Essential Statistics for the Pharmaceutical Sciences

 
 
Wiley-Blackwell (Verlag)
  • 2. Auflage
  • |
  • erschienen am 20. Januar 2016
  • |
  • 432 Seiten
 
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-1-118-91340-6 (ISBN)
 
Essential Statistics for the Pharmaceutical Sciences is targeted at all those involved in research in pharmacology, pharmacy or other areas of pharmaceutical science; everybody from undergraduate project students to experienced researchers should find the material they need.
This book will guide all those who are not specialist statisticians in using sound statistical principles throughout the whole journey of a research project - designing the work, selecting appropriate statistical methodology and correctly interpreting the results. It deliberately avoids detailed calculation methodology. Its key features are friendliness and clarity. All methods are illustrated with realistic examples from within pharmaceutical science.
This edition now includes expanded coverage of some of the topics included in the first edition and adds some new topics relevant to pharmaceutical research.
* a clear, accessible introduction to the key statistical techniques used within the pharmaceutical sciences
* all examples set in relevant pharmaceutical contexts.
* key points emphasised in summary boxes and warnings of potential abuses in 'pirate boxes'.
* supplementary material - full data sets and detailed instructions for carrying out analyses using packages such as SPSS or Minitab - provided at www.ljmu.ac.uk/pbs/rowestats/
An invaluable introduction to statistics for any science student and an essential text for all those involved in pharmaceutical research at whatever level.
2. Auflage
  • Englisch
  • Hoboken
  • |
  • Großbritannien
John Wiley & Sons Inc
  • Für Beruf und Forschung
  • 7,28 MB
978-1-118-91340-6 (9781118913406)
111891340X (111891340X)
weitere Ausgaben werden ermittelt
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Statistical packages
  • About the website
  • Part 1 Presenting data
  • Chapter 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
  • Chapter 2 Data presentation
  • 2.1 Numerical tables
  • 2.2 Bar charts and histograms
  • 2.3 Pie charts
  • 2.4 Scatter plots
  • 2.5 Pictorial symbols
  • 2.6 Chapter summary
  • Part 2 Interval-scale data
  • Chapter 3 Descriptive statistics for interval scale data
  • 3.1 Summarising data sets
  • 3.2 Indicators of central tendency: Mean, median and mode
  • 3.3 Describing variability - standard deviation and coefficient of variation
  • 3.4 Quartiles - Another way to describe data
  • 3.5 Describing ordinal data
  • 3.6 Using computer packages to generate descriptive statistics
  • 3.7 Chapter summary
  • Chapter 4 The normal distribution
  • 4.1 What is a normal distribution?
  • 4.2 Identifying data that are not normally distributed
  • 4.3 Proportions of individuals within 1SD or 2SD of the mean
  • 4.4 Skewness and kurtosis
  • 4.5 Chapter summary
  • 4.6 Appendix: Power, sample size and the problem of attempting to test for a normal distribution
  • Chapter 5 Sampling from populations: The standard error of the mean
  • 5.1 Samples and populations
  • 5.2 From sample to population
  • 5.3 Types of sampling error
  • 5.4 What factors control the extent of random sampling error when estimating a population mean?
  • 5.5 Estimating likely sampling error - The SEM
  • 5.6 Offsetting sample size against SD
  • 5.7 Chapter summary
  • Chapter 6 95% Confidence interval for the mean and data transformation
  • 6.1 What is a confidence interval?
  • 6.2 How wide should the interval be?
  • 6.3 What do we mean by '95%' confidence?
  • 6.4 Calculating the interval width
  • 6.5 A long series of samples and 95% C.I.s
  • 6.6 How sensitive is the width of the C.I. to changes in the SD, the sample size or the required level of confidence?
  • 6.7 Two statements
  • 6.8 One-sided 95% C.I.s
  • 6.9 The 95% C.I. for the difference between two treatments
  • 6.10 The need for data to follow a normal distribution and data transformation
  • 6.11 Chapter summary
  • Chapter 7 The two-sample t-test (1): Introducing hypothesis tests
  • 7.1 The two-sample t-test - an example of an hypothesis test
  • 7.2 Significance
  • 7.3 The risk of a false positive finding
  • 7.4 What aspects of the data will influence whether or not we obtain a significant outcome?
  • 7.5 Requirements for applying a two-sample t-test
  • 7.6 Performing and reporting the test
  • 7.7 Chapter summary
  • Chapter 8 The two-sample t-test (2): The dreaded P value
  • 8.1 Measuring how significant a result is
  • 8.2 P values
  • 8.3 Two ways to define significance?
  • 8.4 Obtaining the P value
  • 8.5 P values or 95% confidence intervals?
  • 8.6 Chapter summary
  • Chapter 9 The two-sample t-test (3): False negatives, power and necessary sample sizes
  • 9.1 What else could possibly go wrong?
  • 9.2 Power
  • 9.3 Calculating necessary sample size
  • 9.4 Chapter summary
