Buch, Englisch, 432 Seiten, Format (B × H): 175 mm x 250 mm, Gewicht: 926 g
Buch, Englisch, 432 Seiten, Format (B × H): 175 mm x 250 mm, Gewicht: 926 g
ISBN: 978-1-119-86738-8
Verlag: Wiley
STATISTICAL THINKING FOR NON-STATISTICIANS IN DRUG REGULATION
Statistical methods in the pharmaceutical industry are accepted as a key element in the design and analysis of clinical studies. Increasingly, the medical and scientific community are aligning with the regulatory authorities and recognizing that correct statistical methodology is essential as the basis for valid conclusions. In order for those correct and robust methods to be successfully employed there needs to be effective communication across disciplines at all stages of the planning, conducting, analyzing and reporting of clinical studies associated with the development and evaluation of new drugs and devices.
Statistical Thinking for Non-Statisticians in Drug Regulation provides a comprehensive in-depth guide to statistical methodology for pharmaceutical industry professionals, including physicians, investigators, medical science liaisons, clinical research scientists, medical writers, regulatory personnel, statistical programmers, senior data managers and those working in pharmacovigilance. The author’s years of experience and up-to-date familiarity with pharmaceutical regulations and statistical practice within the wider clinical community make this an essential guide for the those working in and with the industry.
The third edition of Statistical Thinking for Non-Statisticians in Drug Regulation includes:
- A detailed new chapter on Estimands in line with the 2019 Addendum to ICH E9
- Major new sections on topics including Combining Hierarchical Testing and Alpha Adjustment, Biosimilars, Restricted Mean Survival Time, Composite Endpoints and Cumulative Incidence Functions, Adjusting for Cross-Over in Oncology, Inverse Propensity Score Weighting, and Network Meta-Analysis
- Updated coverage of many existing topics to reflect new and revised guidance from regulatory authorities and author experience
Statistical Thinking for Non-Statisticians in Drug Regulation is a valuable guide for pharmaceutical and medical device industry professionals, as well as statisticians joining the pharmaceutical industry and students and teachers of drug development.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Preface to the second edition, xv
Preface to the first edition, xvii
Abbreviations, xxi
1 Basic ideas in clinical trial design, 1
1.1 Historical perspective, 1
1.2 Control groups, 2
1.3 Placebos and blinding, 3
1.4 Randomisation, 3
1.4.1 Unrestricted randomisation, 4
1.4.2 Block randomisation, 4
1.4.3 Unequal randomisation, 5
1.4.4 Stratified randomisation, 6
1.4.5 Central randomisation, 7
1.4.6 Dynamic allocation and minimisation, 8
1.4.7 Cluster randomisation, 9
1.5 Bias and precision, 9
1.6 Between- and within-patient designs, 11
1.7 Crossover trials, 12
1.8 Signal, noise and evidence, 13
1.8.1 Signal, 13
1.8.2 Noise, 13
1.8.3 Signal-to-noise ratio, 14
1.9 Confirmatory and exploratory trials, 15
1.10 Superiority, equivalence and non-inferiority trials, 16
1.11 Data and endpoint types, 17
1.12 Choice of endpoint, 18
1.12.1 Primary variables, 18
1.12.2 Secondary variables, 19
1.12.3 Surrogate variables, 20
1.12.4 Global assessment variables, 21
1.12.5 Composite variables, 21
1.12.6 Categorisation, 21
2 Sampling and inferential statistics, 23
2.1 Sample and population, 23
2.2 Sample statistics and population parameters, 24
2.2.1 Sample and population distribution, 24
2.2.2 Median and mean, 25
2.2.3 Standard deviation, 25
2.2.4 Notation, 26
2.2.5 Box plots, 27
2.3 The normal distribution, 28
2.4 Sampling and the standard error of the mean, 31
2.5 Standard errors more generally, 34
2.5.1 The standard error for the difference between two means, 34
2.5.2 Standard errors for proportions, 37
2.5.3 The general setting, 37
3 Confidence intervals and p-values, 38
3.1 Confidence intervals for a single mean, 38
3.1.1 The 95 per cent Confidence interval, 38
3.1.2 Changing the confidence coefficient, 40
3.1.3 Changing the multiplying constant, 40
3.1.4 The role of the standard error, 41
