Medical Statistics from Scratch: An Introduction for Health Professionals, 2nd Edition
Medical Statistics from Scratch: An Introduction for Health Professionals, 2nd Edition
ISBN: 9781119971856 January 2011 300 Pages
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Description
The level of mathematics is kept to a minimum to make the material easily accessible to the novice, and a multitude of illustrative cases are included in every chapter, drawn from the current research literature. The new edition has been completely revised and updated and includes new chapters on basic quantitative methods, measuring survival, measurement scales, diagnostic testing, bayesian methods, metaanalysis and systematic reviews.
"... After years of trying and failing, this is the only book on statistics that i have managed to read and understand"  Naveed Kirmani, Surgical Registrar, South London Healthcare HHS Trust, UK
Table of contents
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 casecontrol 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 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 casecontrol 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  the idea of confidence intervals.
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 twosample t test.
Estimating the difference between two matched population means – using a method based on the matchedpairs t test.
Estimating the difference between two independent population proportions.
Estimating the difference between two independent population medians – the Mann–Whitney ranksums method.
Estimating the difference between two matched population medians  Wilcoxon signedranks method.
11 Estimating the ratio of two population parameters.
Learning objectives.
Estimating ratios of means, risks and odds.
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 chisquared (χ^{2}) 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 Straightline 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 KaplanMeier table.
The Kaplan–Meier chart.
Determining median survival time.
Comparing survival with two groups.
20 Systematic review and metaanalysis.
Learning objectives.
Introduction.
Systematic review.
Publication and other biases.
The funnel plot.
Combining the studies.
Appendix: Table of random numbers.
Solutions to Exercises.
References.
Index.
Reviews
Errata
Chapter  Page  Details  Date  Print Run 

66  Errata Table 5.5, heading to last column. For Mas read Mean.  May 2009  
 
164  Errata The last bullet point should read Take the result from the previous step. This result is called the test statistic.  May 2009  
 
164  Errata Footnote. Remove the square root sign from the equation.  May 2009  
 
268  Errata Answer 14.2. Delete all of the existing answer and substitute the following: The test statistic = {(8  3.667)2/3.667 + (3  7.333)2/7.333 + (2 6.333)2/6.333 + (1712.667)2/12.667} = 12.109. Since we have a 2 x 2 table, then we are in the first row of Table 14.3, because (2 1) x (2 1) = 1 x 1 = 1, and the critical chisquared value which must be exceeded to reject the null hypothesis is 3.85. The test statistic value of 12.109 exceeds this value, so the evidence is strong enough for us to reject the null hypothesis of equal proportions of smokers in both Agar groups. There does appear to be a relationship between smoking and Apgar scores.  May 2009  
