Basic Biostatistics For Medical And Biomedical Practitioners

Basic Biostatistics for Medical and Biomedical Practitioners, Second Edition makes it easier to plan experiments, with an emphasis on sample size. It also shows what choices are available when simple tests are unsuitable and offers investigators an overview of how the kinds of complex tests that they won't do on their own work. The second edition presents a new, revised and enhanced version of the chapters, taking into consideration new developments and tools available, discussing topics, such as the basic aspects of statistics, continuous distributions, hypothesis testing, discrete distributions, probability in epidemiology and medical diagnosis, comparing means, regression and correlation. This book is a valuable source for students and researchers looking to expand or refresh their understanding of statistics as it applies to the biomedical and research fields. Based on the author's 40+ years of teaching statistics to medical fellows and biomedical researchers across a wide range of fields, it is a valuable source for researchers who need to understand more about biostatistics to apply it to their work. Introduces procedures, such as multiple regression, Poisson distribution, binomial and multinomial distributions, variance analysis, and how to design and sample clinical trials Presents a new section on ANCOVA Gives references to free online tests Includes over 200 diagrams, enabling the reader to visualize the results Discusses NHST testing in detail, its disadvantages, and how to think about probability Cover BASIC BIOSTATISTICS FOR MEDICAL AND BIOMEDICAL PRACTITIONERS Copyright About the Author Preface References Acknowledgments Section I: Basic Aspects of Statistics 1 Basic Concepts Introduction Populations and samples The effects of variability Variables and parameters Basic Uses of Statistics Description Statistical inference Data Models General Approach to Study Design Replication and pseudoreplication A Brief History of Statistics References 2 Statistical Use and Misuse in Scientific Publications Early Use of Statistics Current-Tests in Common Use Statistical Misuse Basic Guides to Statistics References 3 Hypothesis Testing: Sample Size, Effect Size, Power, and Type II Errors Basic Concepts Statistical Power Effect Size Posthoc Power Analysis Appendix Calculation of Power References 4 Exploratory Descriptive Analysis Basic Concepts Counting Distribution A Sorting Experiment Histograms and Frequency Polygons Shapes of Distributions Transformations Stem and Leaf Diagrams Measures of Central Tendency (Location) Arithmetic Mean Median and Quantiles Mode The Geometric Mean Harmonic Mean Measures of Variability Range Standard Deviation Interquartile Distance Coefficient of Variation Tables and Graphs Tables Figures and Graphs Box Plots Advanced and Alternative Concepts Classical versus Robust Methods General Aspects Trimean Trimming and Winsorization Order Statistics Median Absolute Deviation Propagation of Errors Combining Experiments (Addition) Simple Multiplication (Scale Factor) Multiplying Means (Taylor, 1982) Dividing Means (Taylor, 1982) Three Useful Applications of the Variance Appendix Least Squares Principle References 5 Basic Probability Introduction Types of Probability Basic Principles and Definitions Additional definitions Other definitions Conditional Probability Bayes Theorem Worked Problems References Section II: Continuous Distributions 6 Normal Distribution Introduction Normal or Gaussian Curve The Quincunx Properties of the Normal Curve Populations and Samples Description of the Distribution Shape Skewness Kurtosis Determining Normality Ungrouped Data How Important Is Normality? References 7 Statistical Inference: Confidence Limits and the Central Limit Theorem Central Limit Theorem Generalizations Based on a Single Sample Setting confidence limits Tolerance and Prediction Limits Reporting Results Graphic Representation References 8 Other Continuous Distributions Continuous Uniform Distribution Exponential Distribution Logarithmic Distribution Chi-square Distribution Variance Ratio (F) Distribution References 9 Outliers and Extreme Values Outliers Grubb's test Slippage and contamination Robust tests Dealing with outliers Extreme Values References Section III: Hypothesis Testing 10 Hypothesis Testing: The Null Hypothesis, Significance and Type I Error Hypotheses Criticism of NHST Hypothesis Testing and Maximum Likelihood; the Bayes Factor Appendix Bayes Factor Terminology Type I Error and α References 11 Hypothesis Testing: Sample Size, Effect Size, Power, and Type II Errors Basic Concepts Statistical Power Effect Size Posthoc Power Analysis Appendix Calculation of Power References Section IV: Discrete Distributions 12 Permutations and Combinations; Logarithms Permutations Combinations Logarithms Exponents Logarithms Antilogarithms Worked Problems References 13 