Medical Statistics from Scratch: An Introduction for Health Professionals, 3rd Edition
October 2014, ©2014
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
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
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
Solutions to Exercises 353
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