Ebook
Essential Statistics for the Pharmaceutical SciencesISBN: 9780470319437
308 pages
April 2007

This text is a clear, accessible introduction to the key statistical techniques employed for the analysis of data within this subject area. Written in a concise and logical manner, the book explains why statistics are necessary and discusses the issues that experimentalists need to consider. The reader is carefully taken through the whole process, from planning an experiment to interpreting the results, avoiding unnecessary calculation methodology. The most commonly used statistical methods are described in terms of their purpose, when they should be used and what they mean once they have been performed.
Numerous examples are provided throughout the text, all within a pharmaceutical context, with key points highlighted in summary boxes to aid student understanding.
Essential Statistics for the Pharmaceutical Sciences takes a new and innovative approach to statistics with an informal style that will appeal to the reader who finds statistics a challenge!
This book is an invaluable introduction to statistics for any science student. It is an essential text for students taking biomedical or pharmaceuticalbased science degrees and also a useful guide for researchers.
Statistical packages.
PART 1. DATA TYPES.
1. Data types.
1.1Does it really matter?
1.2 Interval scale data.
1.3 Ordinal scale data.
1.4 Nominal scale data.
1.5 Structure of this book.
1.6 Chapter summary.
PART 2. INTERVALSCALE DATA.
2. Descriptive statistics.
2.1 Summarizing data sets.
2.2 Indicators of central tendency. mean, median and mode.
2.3 Describing variability. standard deviation and coefficient of variation.
2.4 Quartiles. another way to describe data .
2.5 Using computer packages to generate descriptive statistics.
2.6 Chapter summary.
3. The normal distribution.
3.1 What is a normal distribution? .
3.2 Identifying data that are not normally distributed.
3.3 Proportions of individuals within one or two standard deviations of the mean.
3.4 Chapter summary.
4. Sampling from populations. the SEM.
4.1 Samples and populations.
4.2 From sample to population.
4.3 Types of sampling error.
4.4 What factors control the extent of random sampling error?
4.5 Estimating likely sampling error. The SEM.
4.6 Offsetting sample size against standard deviation.
4.7Chapter summary.
5. Ninetyfive per cent confidence interval for the mean.
5.1 What is a confidence interval?
5.2 How wide should the interval be?
5.3 What do we mean by ‘95 per cent’ confidence?
5.4 Calculating the interval width.
5.5 A long series of samples and 95 per cent confidence intervals.
5.6 How sensitive is the width of the confidence interval to changes in the SD, the sample size or the required level of confidence?
5.7 Two statements.
5.8 Onesided 95 per cent confidence intervals.
5.9 The 95 per cent confidence interval for the difference between two treatments.
5.10 The need for data to follow a normal distribution and data transformation.
5.11 Chapter summary.
6. The twosample ttest(1).Introducing hypothesis tests.
6.1 The twosample ttest. an example of a hypothesis test.
6.2 ‘Significance’.
