Statistics at Square Two: Understanding Modern Statistical Applications in Medicine

Updated companion volume to the ever popular Statistics at Square One (SS1)
Statistical methods used in medical literature are increasingly complex but software available to apply these methods is becoming easier to use. The result is that consumers of the literature now have to grasp sophisticated methods which may be inappropriately applied.
This book covers the sophisticated statistical methods and goes beyond the basics of SS1. It reviews the most commonly used modern statistical methods and highlights common misunderstandings. It is easy to read, with annotated computer outputs and a minimum of formulas.
It provides worked examples of methods such as multiple and logistical regression and each chapter concludes with exercises to reinforce learning.
This is for all those who need to understand statistics in clinical research papers and apply them in their own research, such as junior doctors and general practitioners.

Spis treści:
1 Models, tests and data.
2 Multiple linear regression.
3 Logistic regression.
4 Survival analysis.
5 Random effects models.
6 Other models.
.
Appendix 1: Exponentials and logarithms.
Appendix 2: Maximum likelihood and significance tests.
Appendix 3: Bootstrapping and variance robust standard errors.
Appendix 4: Bayesian methods.
Answers to exercises.
Glossary.
Index

Nota biograficzna:
M J Campbell, Professor of Medical Statistics, University of Sheffield, UK

Okładka tylna:
Updated companion volume to the ever popular Statistics at Square One (SS1)
Statistical methods used in medical literature are increasingly complex but software available to apply these methods is becoming easier to use. The result is that consumers of the literature now have to grasp sophisticated methods which may be inappropriately applied.
This book covers the sophisticated statistical methods and goes beyond the basics of SS 1. It reviews the most commonly used modern statistical methods and highlights common misunderstandings. It is easy to read, with annotated computer outputs and a minimum of formulas.
It provides worked examples of methods such as multiple and logistical regression and each chapter concludes with exercises to reinforce learning.
This is for all those who need to understand statistics in clinical research papers and apply them in their own research, such as junior doctors and general practitioners.