Medical Statistics from Scratch: An Introduction for Health Professionals 4th Edition, ISBN-13: 978-1119523888

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Medical Statistics from Scratch: An Introduction for Health Professionals 4th Edition, ISBN-13: 978-1119523888

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• Publisher: ‎ Wiley; 4th edition (October 7, 2019)
• Language: ‎ English
• 495 pages
• ISBN-10: ‎ 1119523885
• ISBN-13: ‎ 978-1119523888

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.

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.

Table of Contents:

Cover

Preface to the 4th Edition

Preface to the 3rd Edition

Preface to the 2nd Edition

Preface to the 1st Edition

Introduction

I: Some Fundamental Stuff

1 First things first – the nature of data

Variables and data

Where are we going …?

The good, the bad, and the ugly – types of variables

Categorical data

Ordinal categorical data

Metric data

Continuous metric data

How can I tell what type of variable I am dealing with?

The baseline table

II: Descriptive Statistics

2 Describing data with tables

Descriptive statistics. What can we do with raw data?

Frequency tables – nominal data

The frequency distribution

Relative frequency

Frequency tables – ordinal data

Frequency tables – metric data

Frequency tables with discrete metric data

Cumulative frequency

Frequency tables with continuous metric data – grouping the raw data

Open‐ended groups

Cross‐tabulation – contingency tables

Ranking data

3 Every picture tells a story – describing data with charts

Picture it!

Charting nominal and ordinal data

The simple bar chart

The clustered bar chart

The stacked bar chart

Charting discrete metric data

Charting continuous metric data

The box (and whisker) plot

Charting cumulative data

The cumulative frequency curve with continuous metric data

Charting time‐based data – the time series chart

The scatterplot

The bubbleplot

4 Describing data from its shape

The shape of things to come

Skewness and kurtosis as measures of shape

Kurtosis

Symmetric or mound‐shaped distributions

Normalness – the Normal distribution

Bimodal distributions

Determining skew from a box plot

5 Measures of location – Numbers R Us

Numbers, percentages, and proportions

Preamble

Numbers, percentages, and proportions

Handling percentages – for those of us who might need a reminder

Summary measures of location

The mode

The median

The mean

Percentiles

Calculating a percentile value

What is the most appropriate measure of location?

6 Measures of spread – Numbers R Us – (again)

Preamble

The range

The interquartile range (IQR)

Estimating the median and interquartile range from the cumulative frequency curve

The boxplot (also known as the box and whisker plot)

Standard deviation

Standard deviation and the Normal distribution

Testing for Normality

Transforming data

7 Incidence, prevalence, and standardisation

Preamble

The incidence rate and the incidence rate ratio (IRR)

Prevalence

Some other useful rates

Age‐specific mortality rate

Standardisation – the age‐standardised mortality rate

The direct method

The standard population and the comparative mortality ratio (CMR)

The indirect method

The standardised mortality rate

III: The Confounding Problem

8 Confounding – like the poor, (nearly) always with us

Preamble

What is confounding?

Confounding by indication

Residual confounding

Detecting confounding

Dealing with confounding – if confounding is such a problem, what can we do about it?

Using restriction

Using matching

Using stratification

Using adjustment

Using randomisation

IV: Design and Data

9 Research design – Part I

Preamble

Hey ho! Hey ho! It’s off to work we go

Types of study

Observational studies

Ecological studies

The ecological fallacy

10 Research design – Part II: Getting stuck in – experimental studies

Clinical trials

Randomisation and the randomised controlled trial (RCT)

Block randomisation

Stratification

Blinding

The crossover RCT

Selection of participants for an RCT

Intention to treat analysis (ITT)

11 Getting the participants for your study

From populations to samples – statistical inference

Collecting the data – types of sample

The simple random sample and its offspring

The systematic random sample

The stratified random sample

The cluster sample

Consecutive and convenience samples

How many participants should we have? Sample size

Inclusion and exclusion criteria

Getting the data

V: Chance Would Be a Fine Thing

12 The idea of probability

Preamble

Calculating probability – proportional frequency

Two useful rules for simple probability

Conditional and Bayesian statistics

Probability distributions

Discrete versus continuous probability distributions

The binomial probability distribution

The Poisson probability distribution

The Normal probability distribution

13 Risk and odds

Absolute risk and the absolute risk reduction (ARR)

The risk ratio

The reduction in the risk ratio (or relative risk reduction (RRR))

A general formula for the risk ratio

Reference value

Number needed to treat (NNT)

What happens if the initial risk is small?

