Essential statistics for the pharmaceutical sciences:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Chichester [u.a.]
Wiley
2007
|
| Subjects: | |
| Online Access: | Table of contents only Inhaltsverzeichnis |
| Physical Description: | 336 S. graph. Darst. |
| ISBN: | 9780470034682 |
Staff View
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| adam_text | Contents
Preface xiii
Statistical packages xix
PART 1: DATA TYPES 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
PART 2: INTERVAL SCALE DATA 7
2 Descriptive statistics 9
2.1 Summarizing data sets 9
2.2 Indicators of central tendency mean, median and mode 10
2.3 Describing variability standard deviation and coefficient
of variation 16
2.4 Quartiles another way to describe data 20
2.5 Using computer packages to generate descriptive statistics 23
2.6 Chapter summary 25
3 The normal distribution 27
3.1 What is a normal distribution? 27
3.2 Identifying data that are not normally distributed 28
3.3 Proportions of individuals within one or two standard
deviations of the mean 31
3.4 Chapter summary 34
4 Sampling from populations the SEM 35
4.1 Samples and populations 35
4.2 From sample to population 36
4.3 Types of sampling error 37
4.4 What factors control the extent of random sampling error? 39
viii CONTENTS
4.5 Estimating likely sampling error The SEM 42
4.6 Offsetting sample size against standard deviation 46
4.7 Chapter summary 46
5 Ninety five per cent confidence interval for the mean 49
5.1 What is a confidence interval? 50
5.2 How wide should the interval be? 50
5.3 What do we mean by 95 percent confidence? 51
5.4 Calculating the interval width 52
5.5 A long series of samples and 95 percent confidence intervals 53
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? 54
5.7 Two statements 56
5.8 One sided 95 per cent confidence intervals 57
5.9 The 95 percent confidence interval for the difference between
two treatments 60
5.10 The need for data to follow a normal distribution and data
transformation 61
5.11 Chapter summary 65
6 The two sample t test (1). Introducing hypothesis tests 67
6.1 The two sample t test an example of a hypothesis test 68
6.2 Significance 74
6.3 The risk of a false positive finding 75
6.4 What factors will influence whether or not we obtain a
significant outcome? 76
6.5 Requirements for applying a two sample f test 79
6.6 Chapter summary 80
7 The two sample t test (2). The dreaded P value 83
7.1 Measuring how significant a result is 83
7.2 P values 84
7.3 Two ways to define significance? 85
7.4 Obtaining the P value 86
7.5 P values or 95 percent confidence intervals? 86
7.6 Chapter summary 87
8 The two sample t test (3). False negatives, power and
necessary sample sizes 89
8.1 What else could possibly go wrong? 90
8.2 Power 91
8.3 Calculating necessary sample size 94
8.4 Chapter summary 101
9 The two sample t test (4). Statistical significance, practical
significance and equivalence 103
9.1 Practical significance is the difference big enough
to matter? 104
CONTENTS ix
9.2 Equivalence testing 107
9.3 Non inferiority testing 111
9.4 P values are less informative and can be positively misleading 113
9.5 Setting equivalence limits prior to experimentation 115
9.6 Chapter summary 116
10 The two sample t test (5). One sided testing 117
10.1 Looking for a change in a specified direction 118
10.2 Protection against false positives 120
10.3 Temptation! 121
10.4 Using a computer package to carry out a one sided test 125
10.5 Should one sided tests be used more commonly? 126
10.5 Chapter summary 126
11 What does a statistically significant result really tell us? 127
11.1 Interpreting statistical significance 127
11.2 Starting from extreme scepticism 131
11.3 Chapter summary 132
12 The paired t test comparing two related sets of
measurements 133
12.1 Paired data 133
12.2 We could analyse the data using a two sample t test 135
12.3 Using a paired t test instead 135
12.4 Performing a paired t test 136
12.5 What determines whether a paired t test will be significant? 138
12.6 Greater power of the paired t test 139