  • Chapter 10 The two-sample t-test (4): Statistical significance, practical significance and equivalence
  • 10.1 Practical significance - Is the difference big enough to matter?
  • 10.2 Equivalence testing
  • 10.3 Non-inferiority testing
  • 10.4 P values are less informative and can be positively misleading
  • 10.5 Setting equivalence limits prior to experimentation
  • 10.6 Chapter summary
  • Chapter 11 The two-sample t-test (5): One-sided testing
  • 11.1 Looking for a change in a specified direction
  • 11.2 Protection against false positives
  • 11.3 Temptation!
  • 11.4 Using a computer package to carry out a one-sided test
  • 11.5 Chapter summary
  • Chapter 12 What does a statistically significant result really tell us?
  • 12.1 Interpreting statistical significance
  • 12.2 Starting from extreme scepticism
  • 12.3 Bayesian statistics
  • 12.4 Chapter summary
  • Chapter 13 The paired t-test: Comparing two related sets of measurements
  • 13.1 Paired data
  • 13.2 We could analyse the data by a two-sample t-test
  • 13.3 Using a paired t-test instead
  • 13.4 Performing a paired t-test
  • 13.5 What determines whether a paired t-test will be significant?
  • 13.6 Greater power of the paired t-test
  • 13.7 Applicability of the test
  • 13.8 Choice of experimental design
  • 13.9 Requirement for applying a paired t-test
  • 13.10 Sample sizes, practical significance and one-sided tests
  • 13.11 Summarising the differences between paired and two-sample t-tests
  • 13.12 Chapter summary
  • Chapter 14 Analyses of variance: Going beyond t-tests
  • 14.1 Extending the complexity of experimental designs
  • 14.2 One-way analysis of variance
  • 14.3 Two-way analysis of variance
  • 14.4 Fixed and random factors
  • 14.5 Multi-factorial experiments
  • 14.6 Chapter summary
  • Chapter 15 Correlation and regression - Relationships between measured values
  • 15.1 Correlation analysis
  • 15.2 Regression analysis
  • 15.3 Multiple regression
  • 15.4 Chapter summary
  • Chapter 16 Analysis of covariance
  • 16.1 A clinical trial where ANCOVA would be appropriate
  • 16.2 General interpretation of ANCOVA results
  • 16.3 Analysis of the COPD trial results
  • 16.4 Advantages of ANCOVA over a simple two-sample t-test
  • 16.5 Chapter summary
  • Part 3 Nominal-scale data
  • Chapter 17 Describing categorised data and the goodness of fit chi-square test
  • 17.1 Descriptive statistics
  • 17.2 Testing whether the population proportion might credibly be some pre-determined figure
  • 17.3 Chapter summary
  • Chapter 18 Contingency chi-square, Fisher's and McNemar's tests
  • 18.1 Using the contingency chi-square test to compare observed proportions
  • 18.2 Extent of change in proportion with an expulsion - Clinically significant?
  • 18.3 Larger tables - Attendance at diabetic clinics
  • 18.4 Planning experimental size
  • 18.5 Fisher's exact test
  • 18.6 McNemar's test
  • 18.7 Chapter summary
  • 18.8 Appendix
  • Chapter 19 Relative risk, odds ratio and number needed to treat
  • 19.1 Measures of treatment effect - relative risk, odds ratio and number needed to treat
  • 19.2 Similarity between relative risk and odds ratio
  • 19.3 Interpreting the various measures
  • 19.4 95% confidence intervals for measures of effect size
  • 19.5 Chapter summary
  • Chapter 20 Logistic regression
  • 20.1 Modelling a binary outcome
  • 20.2 Additional predictors and the problem of confounding
  • 20.3 Analysis by computer package
  • 20.4 Extending logistic regression beyond dichotomous outcomes
  • 20.5 Chapter summary
  • 20.6 Appendix
  • Part 4 Ordinal-scale data
  • Chapter 21 Ordinal and non-normally distributed data: Transformations and non-parametric tests
  • 21.1 Transforming data to a normal distribution
  • 21.2 The Mann-Whitney test - a non-parametric method
  • 21.3 Dealing with ordinal data
  • 21.4 Other non-parametric methods
  • 21.5 Chapter summary
  • 21.6 Appendix
  • Part 5 Other topics
  • Chapter 22 Measures of agreement
  • 22.1 Answers to several questions
  • 22.2 Several answers to one question - do they agree?
  • 22.3 Chapter summary
  • Chapter 23 Survival analysis
  • 23.1 What special problems arise with survival data?
  • 23.2 Kaplan-Meier survival estimation
  • 23.3 Declining sample sizes in survival studies
  • 23.4 Precision of sampling estimates of survival
  • 23.5 Indicators of survival
  • 23.6 Testing for differences in survival
  • 23.7 Chapter summary
  • Chapter 24 Multiple testing
  • 24.1 What is it and why is it a problem?
  • 24.2 Where does multiple testing arise?
  • 24.3 Methods to avoid false positives
  • 24.4 The role of scientific journals
  • 24.5 Chapter summary
  • Chapter 25 Questionnaires
  • 25.1 Types of questions
  • 25.2 Sample sizes and low return rates
  • 25.3 Analysing the results
  • 25.4 Problem number two: Confounded questionnaire data
  • 25.5 Problem number three: Multiple testing with questionnaire data
  • 25.6 Chapter summary
  • Index
  • EULA

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