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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.
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.
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.
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.
Über den Autor
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.
Inhaltsverzeichnis
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 adjustment, 149
10.5.1 Bonferroni correction, 149
10.5.2 Hochberg correction, 150
10.5.3 Interim analyses, 151
10.6 Multiple comparisons, 152
10.7 Repeated evaluation over time, 153
10.8 Subgroup testing, 154
10.9 Other areas for multiplicity, 156
10.9.1 Using different statistical tests, 156
10.9.2 Different analysis sets, 156
10.9.3 Pre-planning, 157
11 Non-parametric and related methods, 158
11.1 Assumptions underlying the t-tests and their extensions, 158
11.2 Homogeneity of variance, 158
11.3 The assumption of normality, 159
11.4 Non-normality and transformations, 161
11.5 Non-parametric tests, 164
11.5.1 The Mann-Whitney U-test, 164
11.5.2 The Wilcoxon signed rank test, 166
11.5.3 General comments, 167
11.6 Advantages and disadvantages of non-parametric methods, 168
11.7 Outliers, 169
12 Equivalence and non-inferiority, 170
12.1 Demonstrating similarity, 170
12.2 Confidence intervals for equivalence, 172
12.3 Confidence intervals for non-inferiority, 173
12.4 A p-value approach, 174
12.5 Assay sensitivity, 176
12.6 Analysis sets, 178
12.7 The choice of Delta, 179
12.7.1 Bioequivalence, 179
12.7.2 Therapeutic equivalence, 180
12.7.3 Non-inferiority, 180
12.7.4 The 10 per cent rule for cure rates, 182
12.7.5 The synthesis method, 183
12.8 Biocreep and constancy, 184
12.9 Sample size calculations, 184
12.10 Switching between non-inferiority and superiority, 186
13 The analysis of survival data, 189
13.1 Time-to-event data and censoring, 189
13.2 Kaplan-Meier curves, 190
13.2.1 Plotting Kaplan-Meier curves, 190
13.2.2 Event rates and relative risk, 192
13.2.3 Median event times, 192
13.3 Treatment comparisons, 193
13.4 The hazard ratio, 196
13.4.1 The hazard rate, 196
13.4.2 Constant hazard ratio, 197
13.4.3 Non-constant hazard ratio, 197
13.4.4 Link to survival curves, 198
13.4.5 Calculating Kaplan-Meier curves, 199
13.5 Adjusted analyses, 199
13.5.1 Stratified methods, 200
13.5.2 Proportional hazards regression, 200
13.5.3 Accelerated failure time model, 201
13.6 Independent censoring, 202
13.7 Sample size calculations, 203
14 Interim analysis and data monitoring committees, 205
14.1 Stopping rules for interim analysis, 205
14.2 Stopping for efficacy and futility, 206
14.2.1 Efficacy, 206
14.2.2 Futility and conditional power, 207
14.2.3 Some practical issues, 208
14.2.4 Analyses following completion of recruitment, 209
14.3 Monitoring safety, 210
14.4 Data monitoring committees, 211
14.4.1 Introduction and responsibilities, 211
14.4.2 Structure and process, 212
14.4.3 Meetings and recommendations, 214
15 Bayesian statistics, 215
15.1 Introduction, 215
15.2 Prior and posterior distributions, 215
15.2.1 Prior beliefs, 215
15.2.2 Prior to posterior, 217
15.2.3 Bayes theorem, 217
15.3 Bayesian inference, 219
15.3.1 Frequentist methods, 219
15.3.2 Posterior probabilities, 219
15.3.3 Credible intervals, 220
15.4 Case study, 221
15.5 History and regulatory acceptance, 222