12.7 The paired t test is only applicable to naturally paired data 139
12.8 Choice of experimental design 140
12.9 Requirements for applying a paired t test 141
12.10 Sample sizes, practical significance and one sided tests 141
12.11 Summarizing the differences between the paired and
two sample t tests 143
12.12 Chapter summary 144
13 Analyses of variance going beyond t tests 145
13.1 Extending the complexity of experimental designs 146
13.2 One way analysis of variance 146
13.3 Two way analysis of variance 156
13.4 Multi factorial experiments 164
13.5 Keep it simple Keep it powerful 165
13.6 Chapter summary 167
14 Correlation and regression relationships between
measured values 169
14.1 Correlation analysis 170
14.2 Regression analysis 178
x CONTENTS
14.3 Multiple regression 185
14.4 Chapter summary 192
PART 3: NOMINAL SCALE DATA 195
15 Describing categorized data 197
15.1 Descriptive statistics 198
15.2 Testing whether the population proportion might credibly
be some pre determined figure 202
15.3 Chapter summary 207
16 Comparing observed proportions the contingency
chi square test 209
16.1 Using the contingency chi square test to compare
observed proportions 210
16.2 Obtaining a 95 percent CI for the change in the proportion of
expulsions is the difference large enough to be of practical
significance? 213
16.3 Larger tables attendance at diabetic clinics 214
16.4 Planning experimental size 217
16.5 Chapter summary 219
PART 4: ORDINAL SCALE DATA 221
17 Ordinal and non normally distributed data. Transformations
and non parametric tests 223
17.1 Transforming data to a normal distribution 224
17.2 The Mann Whitney test a non parametric method 228
17.3 Dealing with ordinal data 233
17.4 Other non parametric methods 235
17.5 Chapter summary 242
Appendix to chapter 17 242
PART 5: SOME CHALLENGES FROM THE REAL WORLD 245
18 Multiple testing 247
18.1 What is it and why is it a problem? 247
18.2 Where does multiple testing arise? 248
18.3 Methods to avoid false positives 250
18.4 The role of scientific journals 254
18.5 Chapter summary 255
19 Questionnaires 257
19.1 Is there anything special about questionnaires? 258
19.2 Types of questions 258
19.3 Designing a questionnaire 262
19.4 Sample sizes and return rates 263
CONTENTS xi
19.5 Analysing the results 265
19.6 Confounded epidemiological data 266
19.7 Multiple testing with questionnaire data 271
19.8 Chapter summary 272
PART 6: CONCLUSIONS 275
20 Conclusions 277
20.1 Be clear about the purpose of the experiment 277
20.2 Keep the experimental design simple and therefore
clear and powerful 278
20.3 Draw up a statistical analysis plan as part of the experimental
design it is not a last minute add on 279
20.4 Explore your data visually before launching into
statistical testing 280
20.5 Beware of multiple analyses 281
20.6 Interpret both significance and non significance with care 282
Index 283
|
| adam_txt |
Contents
Preface xiii
Statistical packages xix
PART 1: DATA TYPES 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
PART 2: INTERVAL SCALE DATA 7
2 Descriptive statistics 9
2.1 Summarizing data sets 9
2.2 Indicators of central tendency mean, median and mode 10
2.3 Describing variability standard deviation and coefficient
of variation 16
2.4 Quartiles another way to describe data 20
2.5 Using computer packages to generate descriptive statistics 23
2.6 Chapter summary 25
3 The normal distribution 27
3.1 What is a normal distribution? 27
3.2 Identifying data that are not normally distributed 28
3.3 Proportions of individuals within one or two standard
deviations of the mean 31
3.4 Chapter summary 34
4 Sampling from populations the SEM 35
4.1 Samples and populations 35
4.2 From sample to population 36
4.3 Types of sampling error 37
4.4 What factors control the extent of random sampling error? 39
viii CONTENTS
4.5 Estimating likely sampling error The SEM 42
4.6 Offsetting sample size against standard deviation 46
4.7 Chapter summary 46
5 Ninety five per cent confidence interval for the mean 49
5.1 What is a confidence interval? 50
5.2 How wide should the interval be? 50
