Statistics for Veterinary and Animal Science

Aviva Petrie, Head of Biostatistics Unit and Senior Lecturer, UCL Eastman Dental Institute, London; Honorary Lecturer in Medical Statistics, Medical Statistics Unit, London School of Hygiene and Tropical Medicine, UK.  She is also author of a number of other books, including Medical Statistics at a Glance. Paul Watson is a distinguished and well respected scientist in the field of Reproductive Biology, and is Emeritus Professor at the Royal Veterinary College, UK.

“This book succeeds in this and represents an appropriate text for anyone wishing to understand or produce veterinary and animal science research.”  (Veterinary Record, 14 December 2013)“Any who carry out or interpret statistical results in a veterinary practice will find this a powerful read, packed with ideas presented in a non-mathematical method clear of jargon but pertinent to any daily practice.”  (Midwest Book Review, 1 September 2013)“This book offers a good introduction to the most important tests used in veterinary statistics. It provides readers with relevant information on appropriate tests, computer programs, and use of the data. All the data is current and a website provides updated information.”  (Doody’s, 6 September 2013)“It also seems likely that the third edition of the book will be positively received and meet with approval from those looking for an introduction to statistics for students taking degree programs in veterinary medicine and animal sciences. Although written primarily with students and practitioners of veterinary medicine in mind, Statistics for Veterinary and Animal Science also has much to offer students and researchers within other branches of the biological sciences.”  (Aquaculture International, 22 July 2013)

• Preface to third edition ixPreface to second edition xiPreface to first edition xiiiAbout the companion website xv1 The whys and wherefores of statistics 11.1 Learning objectives 11.2 Aims of the book 11.3 What is statistics? 21.4 Statistics in veterinary and animal science 31.5 Evidence-based veterinary medicine 41.6 Types of variable 41.7 Variations in measurements 51.8 Terms relating to measurement quality 71.9 Populations and samples 91.10 Types of statistical procedures 101.11 Conclusion 10Exercises 102 Descriptive statistics 122.1 Learning objectives 122.2 Summarizing data 122.3 Empirical frequency distributions 122.4 Tables 142.5 Diagrams 152.6 Numerical measures 192.7 Reference interval 24Exercises 253 Probability and probability distributions 283.1 Learning objectives 283.2 Probability 283.3 Probability distributions 303.4 Discrete probability distributions 313.5 Continuous probability distributions 333.6 Relationships between distributions 42Exercises 434 Sampling and sampling distributions 464.1 Learning objectives 464.2 Distinction between the sample and the population 464.3 Statistical inference 464.4 Sampling distribution of the mean 484.5 Confidence interval for a mean 504.6 Sampling distribution of the proportion 524.7 Confidence interval for a proportion 534.8 Bootstrapping and jackknifing 53Exercises 545 Experimental design and clinical trials 555.1 Learning objectives 555.2 Types of study 555.3 Introducing clinical trials 595.4 Importance of design in the clinical trial 605.5 Control group 615.6 Assignment of animals to the treatment groups 625.7 Avoidance of bias in the assessment procedure 655.8 Increasing the precision of the estimates 665.9 Further considerations 68Exercises 736 An introduction to hypothesis testing 756.1 Learning objectives 756.2 Introduction 756.3 Basic concepts of hypothesis testing 756.4 Type I and Type II errors 796.5 Distinction between statistical and biological significance 806.6 Confidence interval approach to hypothesis testing 816.7 Collecting our thoughts on confidence intervals 826.8 Equivalence and non-inferiority studies 82Exercises 837 Hypothesis tests 1 – the t-test: comparing one or two means 857.1 Learning objectives 857.2 Requirements for hypothesis tests for comparing means 857.3 One-sample t-test 877.4 Two-sample t-test 897.5 Paired t-test 92Exercises 968 Hypothesis tests 2 – the F-test: comparing two variances or more than two means 1008.1 Learning objectives 1008.2 Introduction 1008.3 The F-test for the equality of two variances 1008.4 Levene’s test for the equality of two or more variances 1028.5 Analysis of variance (ANOVA) for the equality of means 1028.6 One-way analysis of variance 105Exercises 1099 Hypothesis tests 3 – the Chi-squared test: comparing proportions 1129.1 Learning objectives 1129.2 Introduction 1129.3 Testing a hypothesis about a single proportion 1129.4 Comparing two proportions: independent groups 1139.5 Testing associations in an r × c contingency table 1179.6 Comparing two proportions: paired observations 1209.7 Chi-squared goodness-of-fit test 122Exercises 12310 Linear correlation and regression 12610.1 Learning objectives 12610.2 Introducing linear correlation and regression 12610.3 Linear correlation 12710.4 Simple (univariable) linear regression 13210.5 Regression to the mean 142Exercises 14211 Further regression analyses 14611.1 Learning objectives 14611.2 Introduction 14611.3 Multiple (multivariable) linear regression 14711.4 Multiple logistic regression: a binary response variable 15411.5 Poisson regression 15911.6 Regression methods for clustered data 161Exercises 16312 Non-parametric statistical methods 16512.1 Learning objectives 16512.2 Parametric and non-parametric tests 16512.3 Sign test 16712.4 Wilcoxon signed rank test 16912.5 Wilcoxon rank sum test 17112.6 Non-parametric analyses of variance 17312.7 Spearman’s rank correlation coefficient 175Exercises 17813 Further aspects of design and analysis 18113.1 Learning objectives 18113.2 Transformations 18113.3 Sample size 18413.4 Sequential and interim analysis 18913.5 Meta-analysis 19013.6 Methods of sampling 194Exercises 19814 Additional techniques 20014.1 Learning objectives 20014.2 Diagnostic tests 20014.3 Bayesian analysis 20814.4 Measuring agreement 21114.5 Measurements at successive points in time 21814.6 Survival analysis 22114.7 Multivariate analysis 226Exercises 22715 Some specialized issues and procedures 23015.1 Learning objectives 23015.2 Introduction 23015.3 Ethical and legal issues 23015.4 Spatial statistics and geospatial information systems 23315.5 Veterinary surveillance 23715.6 Molecular and quantitative genetics 240Exercises 24216 Evidence-based veterinary medicine 24316.1 Learning objectives 24316.2 Introduction 24316.3 What is evidence-based veterinary medicine? 24416.4 Why has evidence-based veterinary medicine developed? 24416.5 What is involved in practising evidence-based veterinary medicine? 24516.6 Integrating evidence-based veterinary medicine into clinical practice 24916.7 Example 249Exercises 25017 Reporting guidelines 25217.1 Learning objectives 25217.2 Introduction to reporting guidelines (EQUATOR network) 25217.3 REFLECT statement (livestock and food safety RCTs) 25417.4 ARRIVE guidelines (research using laboratory animals) 25517.5 STROBE statement (observational studies) 25517.6 STARD statement (diagnostic accuracy) 25617.7 PRISMA statement (systematic reviews and meta-analysis) 25618 Critical appraisal of reported studies 26918.1 Learning objectives 26918.2 Introduction 26918.3 A template for critical appraisal of published research involving animals 27018.4 Paper 1 27318.5 Critical appraisal of paper 1 28418.6 Paper 2 28818.7 Critical appraisal of paper 2 29718.8 General conclusion 302Solutions to exercises 303Appendices 323A Statistical tables 323B Tables of confidence intervals 339C Glossary of notation 341D Glossary of terms 345E Flowcharts for selection of appropriate tests 368References 371Index 379Colour plate section can be found facing page 240