3.2 Confidence interval for other parameters, 42
3.2.1 Difference between two means, 42
3.2.2 Confidence interval for proportions, 43
3.2.3 General case, 44
3.2.4 Bootstrap Confidence interval, 45
3.3 Hypothesis testing, 45
3.3.1 Interpreting the p-value, 46
3.3.2 Calculating the p-value, 47
3.3.3 A common process, 50
3.3.4 The language of statistical significance, 53
3.3.5 One-sided and two-sided tests, 54
4 Tests for simple treatment comparisons, 56
4.1 The unpaired t-test, 56
4.2 The paired t-test, 57
4.3 Interpreting the t-tests, 60
4.4 The chi-square test for binary data, 61
4.4.1 Pearson chi-square, 61
4.4.2 The link to a ratio of the signal to the standard error, 64
4.5 Measures of treatment benefit, 64
4.5.1 Odds ratio, 65
4.5.2 Relative risk, 65
4.5.3 Relative risk reduction, 66
4.5.4 Number needed to treat, 66
4.5.5 Confidence intervals, 67
4.5.6 Interpretation, 68
4.6 Fisher’s exact test, 69
4.7 Tests for categorical and ordinal data, 71
4.7.1 Categorical data, 71
4.7.2 Ordered categorical (ordinal) data, 73
4.7.3 Measures of treatment benefit, 74
4.8 Extensions for multiple treatment groups, 75
4.8.1 Between-patient designs and continuous data, 75
4.8.2 Within-patient designs and continuous data, 76
4.8.3 Binary, categorical and ordinal data, 76
4.8.4 Dose-ranging studies, 77
4.8.5 Further discussion, 77
5 Adjusting the analysis, 78
5.1 Objectives for adjusted analysis, 78
5.2 Comparing treatments for continuous data, 78
5.3 Least squares means, 82
5.4 Evaluating the homogeneity of the treatment effect, 83
5.4.1 Treatment-by-factor interactions, 83
5.4.2 Quantitative and qualitative interactions, 85
5.5 Methods for binary, categorical and ordinal data, 86
5.6 Multi-centre trials, 87
5.6.1 Adjusting for centre, 87
5.6.2 Significant treatment-by-centre interactions, 87
5.6.3 Combining centres, 88
6 Regression and analysis of covariance, 89
6.1 Adjusting for baseline factors, 89
6.2 Simple linear regression, 89
6.3 Multiple regression, 91
6.4 Logistic regression, 94
6.5 Analysis of covariance for continuous data, 94
6.5.1 Main effect of treatment, 94
6.5.2 Treatment-by-covariate interactions, 96
6.5.3 A single model, 98
6.5.4 Connection with adjusted analyses, 98
6.5.5 Advantages of ANCOVA, 99
6.5.6 Least squares means, 100
6.6 Binary, categorical and ordinal data, 101
6.7 Regulatory aspects of the use of covariates, 103
6.8 Baseline testing, 105
7 Intention-to-treat and analysis sets, 107
7.1 The principle of intention-to-treat, 107
7.2 The practice of intention-to-treat, 110
7.2.1 Full analysis set, 110
7.2.2 Per-protocol set, 112
7.2.3 Sensitivity, 112
7.3 Missing data, 113
7.3.1 Introduction, 113
7.3.2 Complete cases analysis, 114
7.3.3 Last observation carried forward, 114
7.3.4 Success/failure classification, 114
7.3.5 Worst-case/best-case classification, 115
7.3.6 Sensitivity, 115
7.3.7 Avoidance of missing data, 116
7.3.8 Multiple imputation, 117
7.4 Intention-to-treat and time-to-event data, 118
7.5 General questions and considerations, 120
8 Power and sample size, 123
8.1 Type I and type II errors, 123
8.2 Power, 124
8.3 Calculating sample size, 127
8.4 Impact of changing the parameters, 130
8.4.1 Standard deviation, 130
8.4.2 Event rate in the control group, 130
8.4.3 Clinically relevant difference, 131
8.5 Regulatory aspects, 132
8.5.1 Power >80 per cent, 132
8.5.2 Powering on the per-protocol set, 132
8.5.3 Sample size adjustment, 133
8.6 Reporting the sample size calculation, 134
9 Statistical significance and clinical importance, 136
9.1 Link between p-values and Confidence intervals, 136
9.2 Confidence intervals for clinical importance, 137
9.3 Misinterpretation of the p-value, 139
9.3.1 Conclusions of similarity, 139
9.3.2 The problem with 0.05, 140
9.4 Single pivotal trial and 0.05, 140
10 Multiple testing, 142
10.1 Inflation of the type I error, 142
10.1.1 False positives, 142
10.1.2 A simulated trial, 142
10.2 How does multiplicity arise?, 143
10.3 Regulatory view, 144
10.4 Multiple primary endpoints, 145
10.4.1 Avoiding adjustment, 145
10.4.2 Significance needed on all endpoints, 145
10.4.3 Composite endpoints, 146
10.4.4 Variables ranked according to clinical importance: Hierarchical testing, 146
10.5 Methods for a
