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. INDICE: 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 Two–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 Describing 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 000
- ISBN: 978-1-118-91339-0
- Editorial: Wiley–Blackwell
- Encuadernacion: Rústica
- Páginas: 440
- Fecha Publicación: 09/10/2015
- Nº Volúmenes: 1
- Idioma: Inglés