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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.