15.6 Discussion, 224
16 Adaptive designs, 225
16.1 What are adaptive designs?, 225
16.1.1 Advantages and drawbacks, 225
16.1.2 Restricted adaptations, 226
16.1.3 Flexible adaptations, 227
16.2 Minimising bias, 228
16.2.1 Control of type I error, 228
16.2.2 Estimation, 229
16.2.3 Behavioural issues, 230
16.2.4 Exploratory trials, 232
16.3 Unblinded sample size re-estimation, 232
16.3.1 Product of p-values, 232
16.3.2 Weighting the two parts of the trial, 233
16.3.3 Rationale, 234
16.4 Seamless phase II/III studies, 234
16.4.1 Standard framework, 234
16.4.2 Aspects of the p-value calculation, 235
16.4.3 Logistical challenges, 236
16.5 Other types of adaptation, 236
16.5.1 Changing the primary endpoint, 236
16.5.2 Focusing on a sub-population, 237
16.5.3 Dropping the...
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 adjustment, 149
10.5.1 Bonferroni correction, 149
10.5.2 Hochberg correction, 150
10.5.3 Interim analyses, 151
10.6 Multiple comparisons, 152
10.7 Repeated evaluation over time, 153
10.8 Subgroup testing, 154
10.9 Other areas for multiplicity, 156
10.9.1 Using different statistical tests, 156
10.9.2 Different analysis sets, 156
10.9.3 Pre-planning, 157
11 Non-parametric and related methods, 158
11.1 Assumptions underlying the t-tests and their extensions, 158
11.2 Homogeneity of variance, 158
11.3 The assumption of normality, 159
11.4 Non-normality and transformations, 161
11.5 Non-parametric tests, 164
11.5.1 The Mann-Whitney U-test, 164
11.5.2 The Wilcoxon signed rank test, 166
11.5.3 General comments, 167
11.6 Advantages and disadvantages of non-parametric methods, 168
11.7 Outliers, 169
12 Equivalence and non-inferiority, 170
12.1 Demonstrating similarity, 170
12.2 Confidence intervals for equivalence, 172
12.3 Confidence intervals for non-inferiority, 173
12.4 A p-value approach, 174
12.5 Assay sensitivity, 176
12.6 Analysis sets, 178
12.7 The choice of Delta, 179
12.7.1 Bioequivalence, 179
12.7.2 Therapeutic equivalence, 180
12.7.3 Non-inferiority, 180
12.7.4 The 10 per cent rule for cure rates, 182
12.7.5 The synthesis method, 183
12.8 Biocreep and constancy, 184
12.9 Sample size calculations, 184
12.10 Switching between non-inferiority and superiority, 186
13 The analysis of survival data, 189
13.1 Time-to-event data and censoring, 189
13.2 Kaplan-Meier curves, 190
13.2.1 Plotting Kaplan-Meier curves, 190
13.2.2 Event rates and relative risk, 192
13.2.3 Median event times, 192
13.3 Treatment comparisons, 193
13.4 The hazard ratio, 196
13.4.1 The hazard rate, 196
13.4.2 Constant hazard ratio, 197
13.4.3 Non-constant hazard ratio, 197
13.4.4 Link to survival curves, 198
13.4.5 Calculating Kaplan-Meier curves, 199
13.5 Adjusted analyses, 199
13.5.1 Stratified methods, 200
13.5.2 Proportional hazards regression, 200
13.5.3 Accelerated failure time model, 201
13.6 Independent censoring, 202
13.7 Sample size calculations, 203
14 Interim analysis and data monitoring committees, 205
14.1 Stopping rules for interim analysis, 205
14.2 Stopping for efficacy and futility, 206
14.2.1 Efficacy, 206
14.2.2 Futility and conditional power, 207
14.2.3 Some practical issues, 208
14.2.4 Analyses following completion of recruitment, 209
14.3 Monitoring safety, 210
14.4 Data monitoring committees, 211
14.4.1 Introduction and responsibilities, 211