5.3 What do we mean by '95 percent' confidence? 51
5.4 Calculating the interval width 52
5.5 A long series of samples and 95 percent confidence intervals 53
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? 54
5.7 Two statements 56
5.8 One sided 95 per cent confidence intervals 57
5.9 The 95 percent confidence interval for the difference between
two treatments 60
5.10 The need for data to follow a normal distribution and data
transformation 61
5.11 Chapter summary 65
6 The two sample t test (1). Introducing hypothesis tests 67
6.1 The two sample t test an example of a hypothesis test 68
6.2 'Significance' 74
6.3 The risk of a false positive finding 75
6.4 What factors will influence whether or not we obtain a
significant outcome? 76
6.5 Requirements for applying a two sample f test 79
6.6 Chapter summary 80
7 The two sample t test (2). The dreaded P value 83
7.1 Measuring how significant a result is 83
7.2 P values 84
7.3 Two ways to define significance? 85
7.4 Obtaining the P value 86
7.5 P values or 95 percent confidence intervals? 86
7.6 Chapter summary 87
8 The two sample t test (3). False negatives, power and
necessary sample sizes 89
8.1 What else could possibly go wrong? 90
8.2 Power 91
8.3 Calculating necessary sample size 94
8.4 Chapter summary 101
9 The two sample t test (4). Statistical significance, practical
significance and equivalence 103
9.1 Practical significance is the difference big enough
to matter? 104
CONTENTS ix
9.2 Equivalence testing 107
9.3 Non inferiority testing 111
9.4 P values are less informative and can be positively misleading 113
9.5 Setting equivalence limits prior to experimentation 115
9.6 Chapter summary 116
10 The two sample t test (5). One sided testing 117
10.1 Looking for a change in a specified direction 118
10.2 Protection against false positives 120
10.3 Temptation! 121
10.4 Using a computer package to carry out a one sided test 125
10.5 Should one sided tests be used more commonly? 126
10.5 Chapter summary 126
11 What does a statistically significant result really tell us? 127
11.1 Interpreting statistical significance 127
11.2 Starting from extreme scepticism 131
11.3 Chapter summary 132
12 The paired t test comparing two related sets of
measurements 133
12.1 Paired data 133
12.2 We could analyse the data using a two sample t test 135
12.3 Using a paired t test instead 135
12.4 Performing a paired t test 136
12.5 What determines whether a paired t test will be significant? 138
12.6 Greater power of the paired t test 139
12.7 The paired t test is only applicable to naturally paired data 139
12.8 Choice of experimental design 140
12.9 Requirements for applying a paired t test 141
12.10 Sample sizes, practical significance and one sided tests 141
12.11 Summarizing the differences between the paired and
two sample t tests 143
12.12 Chapter summary 144
13 Analyses of variance going beyond t tests 145
13.1 Extending the complexity of experimental designs 146
13.2 One way analysis of variance 146
13.3 Two way analysis of variance 156
13.4 Multi factorial experiments 164
13.5 Keep it simple Keep it powerful 165
13.6 Chapter summary 167
14 Correlation and regression relationships between
measured values 169
14.1 Correlation analysis 170
14.2 Regression analysis 178
x CONTENTS
14.3 Multiple regression 185
14.4 Chapter summary 192
PART 3: NOMINAL SCALE DATA 195
15 Describing categorized data 197
15.1 Descriptive statistics 198
15.2 Testing whether the population proportion might credibly
be some pre determined figure 202
15.3 Chapter summary 207
16 Comparing observed proportions the contingency
chi square test 209
16.1 Using the contingency chi square test to compare
observed proportions 210
16.2 Obtaining a 95 percent CI for the change in the proportion of
expulsions is the difference large enough to be of practical
significance? 213
16.3 Larger tables attendance at diabetic clinics 214
16.4 Planning experimental size 217
16.5 Chapter summary 219
PART 4: ORDINAL SCALE DATA 221
17 Ordinal and non normally distributed data. Transformations
and non parametric tests 223
