Correctly understanding and using medical statistics is a key skill for all medical students and health professionals. In an informal and friendly style, Medical Statistics from Scratch provides a practical foundation for everyone whose first interest is probably not medical statistics. Keeping the level of mathematics to a minimum, it clearly illustrates statistical concepts and practice with numerous real world examples and cases drawn from current medical literature. This fully revised and updated third edition includes new material on: * missing data, random allocation and concealment of data * intra-class correlation coefficient * effect modification and interaction * diagnostic testing and the ROC curve * standardisation Medical Statistics from Scratch is an ideal learning partner for all medical students and health professionals needing an accessible introduction, or a friendly refresher, to the fundamentals of medical statistics.
Preface to the 3rd Edition xv Preface to the 2nd Edition xvii Preface to the 1st Edition xix Introduction xxi I Some Fundamental Stuff 1 1 First things first the nature of data 3 II Descriptive Statistics 15 2 Describing data with tables 17 3 Every picture tells a story describing data with charts 31 4 Describing data from its shape 49 5 Numbers R us 59 6 Measures of spread 72 III The Confounding Problem 87 7 Confounding like the poor, (nearly) always with us 89 IV Design and Data 97 8 Research design Part I: Observational study designs 99 9 Research design Part II: Getting stuck in experimental studies 118 10 Getting the participants for your study: ways of sampling 127 V Chance Would be a Fine Thing 135 11 The idea of probability 137 12 Risk and odds 145 VI The Informed Guess An Introduction to Confidence Intervals 161 13 Estimating the value of a single population parameter the idea of confidence intervals 163 14 Using confidence intervals to compare two population parameters 175 15 Confidence intervals for the ratio of two population parameters 191 VII Putting it to the Test 201 16 Testing hypotheses about the difference between two population parameters 203 17 The chi-squared (2) test what, why, and how? 226 18 Testing hypotheses about the ratio of two population parameters 239 VIII Becoming Acquainted 245 19 Measuring the association between two variables 247 20 Measuring agreement 260 IX Getting into a Relationship 267 21 Straight line models: linear regression 269 22 Curvy models: Logistic regression 294 X Three More Chapters 309 23 Measuring survival 311 24 Systematic review and meta-analysis 325 25 Diagnostic testing 336 Appendix: Table of random numbers 342 References 343 Solutions to Exercises 353 Index 381