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Essential statistics for the pharmaceutical sciences

Author: Philip Rowe
Publisher: Chichester, England ; Hoboken, NJ : John Wiley & Sons, ©2007.
Edition/Format:   eBook : Document : EnglishView all editions and formats
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Essential Statistics for the Pharmaceutical Sciences is a clear, accessible introduction to the key statistical techniques employed for the analysis of data within this subject area. Written in a  Read more...

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Genre/Form: Electronic books
Additional Physical Format: Print version:
Rowe, Philip.
Essential statistics for the pharmaceutical sciences.
Chichester, England ; Hoboken, NJ : John Wiley & Sons, ©2007
(DLC) 2006102028
(OCoLC)76851441
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Philip Rowe
ISBN: 9780470319437 0470319437 9786610839421 6610839425
OCLC Number: 126874563
Description: 1 online resource (xx, 287 pages) : illustrations
Contents: Machine derived contents note: Contents --
Preface --
Statistical packages --
Part 1: Data Types --
1 Data types --
1.1 Does 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: Interval-Scale 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 --
4.7 Chapter summary --
5. Ninety-five 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 One-sided 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 two-sample t-test (1). Introducing hypothesis tests --
6.1 The two-sample t-test ? 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 two-sample t-test --
6.6 Chapter summary --
7. The two-sample t-test (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 two-sample t-test (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 two-sample t-test (4). Statistical significance, practical significance and equivalence --
9.1 Practical significance ? is the difference big enough to matter? --
9.2 Equivalence testing --
9.3 Non-inferiority 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 two-sample t-test (5). One-sided 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 one-sided test --
10.5 Should one-sided 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 t-test ? comparing two related sets of measurements --
12.1 Paired data --
12.2 We could analyse the data using a two-sample t-test --
12.3 Using a paired t-test instead --
12.4 Performing a paired t-test --
12.5 What determines whether a paired t-test will be significant? --
12.6 Greater power of a paired t-test --
12.7 The paired t-test is only applicable to naturally paired data --
12.8 Choice of experimental design --
12.9 Requirements for applying a paired t-test --
12.10 Sample sizes, practical significance and one-sided tests --
12.11 Summarizing the differences between the paired and two-sample t-tests --
12.12 Chapter summary --
13. Analyses of variance ? going beyond t-tests --
13.1 Extending the complexity of experimental designs --
13.2 One-way analysis of variance --
13.3 Two-way analysis of variance --
13.4 Multi-factorial 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: Nominal-Scale Data --
15. Describing categorized data --
15.1 Descriptive statistics --
15.2 Testing whether the population proportion might credibly be some pre-determined figure --
15.3 Chapter summary --
16. Comparing observed proportions ? the contingency chi-square test --
16.1 Using the contingency chi-square 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: Ordinal-Scale Data --
17. Ordinal and non-normally distributed data. Transformations and non-parametric tests --
17.1 Transforming data to a normal distribution --
17.2 The Mann?Whitney test ? a non-parametric method --
17.3 Dealing with ordinal data --
17.4 Other non-parametric 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 add-on --
20.4 Explore your data visually before launching into statistical testing --
20.5 Beware of multiple analyses --
20.6 Interpret both significance and non-significance with care --
Index.
Responsibility: Philip Rowe.
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