Statistics at Square Two: Understanding Modern Statistical Applications in Medicine

✍ Scribed by Michael J. Campbell

• Publisher: BMJ Books
• Year: 2006
• Tongue: English
• Leaves: 147
• Edition: 2
• Category: Library

✦ Synopsis

Updated companion volume to the ever popular Statistics at Square One (SS1) Statistics at Square Two, Second Edition, helps you evaluate the many statistical methods in current use. Going beyond the basics of SS1, it covers sophisticated methods and highlights misunderstandings. Easy to read, it includes annotated computer outputs and keeps formulas to a minimum. Worked examples of methods such as multiple and logical regression reinforce the text. Each chapter concludes with exercises to stimulate learning.All those who need to understand statistics in clinical research papers and apply them in their own research will value this compact and coherent guide.

✦ Table of Contents

Statistics at Square Two: Understanding modern statistical applications in medicine......Page 6
Contents......Page 8
Preface......Page 11
1.1 Basics......Page 14
1.2 Models......Page 15
1.3 Types of data......Page 16
1.4 Significance tests......Page 17
1.5 Confidence intervals......Page 18
1.7 Model fitting and analysis: confirmatory and exploratory analyses......Page 19
1.10 Missing values......Page 20
1.11 Reporting statistical results in the literature......Page 21
1.12 Reading statistics in the literature......Page 22
2.1 The model......Page 23
2.3 Two independent variables......Page 24
2.4 Interpreting a computer output......Page 29
2.5 Multiple regression in action......Page 35
2.6 Assumptions underlying the models......Page 36
2.7 Model sensitivity......Page 38
2.8 Stepwise regression......Page 40
2.10 Reading the results of a multiple regression......Page 41
3.1 The model......Page 45
3.2 Uses of logistic regression......Page 47
3.3 Interpreting a computer output: grouped analysis......Page 48
3.4 Logistic regression in action......Page 53
3.5 Model checking......Page 54
3.6 Interpreting computer output: ungrouped analysis......Page 56
3.8 Interpreting computer output: unmatched case–control study......Page 58
3.10 Interpreting computer output: matched case–control study......Page 60
3.11 Conditional logistic regression in action......Page 62
3.13 Reading about logistic regression......Page 63
4.1 Introduction......Page 66
4.2 The model......Page 67
4.4 Interpreting a computer output......Page 69
4.5 Survival analysis in action......Page 71
4.7 Generalisations of the model......Page 72
4.8 Model checking......Page 73
4.9 Reporting the results of a survival analysis......Page 75
4.10 Reading about the results of a survival analysis......Page 76
5.1 Introduction......Page 80
5.2 Models for random effects......Page 81
5.4 Use of random effects models......Page 82
5.5 Random effects models in action......Page 85
5.6 Ordinary least squares at the group level......Page 87
5.7 Computer analysis......Page 89
5.8 Model checking......Page 93
5.10 Reading about the results of random effects analysis......Page 94
6.1 Poisson regression......Page 97
6.2 Ordinal regression......Page 102
6.3 Time series regression......Page 105
6.4 Reporting Poisson, ordinal or time series regression in the literature......Page 108
6.5 Reading about the results of Poisson, ordinal or time series regression in the literature......Page 109
A1.1 Logarithms......Page 111
A2.1 Binomial models and likelihood......Page 113
A2.3 Normal model......Page 115
A2.4 Hypothesis testing: LR test......Page 117
A2.5 Wald test......Page 118
A2.7 Which method to choose?......Page 119
A2.8 Confidence intervals......Page 120
Appendix 3: Bootstrapping and variance robust standard errors......Page 122
A3.1 Computer analysis......Page 124
A3.3 Robust or sandwich estimate SE......Page 126
A3.4 Reporting the bootstrap and robust SEs in the literature......Page 128
Appendix 4: Bayesian methods......Page 130
A4.1 Reporting Bayesian methods in the literature......Page 131
Answers to exercises......Page 132
Glossary......Page 136
Index......Page 142