Hypergeometric Distribution Introduction General Formula Fisher's Exact Test (Fisher-Irwin Test) Multiple Groups Reference 14 Categorical and Cross-Classified Data: Goodness of Fit and Association Basic Concepts Introduction Goodness of Fit Continuity Correction Degrees of Freedom 2x2 Contingency Tables Practical Matters Odds Ratio Cautionary Tales Larger Contingency Tables Fisher's Exact Test Determining Power and Sample Size Advanced Concepts Cochran-Mantel-Haenszel (C-M-H) Test and Confounders Pooling Data Testing for Trends in Proportions (Cochran-Armitage Test) Problems With R x C Tables Multidimensional Tables References 15 Categorical and Cross-Classified Data: McNemar's and Bowker's Tests, Kolmogorov-Smirnov Tests, Concord Paired Samples: McNemars Test Bowker's Test Testing Ordered Categorical Data: Kolmogorov-Smirnov (K-S) Tests Kolmogoro-Smirnov One-Sample Test Kolmogorov-Smirnov Two-Sample Test Concordance (Agreement) Between Observers Two Categories More Than Two Categories Weighted Kappa References 16 Binomial and Multinomial Distributions Basic Concepts Introduction Bernoulli formula Binomial basics The normal approximation Fitting a binomial distribution to a set of trial results Cumulative binomial probabilities Confidence limits Continuity correction Comparing two binomial distributions Estimating sample size Comparing probabilities Advanced or Alternative Concepts Exact confidence limits Multinomial Distribution Appendix References 17 Proportions Introduction Proportions and Binomial Theorem Confidence Limits Sample and Population Proportions Sample Size Comparing Proportions Pooling samples References 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 Normal Approximation Exact Method The Square Root Transformation Cumulative Poisson Probabilities Differences Between Means of Poisson Distributions Comparison of Counts Based on Same Units Comparison of Counts Not Based on Same Units Comparing the Ratio of Two Poisson Variates Determining the Required Sample Size Appendix References 19 Negative Binomial Distribution Introduction Probability of r Successes Overdispersed Distribution Uses of the Negative Binomial References Section V: Probability in Epidemiology and Medical Diagnosis 20 Odds Ratio, Relative Risk, Attributable Risk, and Number Needed to Treat Basic Concepts Introduction Cohort study Noncohort study Sample size and power Attributable risk Population attributable risk Relative risks below 1; number needed to treat Advanced Concepts Confidence limits for attributable risk Differences between proportions Difference between proportional ratios (Proportional AR and Proportional PAR) Confidence limits for NNT References Further Reading 21 Probability, Bayes Theorem, Medical Diagnostic Evaluation, and Screening Bayes Theorem Applied Sensitivity and Specificity Cautionary Tales Practical Issues of Screening Tests Spectrum Bias or Effect Verification Bias Likelihood Ratios Cutting Points ROC Curves Some Comments on Screening Tests References Section VI: Comparing Means 22 Comparison of Two Groups: t-Tests and Nonparametric Tests Basic Concepts Introduction Paired t-Test Sample Size for Paired Test Unpaired t-Test Unequal Variances Sample Size for Unpaired Test Conclusion Nonparametric or Distribution-Free Tests The Wilcoxon Signed Rank Test The Sign Test The Mann-Whitney U-Test Requirements Advanced Concepts Comparing Two Coefficients of Variation The Paired t-Test Implies an Additive Model Confidence Limits for Medians Ranking Transforms The Meaning of the Mann-Whitney Test Problems References 23 t-Test Variants: Cross-Over Tests, Equivalence Tests Cross-Over Trials N of 1 Trials Equivalence and Noninferiority Testing References 24 Multiple Comparisons Introduction Bonferroni Correction and Equivalent Tests Error Rates Extreme Multiplicity and False Discovery Rates Group Sequential Boundaries Sequential Analysis Adaptive Methods References 25 Analysis of Variance. I. One-Way Basic Concepts Basic Test Requirements for Test Homogeneity of Variance Kruskal-Wallis Test Independence of Observations Effect Size Sample Size Advanced Concepts Multiple Comparisons Studentized Range Test Single Step Tests Multiple Step Tests Recommendations Linear Combinations Planned Experiments References 26 Analysis of Variance. II. More Complex Forms Basic Concepts Introduction Two-Way ANOVA Additivity Multiple Factors Friedman Test Cochrane's Q-Test Interaction Advanced and Alternative Concepts Missing Data or Unequal Cell Sizes Nested Designs Transformations Model II ANOVA More About Repeated Measures Designs Bibliography Section VII: Regression and Correlation 27 Linear Regression Basic Concepts Introduction Exploratory Data Analysis Transforming Curves Model I Linear Regression Basics Additional Calculations Residuals Confidence Limits Comparison Methods (Basic) Advanced or Alternative Concepts Heteroscedasticity The Comparison Problem (Advanced) Comparing