14.4.2 Structure and process, 212
14.4.3 Meetings and recommendations, 214
15 Bayesian statistics, 215
15.1 Introduction, 215
15.2 Prior and posterior distributions, 215
15.2.1 Prior beliefs, 215
15.2.2 Prior to posterior, 217
15.2.3 Bayes theorem, 217
15.3 Bayesian inference, 219
15.3.1 Frequentist methods, 219
15.3.2 Posterior probabilities, 219
15.3.3 Credible intervals, 220
15.4 Case study, 221
15.5 History and regulatory acceptance, 222
15.6 Discussion, 224
16 Adaptive designs, 225
16.1 What are adaptive designs?, 225
16.1.1 Advantages and drawbacks, 225
16.1.2 Restricted adaptations, 226
16.1.3 Flexible adaptations, 227
16.2 Minimising bias, 228
16.2.1 Control of type I error, 228
16.2.2 Estimation, 229
16.2.3 Behavioural issues, 230
16.2.4 Exploratory trials, 232
16.3 Unblinded sample size re-estimation, 232
16.3.1 Product of p-values, 232
16.3.2 Weighting the two parts of the trial, 233
16.3.3 Rationale, 234
16.4 Seamless phase II/III studies, 234
16.4.1 Standard framework, 234
16.4.2 Aspects of the p-value calculation, 235
16.4.3 Logistical challenges, 236
16.5 Other types of adaptation, 236
16.5.1 Changing the primary endpoint, 236
16.5.2 Focusing on a sub-population, 237
16.5.3 Dropping the...
Details
| Erscheinungsjahr: | 2023 |
|---|---|
| Fachbereich: | Allgemeine Lexika |
| Genre: | Medizin |
| Rubrik: | Wissenschaften |
| Medium: | Buch |
| Seiten: | 432 |
| Inhalt: | 432 S. |
| ISBN-13: | 9781119867388 |
| ISBN-10: | 111986738X |
| Sprache: | Englisch |
| Herstellernummer: | 1W119867380 |
| Einband: | Gebunden |
| Autor: | Kay, Richard |
| Auflage: | 3rd edition |
| Hersteller: | Open Stax Textbooks |
| Maße: | 250 x 175 x 28 mm |
| Von/Mit: | Richard Kay |
| Erscheinungsdatum: | 28.02.2023 |
| Gewicht: | 0,926 kg |
Über den Autor
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.
Inhaltsverzeichnis
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 adjustment, 149
10.5.1 Bonferroni correction, 149
10.5.2 Hochberg correction, 150
10.5.3 Interim analyses, 151
10.6 Multiple comparisons, 152
10.7 Repeated evaluation over time, 153
10.8 Subgroup testing, 154
10.9 Other areas for multiplicity, 156
10.9.1 Using different statistical tests, 156
10.9.2 Different analysis sets, 156
10.9.3 Pre-planning, 157
11 Non-parametric and related methods, 158
11.1 Assumptions underlying the t-tests and their extensions, 158
11.2 Homogeneity of variance, 158
11.3 The assumption of normality, 159
11.4 Non-normality and transformations, 161
11.5 Non-parametric tests, 164
11.5.1 The Mann-Whitney U-test, 164
11.5.2 The Wilcoxon signed rank test, 166
11.5.3 General comments, 167
11.6 Advantages and disadvantages of non-parametric methods, 168
11.7 Outliers, 169
12 Equivalence and non-inferiority, 170
12.1 Demonstrating similarity, 170
12.2 Confidence intervals for equivalence, 172
12.3 Confidence intervals for non-inferiority, 173
12.4 A p-value approach, 174
12.5 Assay sensitivity, 176
12.6 Analysis sets, 178
12.7 The choice of Delta, 179
12.7.1 Bioequivalence, 179
12.7.2 Therapeutic equivalence, 180
12.7.3 Non-inferiority, 180
12.7.4 The 10 per cent rule for cure rates, 182
12.7.5 The synthesis method, 183
12.8 Biocreep and constancy, 184
12.9 Sample size calculations, 184
12.10 Switching between non-inferiority and superiority, 186
13 The analysis of survival data, 189
