months as a Fellow at the Cardiovascular Research Institute (CVRI) at the University of California in San Francisco (UCSF).
In 1962 he joined the faculty of the Albert Einstein College of Medicine in New York, and moved in 1966 to UCSF as Associate Professor of Pediatrics and member of the CVRI. He spent 50% of his time in the care of children with heart disease and 50% of his time doing research into the pathophysiology of the coronary circulation.
His interest in Statistics began while taking his Science degree. In England, he took a short course run by Bradford Hill. On returning to Johannesburg he was assigned to statistical analyses for other members of the Department of Medicine. Learning was by trial and error, helped by Dr J Kerrich, head of the University’s Statistics Department. Hoffman began teaching statistics to Medical students in 1964, and in San Francisco conducted an approved course for Fellows and Residents for over 30 years. He was a member of the Biostatistics group for approving and coordinating statistics at UCSF. For many years he was a statistical consultant for the journal Circulation Research, and was intermittently statistical consultant to several other medical journals.
Affiliations and expertise
Professor of Pediatrics, Emeritus, Senior Member of the Cardiovascular Research Institute, University of California, San Francisco, CARead Biostatistics for Medical and Biomedical Practitioners on ScienceDirect