6.3 The risk of a false positive finding.
6.4 What factors will influence whether or not we obtain a significant outcome?
6.5 Requirements for applying a twosample ttest.
6.6 Chapter summary.
7. The twosample ttest(2).The dreaded P value.
7.1 Measuring how significant a result is.
7.2 P values.
7.3 Two ways to define significance?
7.4 Obtaining the P value.
7.5 P values or 95 per cent confidence intervals?
7.6 Chapter summary.
8. The twosample ttest(3).False negatives, power and necessary sample sizes.
8.1 What else could possibly go wrong?
8.2 Power.
8.3 Calculating necessary sample size.
8.4 Chapter summary.
9. The twosample ttest(4).Statistical significance, practical significance and equivalence.
9.1 Practical significance. is the difference big enough to matter?
9.2 Equivalence testing.
9.3 Noninferiority testing.
9.4 P values are less informative and can be positively misleading.
9.5 Setting equivalence limits prior to experimentation.
9.6 Chapter summary.
10. The twosample ttest(5).Onesided testing.
10.1 Looking for a change in a specified direction.
10.2 Protection against false positives.
10.3 Temptation!.
10.4 Using a computer package to carry out a onesided test.
10.5 Should onesided tests be used more commonly?
10.5 Chapter summary.
11. What does a statistically significant result really tell us?
11.1 Interpreting statistical significance.
11.2 Starting from extreme scepticism.
11.3 Chapter summary.
12. The paired ttest. comparing two related sets of measurements.
12.1 Paired data.
12.2 We could analyse the data using a twosample ttest.
12.3 Using a paired ttest instead.
12.4 Performing a paired ttest.
12.5 What determines whether a paired ttest will be significant?
12.6 Greater power of a paired ttest.
12.7 The paired ttest is only applicable to naturally paired data.
12.8 Choice of experimental design.
12.9 Requirements for applying a paired ttest .
12.10 Sample sizes, practical significance and onesided tests.
12.11 Summarizing the differences between the paired and twosample ttests.
12.12 Chapter summary.
13. Analyses of variance. going beyond ttests.
13.1 Extending the complexity of experimental designs.
13.2 Oneway analysis of variance.
13.3 Twoway analysis of variance.
13.4 Multifactorial experiments.
13.5 Keep it simple. Keep it powerful.
13.6 Chapter summary.
14. Correlation and regression. relationships between measured values.
14.1 Correlation analysis.
14.2 Regression analysis.
14.3 Multiple regression.
14.4 Chapter summary.
PART 3. NOMINALSCALE DATA.
15. Describing categorized data.
15.1 Descriptive statistics.
15.2 Testing whether the population proportion might credibly be some predetermined figure.
15.3 Chapter summary.
16. Comparing observed proportions. the contingency chisquare test.
16.1 Using the contingency chisquare test to compare observed proportions.
16.2 Obtaining a 95 per cent CI for the change in the proportion of expulsions. is the difference large enough to be of practical significance?
16.3 Larger tables. attendance at diabetic clinics.
16.4 Planning experimental size.
16.5 Chapter summary.
PART 4. ORDINALSCALE DATA.
17. Ordinal and nonnormally distributed data.Transformations and nonparametric tests.
17.1 Transforming data to a normal distribution.
17.2 The MannWhitney test. a nonparametric method.
17.3 Dealing with ordinal data.
17.4 Other nonparametric methods.
17.5 Chapter summary.
Appendix to Chapter 17.
PART 5. SOME CHALLENGES FROM THE REAL WORLD.
18. Multiple testing.
18.1 What is it and why is it a problem?
18.2 Where does multiple testing arise?
18.3 Methods to avoid false positives.
18.4 The role of scientific journals.
18.5 Chapter summary.
19. Questionnaires.
19.1 Is there anything special about questionnaires?
19.2 Types of questions.
19.3 Designing a questionnaire.
19.4 Sample sizes and return rates.
19.5 Analysing the results.
19.6 Confounded epidemiological data.
19.7 Multiple testing with questionnaire data.
19.8 Chapter summary.
PART 6. CONCLUSIONS.
20. Conclusions.
20.1 Be clear about the purpose of the experiment.
20.2 Keep the experimental design simple and therefore clear and powerful.
20.3 Draw up a statistical analysis plan as part of the experimental design. it is not a last minute addon.
20.4 Explore your data visually before launching into statistical testing.
20.5 Beware of multiple analyses.
20.6 Interpret both significance and nonsignificance with care.
Index.
 A clear, accessible introduction to the key statistical techniques used within the pharmaceutical and biomedical sciences
 All examples taken from a biomedical/pharmaceutical context to enhance student understanding
 Supplementary website to include additional methods, instructions for performing tests and specific information for carrying out each text in packages such as SPSS and Minitab. Example data sets are also provided as Excel files to enable readers to try the analyses without reentering the data
 Key points emphasized in summary boxes and potential ‘abuses’ highlighted in ‘Pirate Boxes’ a summary is provided at the end of each chapter