Confounding with the risk ratio

Odds

Why you can’t calculate risk in a case–control study

The link between probability and odds

The odds ratio

Confounding with the odds ratio

Approximating the risk ratio from the odds ratio

VI: The Informed Guess – An Introduction to Confidence Intervals

14 Estimating the value of a single population parameter – the idea of confidence intervals

Confidence interval estimation for a population mean

The standard error of the mean

How we use the standard error of the mean to calculate a confidence interval for a population mean

An example from practice

An example from practice

Confidence interval for a population proportion

Estimating a confidence interval for the median of a single population

15 Using confidence intervals to compare two population parameters

What’s the difference?

Comparing two independent population means

An example using birthweights

Assessing the evidence using the confidence interval (and was the sample size large enough?)

Comparing two paired population means

Within‐subject and between‐subject variations

Comparing two independent population proportions

An example from practice

Comparing two independent population medians – the Mann–Whitney rank sums method

An example from practice

Comparing two matched population medians – the Wilcoxon signed‐ranks method

An example from practice

16 Confidence intervals for the ratio of two population parameters

Getting a confidence interval for the ratio of two independent population means

An example from practice

Confidence interval for a population risk ratio

An example from practice

An example from practice

Confidence intervals for a population odds ratio

An example from practice

Confidence intervals for hazard ratios

VII: Putting it to the Test

17 Testing hypotheses about the difference between two population parameters

Answering the question

The hypothesis

The null hypothesis

The hypothesis testing process

The p‐value and the decision rule

A brief summary of a few of the commonest tests

Using the p‐value to compare the means of two independent populations

Interpreting computer hypothesis test results for the difference in two independent population means – the two‐sample t test

Output from Minitab – two‐sample t test of difference in mean birthweights of babies born to white mothers and to non‐white mothers

Output from SPSS: two‐sample t test of difference in mean birthweights of babies born to white mothers and to non‐white mothers

Comparing the means of two paired populations – the matched‐pairs t test

Using p‐values to compare the medians of two independent populations: the Mann–Whitney rank‐sums test

How the Mann–Whitney test works

Correction for multiple comparisons

The Bonferroni correction for multiple testing

Interpreting computer output for the Mann–Whitney test

Two matched medians – the Wilcoxon signed‐ranks test

Confidence intervals versus hypothesis testing

What could possibly go wrong?

Types of error

The power of a test

An example from practice

Maximising power – calculating sample size

Rule of thumb 1. Comparing the means of two independent populations (metric data)

Rule of thumb 2. Comparing the proportions of two independent populations (binary data)

18 The Chi‐squared (χ2) test – what, why, and how?

Of all the tests in all the world – you had to walk into my hypothesis testing procedure

Using chi‐squared to test for related‐ness or for the equality of proportions

Calculating the chi‐squared statistic

Using the chi‐squared statistic

Yate’s correction (continuity correction)

Fisher’s exact test

The chi‐squared test with Minitab

The chi‐squared test with SPSS

The chi‐squared test for trend

SPSS output for chi‐squared trend test

19 Testing hypotheses about the ratio of two population parameters

Preamble

The chi‐squared test with the risk ratio

The chi‐squared test with odds ratios

The chi‐squared test with hazard ratios

VIII: Becoming Acquainted

20 Measuring the association between two variables

Preamble – plotting data

Association

The scatterplot

The correlation coefficient

Pearson’s correlation coefficient

Is the correlation coefficient statistically significant in the population?

