Table of contents for Medical statistics from scratch : an introduction for health professionals / David Bowers.


Bibliographic record and links to related information available from the Library of Congress catalog


Information from electronic data provided by the publisher. May be incomplete or contain other coding.


Counter
Preface to the 1st Edition
Preface to the 2nd Edition
Introduction
I Some fundamental stuff
1 First things first – the nature of data
Learning objectives
Variables and data
The good, the bad and the ugly - types of variable
Categorical variables
Metric variables

II Descriptive statistics
2 Describing data with tables
Learning objectives
What is descriptive statistics?
The frequency table
3 Describing data with charts
Learning objectives Picture it!
Charting nominal and ordinal data Charting discrete metric data
Charting continuous metric data
Charting cumulative data
4 Describing data from its distributional shape Learning objectives
The shape of things to come
5 Describing data with numeric summary values
Learning objectives
Numbers R us
Summary measures of location
Summary measures of spread
Standard deviation and the Normal distribution
III Getting the data
6 Doing it right first time – designing a study Learning objectives
Hey ho! Hey ho! It's off to work we go
Collecting the data - types of sample Types of study
Confounding
Matching
Comparing cohort and case-control designs
Getting stuck in - experimental studies
IV From little to large – statistical inference
7 From samples to populations – making inferences Learning objectives
Statistical inference

8 Chance would be a fine thing- probability, risk and odds Learning objectives
Chance would be a fine thing - the idea of probability
Calculating probability
Probability and the Normal distribution
Risk
Odds
Why you can't calculate risk in a case-control study
The link between probability and odds
The risk ratio
The odds ratio
Number needed to treat
V The informed guess - confidence interval estimation
9 Estimating the value of a single population parameter Learning objectives
Confidence interval estimation for a population mean
Confidence interval for a population proportion
Estimating a confidence interval for the median of a single population
10 Estimating the differences between two population parameters
Learning objectives
What's the difference?
Estimating the difference between the means of two independent populations – using a method based on the two-sample t test
Estimating the difference between two matched population means – using a method based on the matched-pairs t test
Estimating the difference between two independent population proportions
Estimating the difference between two independent population medians – the Mann–Whitney rank-sums method
Estimating the difference between two matched population medians - Wilcoxon signed-ranks method


11 Estimating the ratio of two population parameters Learning objectives
Estimating ratios
VI Putting it to the test


12 Testing hypotheses about the difference between two population parameters
Learning objectives
The research question and the hypothesis test
A brief summary of a few of the commonest tests
Some examples of hypothesis tests from practice
Confidence intervals versus hypothesis testing
Nobody's perfect - types of error
The power of a test
Maximising power - calculating sample size
Rules of thumb

13 Testing hypotheses about the ratio of two population parameters
Learning objectives Testing the risk ratio Testing the odds ratio

14 Testing hypotheses about the equality of two or more proportions Learning objectives
Of all the tests in all the world...the chi-squared (χ2) test


VII Getting up close

15 Measuring the association between two variables Learning objectives
Association
The correlation coefficient
16 Measuring the agreement between two variables Learning objectives
To agree or not agree: that is the question
Cohen's kappa
Measuring agreement with ordinal data - weighted kappa
Measuring the agreement between two metric continuous variables
VIII Getting into a relationship
17 Straight-line models - linear regression Learning objectives
Health warning!
Relationship and association
The linear regression model
Model building and variable selection

18 Curvy models - logistic regression Learning objectives
A second health warning!
The logistic regression model
IX Two more chapters
19 Measuring survival
Learning objectives
Introduction
Calculating survival probabilities and the proportion surviving: the Kaplan-Meier table
The Kaplan–Meier chart
Determining median survival time
Comparing survival with two groups
20 Systematic review and meta-analysis
Learning objectives
Introduction
Systematic review
Publication and other biases
The funnel plot
Combining the studies


Library of Congress subject headings for this publication:
Medical statistics.
Medicine -- Research -- Statistical methods.
Biometry.
Statistics -- methods.