13.1 Time-to-event data and censoring, 189
13.2 Kaplan-Meier curves, 190
13.2.1 Plotting Kaplan-Meier curves, 190
13.2.2 Event rates and relative risk, 192
13.2.3 Median event times, 192
13.3 Treatment comparisons, 193
13.4 The hazard ratio, 196
13.4.1 The hazard rate, 196
13.4.2 Constant hazard ratio, 197
13.4.3 Non-constant hazard ratio, 197
13.4.4 Link to survival curves, 198
13.4.5 Calculating Kaplan-Meier curves, 199
13.5 Adjusted analyses, 199
13.5.1 Stratified methods, 200
13.5.2 Proportional hazards regression, 200
13.5.3 Accelerated failure time model, 201
13.6 Independent censoring, 202
13.7 Sample size calculations, 203
14 Interim analysis and data monitoring committees, 205
14.1 Stopping rules for interim analysis, 205
14.2 Stopping for efficacy and futility, 206
14.2.1 Efficacy, 206
14.2.2 Futility and conditional power, 207
14.2.3 Some practical issues, 208
14.2.4 Analyses following completion of recruitment, 209
14.3 Monitoring safety, 210
14.4 Data monitoring committees, 211
14.4.1 Introduction and responsibilities, 211
14.4.2 Structure and process, 212
14.4.3 Meetings and recommendations, 214
15 Bayesian statistics, 215
15.1 Introduction, 215
15.2 Prior and posterior distributions, 215
15.2.1 Prior beliefs, 215
15.2.2 Prior to posterior, 217
15.2.3 Bayes theorem, 217
15.3 Bayesian inference, 219
15.3.1 Frequentist methods, 219
15.3.2 Posterior probabilities, 219
15.3.3 Credible intervals, 220
15.4 Case study, 221
15.5 History and regulatory acceptance, 222
15.6 Discussion, 224
16 Adaptive designs, 225
16.1 What are adaptive designs?, 225
16.1.1 Advantages and drawbacks, 225
16.1.2 Restricted adaptations, 226
16.1.3 Flexible adaptations, 227
16.2 Minimising bias, 228
16.2.1 Control of type I error, 228
16.2.2 Estimation, 229
16.2.3 Behavioural issues, 230
16.2.4 Exploratory trials, 232
16.3 Unblinded sample size re-estimation, 232
16.3.1 Product of p-values, 232
16.3.2 Weighting the two parts of the trial, 233
16.3.3 Rationale, 234
16.4 Seamless phase II/III studies, 234
16.4.1 Standard framework, 234
16.4.2 Aspects of the p-value calculation, 235
16.4.3 Logistical challenges, 236
16.5 Other types of adaptation, 236
16.5.1 Changing the primary endpoint, 236
16.5.2 Focusing on a sub-population, 237
16.5.3 Dropping the...
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 adjustment, 149
10.5.1 Bonferroni correction, 149
10.5.2 Hochberg correction, 150
10.5.3 Interim analyses, 151
10.6 Multiple comparisons, 152
10.7 Repeated evaluation over time, 153
10.8 Subgroup testing, 154
10.9 Other areas for multiplicity, 156
10.9.1 Using different statistical tests, 156
10.9.2 Different analysis sets, 156
10.9.3 Pre-planning, 157
11 Non-parametric and related methods, 158
11.1 Assumptions underlying the t-tests and their extensions, 158
11.2 Homogeneity of variance, 158
11.3 The assumption of normality, 159
11.4 Non-normality and transformations, 161
11.5 Non-parametric tests, 164
11.5.1 The Mann-Whitney U-test, 164
11.5.2 The Wilcoxon signed rank test, 166
11.5.3 General comments, 167
11.6 Advantages and disadvantages of non-parametric methods, 168
11.7 Outliers, 169
12 Equivalence and non-inferiority, 170
12.1 Demonstrating similarity, 170
12.2 Confidence intervals for equivalence, 172
12.3 Confidence intervals for non-inferiority, 173
