Medical Statistics from Scratch: An Introduction for Health Professionals / Edition 3

by David Bowers

Overview

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.

Product Details

ISBN-13:	9781118519387
Publisher:	Wiley
Publication date:	10/06/2014
Edition description:	Older Edition
Pages:	408
Product dimensions:	6.70(w) x 9.50(h) x 0.80(d)

About the Author

DAVID BOWERS, Leeds Institute of Health Sciences, School of Medicine, University of Leeds, Leeds, UK

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Table of Contents

Preface to the 2nd Edition     xi
Preface to the 1st Edition     xiii
Introduction     xv
Some Fundamental Stuff     1
First things first - the nature of data     3
Learning Objectives     3
Variables and data     3
The good, the bad, and the ugly - types of variable     4
Categorical variables     4
Metric variables     7
How can I tell what type of variable I am dealing with?     9
Descriptive Statistics     15
Describing data with tables     17
Learning Objectives     17
What is descriptive statistics?     17
The frequency table     18
Describing data with charts     29
Learning Objectives     29
Picture it!     29
Charting nominal and ordinal data     30
Charting discrete metric data     34
Charting continuous metric data     35
Charting cumulative data     37
Describing data from its shape     43
Learning Objectives     43
The shape of things to come     43
Describing data with numeric summary values     51
Learning Objectives     51
Numbers R us     52
Summary measures of location     54
Summary measures of spread     57
Standard deviation and the Normal distribution     65
Getting the Data     69
Doing it right first time - designing a study     71
Learning Objectives     71
Hey ho! Hey ho! It's off to work we go     72
Collecting the data - types of sample     74
Types of study     75
Confounding     81
Matching     81
Comparing cohort and case-control designs     83
Getting stuck in - experimental studies     83
From Little to Large - Statistical Inference     91
From samples to populations - making inferences     93
Learning Objectives     93
Statistical inference     93
Probability, risk and odds     97
Learning Objectives     97
Chance would be a fine thing - the idea of probability     98
Calculating probability     99
Probability and the Normal distribution     100
Risk     100
Odds     101
Why you can't calculate risk in a case-control study     102
The link between probability and odds     103
The risk ratio     104
The odds ratio     105
Number needed to treat (NNT)     106
The Informed Guess - Confidence Interval Estimation     109
Estimating the value of a single population parameter - the idea of confidence intervals     111
Learning Objectives     111
Confidence interval estimation for a population mean     112
Confidence interval for a population proportion     116
Estimating a confidence interval for the median of a single population     117
Estimating the difference between two population parameters     119
Learning Objectives     119
What's the difference?     120
Estimating the difference between the means of two independent populations - using a method based on the two-sample t test     120
Estimating the difference between two matched population means - using a method based on the matched-pairs t test     125
Estimating the difference between two independent population proportions     126
Estimating the difference between two independent population medians - the Mann-Whitney rank-sums method     127
Estimating the difference between two matched population medians - Wilcoxon signed-ranks method     131
Estimating the ratio of two population parameters      133
Learning Objectives     133
Estimating ratios of means, risks and odds     133
Putting it to the Test     139
Testing hypotheses about the difference between two population parameters     141
Learning Objectives     141
The research question and the hypothesis test     142
A brief summary of a few of the commonest tests     144
Some examples of hypothesis tests from practice     146
Confidence intervals versus hypothesis testing     149
Nobody's perfect - types of error     149
The power of a test     151
Maximising power - calculating sample size     152
Rules of thumb     152
Testing hypotheses about the ratio of two population parameters     155
Learning Objectives     155
Testing the risk ratio     155
Testing the odds ratio     158
Testing hypotheses about the equality of population proportions: the chi-squared test     161
Learning Objectives     161
Of all the tests in all the world...the chi-squared (x[superscript 2]) test     162
Getting up Close     169
Measuring the association between two variables     171
Learning Objectives      171
Association     171
The correlation coefficient     175
Measuring agreement     181
Learning Objectives     181
To agree or not agree: that is the question     181
Cohen's kappa     182
Measuring agreement with ordinal data - weighted kappa     184
Measuring the agreement between two metric continuous variables     184
Getting into a Relationship     187
Straight line models: linear regression     189
Learning Objectives     189
Health warning!     190
Relationship and association     190
The linear regression model     192
Model building and variable selection     200
Curvy models: logistic regression     213
Learning Objectives     213
A second health warning!     213
Binary dependent variables     214
The logistic regression model     215
Two More Chapters     225
Measuring survival     227
Learning Objectives     227
Introduction     227
Calculating survival probabilities and the proportion surviving: the Kaplan-Meier table     228
The Kaplan-Meier chart     230
Determining median survival time     231
Comparing survival with two groups     232
Systematic review and meta-analysis     239
Learning Objectives     239
Introduction     240
Systematic review     240
Publication and other biases     244
The funnel plot     244
Combining the studies     246
Table of random numbers     251
Solutions to Exercises     253
References     273
Index     277

Editorial Reviews

"Students of medical statistics will find the book a trusty companion." (Journal of Tropical Pediatrics, November 2008)

From the Publisher