17.1 Transforming data to a normal distribution 224
17.2 The Mann Whitney test a non parametric method 228
17.3 Dealing with ordinal data 233
17.4 Other non parametric methods 235
17.5 Chapter summary 242
Appendix to chapter 17 242
PART 5: SOME CHALLENGES FROM THE REAL WORLD 245
18 Multiple testing 247
18.1 What is it and why is it a problem? 247
18.2 Where does multiple testing arise? 248
18.3 Methods to avoid false positives 250
18.4 The role of scientific journals 254
18.5 Chapter summary 255
19 Questionnaires 257
19.1 Is there anything special about questionnaires? 258
19.2 Types of questions 258
19.3 Designing a questionnaire 262
19.4 Sample sizes and return rates 263
CONTENTS xi
19.5 Analysing the results 265
19.6 Confounded epidemiological data 266
19.7 Multiple testing with questionnaire data 271
19.8 Chapter summary 272
PART 6: CONCLUSIONS 275
20 Conclusions 277
20.1 Be clear about the purpose of the experiment 277
20.2 Keep the experimental design simple and therefore
clear and powerful 278
20.3 Draw up a statistical analysis plan as part of the experimental
design it is not a last minute add on 279
20.4 Explore your data visually before launching into
statistical testing 280
20.5 Beware of multiple analyses 281
20.6 Interpret both significance and non significance with care 282
Index 283 |
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| discipline | Chemie / Pharmazie Medizin |
| discipline_str_mv | Chemie / Pharmazie Medizin |
| format | Book |
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| publisher | Wiley |
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| spelling | Rowe, Philip Verfasser (DE-588)1025609786 aut Essential statistics for the pharmaceutical sciences Philip Rowe Chichester [u.a.] Wiley 2007 336 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Statistik Drugs Research Statistical methods Pharmacology Statistical methods Pharmacology methods Statistics Pharmazie (DE-588)4045705-9 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Biostatistik (DE-588)4729990-3 gnd rswk-swf 1\p (DE-588)4123623-3 Lehrbuch gnd-content Pharmazie (DE-588)4045705-9 s Statistik (DE-588)4056995-0 s 2\p DE-604 Biostatistik (DE-588)4729990-3 s 3\p DE-604 http://www.loc.gov/catdir/toc/ecip077/2006102028.html Table of contents only HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015713418&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Rowe, Philip Essential statistics for the pharmaceutical sciences Statistik Drugs Research Statistical methods Pharmacology Statistical methods Pharmacology methods Statistics Pharmazie (DE-588)4045705-9 gnd Statistik (DE-588)4056995-0 gnd Biostatistik (DE-588)4729990-3 gnd |
| subject_GND | (DE-588)4045705-9 (DE-588)4056995-0 (DE-588)4729990-3 (DE-588)4123623-3 |
| title | Essential statistics for the pharmaceutical sciences |
| title_auth | Essential statistics for the pharmaceutical sciences |
| title_exact_search | Essential statistics for the pharmaceutical sciences |
| title_exact_search_txtP | Essential statistics for the pharmaceutical sciences |
| title_full | Essential statistics for the pharmaceutical sciences Philip Rowe |
| title_fullStr | Essential statistics for the pharmaceutical sciences Philip Rowe |
| title_full_unstemmed | Essential statistics for the pharmaceutical sciences Philip Rowe |
| title_short | Essential statistics for the pharmaceutical sciences |
| title_sort | essential statistics for the pharmaceutical sciences |
| topic | Statistik Drugs Research Statistical methods Pharmacology Statistical methods Pharmacology methods Statistics Pharmazie (DE-588)4045705-9 gnd Statistik (DE-588)4056995-0 gnd Biostatistik (DE-588)4729990-3 gnd |
| topic_facet | Statistik Drugs Research Statistical methods Pharmacology Statistical methods Pharmacology methods Statistics Pharmazie Biostatistik Lehrbuch |
| url | http://www.loc.gov/catdir/toc/ecip077/2006102028.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015713418&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT rowephilip essentialstatisticsforthepharmaceuticalsciences |