12.4 A p-value approach, 174
12.5 Assay sensitivity, 176
12.6 Analysis sets, 178
12.7 The choice of Delta, 179
12.7.1 Bioequivalence, 179
12.7.2 Therapeutic equivalence, 180
12.7.3 Non-inferiority, 180
12.7.4 The 10 per cent rule for cure rates, 182
12.7.5 The synthesis method, 183
12.8 Biocreep and constancy, 184
12.9 Sample size calculations, 184
12.10 Switching between non-inferiority and superiority, 186
13 The analysis of survival data, 189
13.1 Time-to-event data and censoring, 189
13.2 Kaplan-Meier curves, 190
13.2.1 Plotting Kaplan-Meier curves, 190
13.2.2 Event rates and relative risk, 192
13.2.3 Median event times, 192
13.3 Treatment comparisons, 193
13.4 The hazard ratio, 196
13.4.1 The hazard rate, 196
13.4.2 Constant hazard ratio, 197
13.4.3 Non-constant hazard ratio, 197
13.4.4 Link to survival curves, 198
13.4.5 Calculating Kaplan-Meier curves, 199
13.5 Adjusted analyses, 199
13.5.1 Stratified methods, 200
13.5.2 Proportional hazards regression, 200
13.5.3 Accelerated failure time model, 201
13.6 Independent censoring, 202
13.7 Sample size calculations, 203
14 Interim analysis and data monitoring committees, 205
14.1 Stopping rules for interim analysis, 205
14.2 Stopping for efficacy and futility, 206
14.2.1 Efficacy, 206
14.2.2 Futility and conditional power, 207
14.2.3 Some practical issues, 208
14.2.4 Analyses following completion of recruitment, 209
14.3 Monitoring safety, 210
14.4 Data monitoring committees, 211
14.4.1 Introduction and responsibilities, 211
14.4.2 Structure and process, 212
14.4.3 Meetings and recommendations, 214
15 Bayesian statistics, 215
15.1 Introduction, 215
15.2 Prior and posterior distributions, 215
15.2.1 Prior beliefs, 215
15.2.2 Prior to posterior, 217
15.2.3 Bayes theorem, 217
15.3 Bayesian inference, 219
15.3.1 Frequentist methods, 219
15.3.2 Posterior probabilities, 219
15.3.3 Credible intervals, 220
15.4 Case study, 221
15.5 History and regulatory acceptance, 222
15.6 Discussion, 224
16 Adaptive designs, 225
16.1 What are adaptive designs?, 225
16.1.1 Advantages and drawbacks, 225
16.1.2 Restricted adaptations, 226
16.1.3 Flexible adaptations, 227
16.2 Minimising bias, 228
16.2.1 Control of type I error, 228
16.2.2 Estimation, 229
16.2.3 Behavioural issues, 230
16.2.4 Exploratory trials, 232
16.3 Unblinded sample size re-estimation, 232
16.3.1 Product of p-values, 232
16.3.2 Weighting the two parts of the trial, 233
16.3.3 Rationale, 234
16.4 Seamless phase II/III studies, 234
16.4.1 Standard framework, 234
16.4.2 Aspects of the p-value calculation, 235
16.4.3 Logistical challenges, 236
16.5 Other types of adaptation, 236
16.5.1 Changing the primary endpoint, 236
16.5.2 Focusing on a sub-population, 237
16.5.3 Dropping the...
Details
| Erscheinungsjahr: | 2023 |
|---|---|
| Fachbereich: | Allgemeine Lexika |
| Genre: | Medizin |
| Rubrik: | Wissenschaften |
| Medium: | Buch |
| Seiten: | 432 |
| Inhalt: | 432 S. |
| ISBN-13: | 9781119867388 |
| ISBN-10: | 111986738X |
| Sprache: | Englisch |
| Herstellernummer: | 1W119867380 |
| Einband: | Gebunden |
| Autor: | Kay, Richard |
| Auflage: | 3rd edition |
| Hersteller: | Open Stax Textbooks |
| Maße: | 250 x 175 x 28 mm |
| Von/Mit: | Richard Kay |
| Erscheinungsdatum: | 28.02.2023 |
| Gewicht: | 0,926 kg |
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