Philip Rowe

Essential Statistics for the Pharmaceutical Sciences (eBook, PDF)

• Format: PDF

Produktbeschreibung

"... this text takes a novel approach... The style... is not as dry as other statistics texts, and so should not be intimidating even to a relative newcomer to the subject... The layout is easy to navigate, there are chapter aims, summaries and "key point boxes" throughout." -The Pharmaceutical Journal, 2008 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 pharmaceutical-based science degrees and also a useful guide for researchers.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 308
• Erscheinungstermin: 4. April 2007
• Englisch
• ISBN-13: 9780470319437
• Artikelnr.: 37291141

Autorenporträt

Dr Philip Rowe. Reader in Pharmaceutical Computing, School of Pharmacy and Chemistry, Liverpool,?UK In addition to Dr Rowe's teaching and research at LJMU he also works on a consultancy basis offering advice and assistance with pharmacokinetic or general data analysis problems for the pharmaceutical industry, professional organizations and hospitals. He has recently secured a post delivering statistics training for the Institute of Clinical Research.

Inhaltsangabe

Preface xiii Statistical packages xix About the website xxi PART 1
PRESENTING DATA 1 1 Data types 3 1.1 Does it really matter? 3 1.2 Interval
scale data 4 1.3 Ordinal scale data 4 1.4 Nominal scale data 5 1.5
Structure of this book 6 1.6 Chapter summary 6 2 Data presentation 7 2.1
Numerical tables 8 2.2 Bar charts and histograms 9 2.3 Pie charts 14 2.4
Scatter plots 16 2.5 Pictorial symbols 21 2.6 Chapter summary 22 PART 2
INTERVAL?]SCALE DATA 23 3 Descriptive statistics for interval scale data 25
3.1 Summarising data sets 25 3.2 Indicators of central tendency: Mean,
median and mode 26 3.3 Describing variability - standard deviation and
coefficient of variation 33 3.4 Quartiles - Another way to describe data 36
3.5 Describing ordinal data 40 3.6 Using computer packages to generate
descriptive statistics 43 3.7 Chapter summary 45 4 The normal distribution
47 4.1 What is a normal distribution? 47 4.2 Identifying data that are not
normally distributed 48 4.3 Proportions of individuals within 1SD or 2SD of
the mean 52 4.4 Skewness and kurtosis 54 4.5 Chapter summary 57 4.6
Appendix: Power, sample size and the problem of attempting to test for a
normal distribution 58 5 Sampling from populations: The standard error of
the mean 63 5.1 Samples and populations 63 5.2 From sample to population 65
5.3 Types of sampling error 65 5.4 What factors control the extent of
random sampling error when estimating a population mean? 68 5.5 Estimating
likely sampling error - The SEM 70 5.6 Offsetting sample size against SD 74
5.7 Chapter summary 75 6 95% Confidence interval for the mean and data
transformation 77 6.1 What is a confidence interval? 78 6.2 How wide should
the interval be? 78 6.3 What do we mean by '95%' confidence? 79 6.4
Calculating the interval width 80 6.5 A long series of samples and 95%
C.I.s 81 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? 82 6.7 Two statements
85 6.8 One?]sided 95% C.I.s 85 6.9 The 95% C.I. for the difference between
two treatments 88 6.10 The need for data to follow a normal distribution
and data transformation 90 6.11 Chapter summary 94 7 The two?]sample
t?]test (1): Introducing hypothesis tests 95 7.1 The two?]sample t?]test -
an example of an hypothesis test 96 7.2 Significance 103 7.3 The risk of a
false positive finding 104 7.4 What aspects of the data will influence
whether or not we obtain a significant outcome? 106 7.5 Requirements for
applying a two?]sample t?]test 108 7.6 Performing and reporting the test
109 7.7 Chapter summary 110 8 The two?]sample t?]test (2): The dreaded P
value 111 8.1 Measuring how significant a result is 111 8.2 P values 112
8.3 Two ways to define significance? 113 8.4 Obtaining the P value 113 8.5
P values or 95% confidence intervals? 114 8.6 Chapter summary 115 9 The
two?]sample t?]test (3): False negatives, power and necessary sample sizes
117 9.1 What else could possibly go wrong? 118 9.2 Power 119 9.3
Calculating necessary sample size 122 9.4 Chapter summary 130 10 The
two?]sample t?]test (4): Statistical significance, practical significance
and equivalence 131 10.1 Practical significance - Is the difference big
enough to matter? 131 10.2 Equivalence testing 135 10.3 Non?]inferiority
testing 139 10.4 P values are less informative and can be positively
misleading 141 10.5 Setting equivalence limits prior to experimentation 143
