Medical Statistics from Scratch: An Introduction for Health Professionals

Description

This long awaited second edition of this bestseller continues to provide a comprehensive, user friendly, down-to-earth guide to elementary statistics. The book presents a detailed account of the most important procedures for the analysis of data, from the calculation of simple proportions, to a variety of statistical tests, and the use of regression models for modeling of clinical outcomes.

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, meta-analysis 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 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 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 - 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 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 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 chi-squared (?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 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.

Appendix: Table of random numbers.
Solutions to Exercises.
References.
Index.

Author Information

David Bowers, School of Medicine, University of Leeds, UK.

Reviews

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

Errata

Chapter
Page 66 Table 5.5, heading to last column. For ‘Mas’ read ‘Mean’. May 2009
Page 164 The last bullet point should read “Take the result from the previous step. This result is called the test statistic.” May 2009
Page 164 Footnote. Remove the square root sign from the equation. May 2009
Page 268 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 + (17-12.667)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 chi-squared 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