An example from practice

Spearman’s rank correlation coefficient

An example from practice

21 Measuring agreement

To agree or not agree: that is the question

Cohen’s kappa (κ)

Some shortcomings of kappa

Weighted kappa

Measuring the agreement between two metric continuous variables, the Bland–Altmann plot

IX: Getting into a Relationship

22 Straight line models

Health warning!

Relationship and association

A causal relationship – explaining variation

Refresher – finding the equation of a straight line from a graph

The linear regression model

First, is the relationship linear?

Estimating the regression parameters – the method of ordinary least squares (OLS)

Basic assumptions of the ordinary least squares procedure

Back to the example – is the relationship statistically significant?

Using SPSS to regress birthweight on mother’s weight

Using Minitab

Interpreting the regression coefficients

Goodness‐of‐fit, R2

Multiple linear regression

Adjusted goodness‐of‐fit:

Including nominal covariates in the regression model: design variables and coding

Building your model. Which variables to include?

Automated variable selection methods

Manual variable selection methods

Adjustment and confounding

An example from practice

Diagnostics – checking the basic assumptions of the multiple linear regression model

Analysis of variance

23 Curvy models

A second health warning!

The binary outcome variable

Finding an appropriate model when the outcome variable is binary

The logistic regression model

Estimating the parameter values

Interpreting the regression coefficients

Have we got a significant result? statistical inference in the logistic regression model

The Odds Ratio

The multiple logistic regression model

Building the model

Goodness‐of‐fit

24 Counting models

Preamble

Poisson regression

The Poisson regression equation

Estimating β1 and β2 with the estimators b0 and b1

Interpreting the estimated coefficients of a Poisson regression, b0 and b1

Model building – variable selection

Goodness‐of‐fit

Zero‐inflated Poisson regression

Negative binomial regression

Zero‐inflated negative binomial regression

X: Four More Chapters

25 Measuring survival

Preamble

Censored data

A simple example of survival in a single group

Calculating survival probabilities and the proportion surviving: the Kaplan–Meier table

The Kaplan–Meier curve

Determining median survival time

Comparing survival with two groups

The log‐rank test

An example of the log‐rank test in practice

The hazard ratio

The proportional hazards (Cox’s) regression model – introduction

The proportional hazards (Cox’s) regression model – the detail

Checking the assumptions of the proportional hazards model

An example of proportional hazards regression

26 Systematic review and meta‐analysis

Introduction

Systematic review

The forest plot

Publication and other biases

The funnel plot

Significance tests for bias – Begg’s and Egger’s tests

Combining the studies: meta‐analysis

The problem of heterogeneity – the Q and I2 tests

27 Diagnostic testing

Preamble

The measures – sensitivity and specificity

The positive prediction and negative prediction values (PPV and NPV)

The sensitivity–specificity trade‐off

Using the ROC curve to find the optimal sensitivity versus specificity trade‐off

Note

28 Missing data

The missing data problem

Types of missing data

Consequences of missing data

Dealing with missing data

Imputation methods – simple imputation

Multiple imputation

Full Information Maximum Likelihood (FIML) and other methods

Appendix Table of random numbers

References

Solutions to exercises

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6. Measures of spread

Chapter 7. Incidence, prevalence, standardisation

Chapter 8. Confounding

Chapter 9. Research design Part I

Chapter 10

Chapter 11. Getting the participants

Chapter 12. The idea of probability

Chapter 13. Risk and odds

Chapter 14. Estimating the value of a single population parameter

Chapter 15

Chapter 16. Confidence intervals for ratios

Chapter 17. Testing hypotheses about the difference between two population parameters

Chapter 18. Chi‐squared test

Chapter 19. Testing hypothesis about the ratio of two population parameters

Chapter 20. Measuring association

Chapter 21. Agreement

Chapter 22. Linear regression

Chapter 23. Logistic regression

Chapter 24. Poisson regression

Chapter 25. Survival analysis

Chapter 26. Systematic review

Chapter 27. Diagnostic testing

Chapter 28. Missing data

Index

End User License Agreement

David Bowers Leeds Institute of Health Sciences, School of Medicine, University of Leeds, Leeds, UK.

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