10.6 Chapter summary 144 11 The two?]sample t?]test (5): One?]sided testing
145 11.1 Looking for a change in a specified direction 146 11.2 Protection
against false positives 148 11.3 Temptation! 149 11.4 Using a computer
package to carry out a one?]sided test 153 11.5 Chapter summary 153 12 What
does a statistically significant result really tell us? 155 12.1
Interpreting statistical significance 155 12.2 Starting from extreme
scepticism 159 12.3 Bayesian statistics 160 12.4 Chapter summary 161 13 The
paired t?]test: Comparing two related sets of measurements 163 13.1 Paired
data 163 13.2 We could analyse the data by a two?]sample t?]test 165 13.3
Using a paired t?]test instead 165 13.4 Performing a paired t?]test 166
13.5 What determines whether a paired t?]test will be significant? 169 13.6
Greater power of the paired t?]test 170 13.7 Applicability of the test 170
13.8 Choice of experimental design 171 13.9 Requirement for applying a
paired t?]test 172 13.10 Sample sizes, practical significance and
one?]sided tests 173 13.11 Summarising the differences between paired and
two?]sample t?]tests 175 13.12 Chapter summary 175 14 Analyses of variance:
Going beyond t?]tests 177 14.1 Extending the complexity of experimental
designs 177 14.2 One?]way analysis of variance 178 14.3 T wo?]way analysis
of variance 188 14.4 Fixed and random factors 198 14.5 Multi?]factorial
experiments 204 14.6 Chapter summary 204 15 Correlation and regression -
Relationships between measured values 207 15.1 Correlation analysis 208
15.2 Regression analysis 218 15.3 Multiple regression 225 15.4 Chapter
summary 235 16 Analysis of covariance 237 16.1 A clinical trial where
ANCOVA would be appropriate 238 16.2 General interpretation of ANCOVA
results 239 16.3 Analysis of the COPD trial results 241 16.4 Advantages of
ANCOVA over a simple two?]sample t?]test 244 16.5 Chapter summary 249 PART
3 NOMINAL?]SCALE DATA 251 17 D escribing categorised data and the goodness
of fit chi?]square test 253 17.1 Descriptive statistics 254 17.2 Testing
whether the population proportion might credibly be some pre?]determined
figure 258 17.3 Chapter summary 264 18 Contingency chi?]square, Fisher's
and McNemar's tests 265 18.1 Using the contingency chi?]square test to
compare observed proportions 266 18.2 Extent of change in proportion with
an expulsion - Clinically significant? 270 18.3 Larger tables - Attendance
at diabetic clinics 270 18.4 Planning experimental size 273 18.5 Fisher's
exact test 275 18.6 McNemar's test 277 18.7 Chapter summary 279 18.8
Appendix 280 19 Relative risk, odds ratio and number needed to treat 283
19.1 Measures of treatment effect - relative risk, odds ratio and number
needed to treat 283 19.2 Similarity between relative risk and odds ratio
287 19.3 Interpreting the various measures 288 19.4 95% confidence
intervals for measures of effect size 289 19.5 Chapter summary 293 20
Logistic regression 295 20.1 Modelling a binary outcome 295 20.2 Additional
predictors and the problem of confounding 304 20.3 Analysis by computer
package 307 20.4 Extending logistic regression beyond dichotomous outcomes
308 20.5 Chapter summary 309 20.6 Appendix 309 PART 4 ORDINAL?]SCALE DATA
311 21 Ordinal and non?]normally distributed data: Transformations and
non?]parametric tests 313 21.1 Transforming data to a normal distribution
314 21.2 The Mann-Whitney test - a non?]parametric method 318 21.3 Dealing
with ordinal data 323 21.4 Other non?]parametric methods 325 21.5 Chapter
summary 333 21.6 Appendix 334 PART 5 OTHER TOPICS 337 22 Measures of
agreement 339 22.1 Answers to several questions 340 22.2 Several answers to
one question - do they agree? 344 22.3 Chapter summary 358 23 Survival
analysis 361 23.1 What special problems arise with survival data? 362 23.2
Kaplan-Meier survival estimation 363 23.3 Declining sample sizes in
survival studies 369 23.4 Precision of sampling estimates of survival 369
23.5 Indicators of survival 371 23.6 Testing for differences in survival
374 23.7 Chapter summary 383 24 Multiple testing 385 24.1 What is it and
why is it a problem? 385 24.2 Where does multiple testing arise? 386 24.3
Methods to avoid false positives 388 24.4 The role of scientific journals
392 24.5 Chapter summary 393 25 Questionnaires 395 25.1 Types of questions
396 25.2 Sample sizes and low return rates 398 25.3 Analysing the results
399 25.4 Problem number two: Confounded questionnaire data 401 25.5 Problem
number three: Multiple testing with questionnaire data 401 25.6 Chapter
summary 403 Index 405