Statistical Thinking for Non-Statisticians in Drug Regulation
(Sprache: Englisch)
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...
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...
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Klappentext zu „Statistical Thinking for Non-Statisticians in Drug Regulation “
STATISTICAL THINKING FOR NON-STATISTICIANS IN DRUG REGULATIONStatistical 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
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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.
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Inhaltsverzeichnis zu „Statistical Thinking for Non-Statisticians in Drug Regulation “
Preface to the second edition, xvPreface 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,
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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
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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
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Autoren-Porträt von Richard Kay
Richard Kay, PhD is a Visiting Professor at the School of Pharmacy and Pharmaceutical Medicine, Cardiff University, UK, and a longtime statistical consultant for the pharmaceutical industry. He provides consultancy and training services for pharmaceutical companies and research institutions.
Bibliographische Angaben
- Autor: Richard Kay
- 2022, 3. Aufl., 432 Seiten, Maße: 17,5 x 25 cm, Gebunden, Englisch
- Verlag: Wiley & Sons
- ISBN-10: 111986738X
- ISBN-13: 9781119867388
- Erscheinungsdatum: 03.01.2023
Sprache:
Englisch
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