Two or More Lines Outliers Leverage Influence Ratio Measurements and Scaling Factors References 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 Derivation of Formulas for a Straight Line Passing Through Zero References 29 Correlation Basic Concepts Introduction P Values and Confidence Limits Sample Size and Power Ordinal Numbers Spearman's Test Kendall's Tau Test Advanced and Alternative Concepts Partial Correlation Spurious Correlation Ratios and Scaling Factors Intraclass Correlation Reliability Rater Agreement Appendix Related Expressions References 30 Multiple Regression Basic Concepts Introduction Multiple Linear Regression With Two X Variables Multiple Collinearity Prerequisites for Multiple Regression Determining the ``Best ́ ́ Regression Which Is the Best Regression? Nonlinear Regression: Polynomial Regression Conclusions About Regression Methods in General Advanced Concepts and Examples Multiple Regression With Many Independent X Variates Analysis of Cystic Fibrosis Example Detecting Multicollinearity Determining the Correct Model All Subsets Regression Forward Selection Backward Elimination Nonlinear Regression: Polynomial Regression Splines Correcting for Multicollinearity Principles of Nonlinear Regression Dummy or Indicator Variables Multivariate Analysis Longitudinal Regression References 31 Serial Measurements: Time Series, Control Charts, Cusums Introduction Serial Correlation Wald-Wolfowitz Runs Test Up-and-Down Runs Test Rank von Neumann Ratio Ratio Measurements Control Charts Cumulative Sum Techniques Serial Measurements References 32 Dose-Response Analysis General Principles Quantal Dose-Response Curves References 33 Logistic Regression Introduction Single Explanatory Variable Multiple Explanatory Variables Appropriateness of Model References 34 Poisson Regression Introduction Suitability of Poisson Regression Detecting Overdispersion Correcting for Overdispersion References Section VIII: Miscellaneous Topics 35 Survival Analysis Basic Concepts Introduction Basic Method Confidence Limits Comparison of Different Survival Curves Sample Size Advanced Concepts Calculating the Log-Rank Test The Hazard Function Hazard Ratio Cox Proportional Hazards Regression Competing Risks Analysis References 36 Meta-analysis Introduction Forest Graphs Funnel plots Radial Plots LAbbé Plots Appendix References 37 Resampling Statistics Introduction Bootstrap Permutations (or Randomization) Test Jackknife Monte Carlo Methods References 38 Design: Sampling, Clinical Trials Sampling Problems Historical Controls Randomization Clinical Trials Placebo Effect Alternatives to Randomized Clinical Trials Propensity Analysis References Answers to Problems Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 11 Chapter 12 Chapter 13 Chapter 14 Chapter 15 Chapter 16 Chapter 17 Chapter 18 Chapter 19 Chapter 20 Chapter 22 Chapter 25 Chapter 26 Chapter 27 Chapter 29 Chapter 31 Chapter 35 Glossary Index A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Back Cover Basic Biostatistics For Medical And Biomedical Practitioners, Second Edition, Makes It Easier To Plan Experiments, With Emphasis On Sample Size. It Also Shows What Choices Are Available When Simple Tests Are Unsuitable, And Offers Investigators An Overview Of Complex Tests That They Will Not Do On Their Own, But Need To Know How They Work. The Second Edition Presents A New Revised And Enhanced Version Of The Chapters, Taking Into Consideration New Developments And Tools Available Recently, And Discusses Topics Such As Basic Aspects Of Statistics, Continuous Distributions, Hypothesis Testing, Discrete Distributions, Probability In Epidemiology And Medical Diagnosis, Comparing Means, Regression And Correlation. 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, And It Is A Valuable Source For Researchers Who Need To Understand More About Biostatistics To Apply To Their Work. Basic Biostatistics For Medical And Biomedical Practitioners, Second Edition Is A Valuable Source For Students And Researchers Looking To Expand Or Refresh The Understanding Of Statistics As It Applies To The Biomedical And Research Fields. Introduces Procedures Such As Multiple Regression, Poisson Distribution, Binomial And Multinomial Distributions, Variance Analysis, As Well As Designing And Sampling Clinical Trials Presents A New Section On Ancova Gives References To Free Online Tests Each Statistical Inferential Test Is Followed Immediately By A Problem, To Give Readers, The Opportunity To Perform The Test And Interpret It Includes Over 200 Diagrams, Which Enables The Reader To Visualize The Results Discusses Nhst Testing In Detail, Its Disadvantages And How To Think About Probability; The Unsafety Of Accepting P 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 CusumsIntroduces procedures such as multiple regression, Poisson regression, meta-analysis and resampling statistics, and provides references for further studies