| Foreword |
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xi | |
| Preface |
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xiii | |
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1 Statistical approaches for clinical trials |
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1 | (18) |
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1 | (3) |
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1.2 Comparisons between Bayesian and frequentist approaches |
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4 | (2) |
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1.3 Adaptivity in clinical trials |
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6 | (2) |
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1.4 Features and use of the Bayesian adaptive approach |
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8 | (11) |
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1.4.1 The fully Bayesian approach |
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8 | (2) |
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1.4.2 Bayes as a frequentist tool |
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10 | (2) |
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1.4.3 Examples of the Bayesian approach to drug and medical device development |
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12 | (7) |
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2 Basics of Bayesian inference |
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19 | (68) |
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2.1 Introduction to Bayes' Theorem |
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19 | (7) |
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26 | (16) |
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26 | (1) |
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2.2.2 Interval estimation |
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27 | (2) |
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2.2.3 Hypothesis testing and model choice |
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29 | (5) |
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34 | (3) |
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2.2.5 Effect of the prior: sensitivity analysis |
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37 | (1) |
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2.2.6 Role of randomization |
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38 | (2) |
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2.2.7 Handling multiplicities |
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40 | (2) |
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42 | (9) |
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44 | (1) |
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2.3.2 The Metropolis-Hastings algorithm |
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45 | (3) |
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2.3.3 Convergence diagnosis |
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48 | (1) |
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2.3.4 Variance estimation |
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49 | (2) |
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2.4 Hierarchical modeling and metaanalysis |
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51 | (12) |
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2.5 Principles of Bayesian clinical trial design |
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63 | (23) |
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2.5.1 Bayesian predictive probability methods |
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64 | (2) |
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2.5.2 Bayesian indifference zone methods |
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66 | (2) |
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2.5.3 Prior determination |
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68 | (2) |
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2.5.4 Operating characteristics |
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70 | (8) |
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2.5.5 Incorporating costs |
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78 | (3) |
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81 | (1) |
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2.5.7 Noncompliance and causal modeling |
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82 | (4) |
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86 | (1) |
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87 | (50) |
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3.1 Rule-based designs for determining the MTD |
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88 | (5) |
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3.1.1 Traditional 3+3 design |
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88 | (3) |
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3.1.2 Pharmacologically guided dose escalation |
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91 | (1) |
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3.1.3 Accelerated titration designs |
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92 | (1) |
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3.1.4 Other rule-based designs |
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92 | (1) |
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3.1.5 Summary of rule-based designs |
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92 | (1) |
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3.2 Model-based designs for determining the MTD |
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93 | (23) |
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3.2.1 Continual reassessment method (CRM) |
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94 | (8) |
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3.2.2 Escalation with overdose control (EWOC) |
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102 | (3) |
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3.2.3 Time-to-event (TITE) monitoring |
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105 | (4) |
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109 | (4) |
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3.2.5 Ordinal toxicity intervals |
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113 | (3) |
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3.3 Efficacy versus toxicity |
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116 | (5) |
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117 | (1) |
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3.3.2 Joint probability model for efficacy and toxicity |
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117 | (1) |
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3.3.3 Defining the acceptable dose levels |
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118 | (1) |
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3.3.4 Efficacy-toxicity trade-off contours |
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118 | (3) |
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121 | (13) |
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122 | (4) |
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126 | (1) |
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3.4.3 Combination therapy with bivariate response |
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127 | (2) |
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3.4.4 Dose escalation with two agents |
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129 | (5) |
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134 | (3) |
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137 | (56) |
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137 | (5) |
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138 | (2) |
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140 | (2) |
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4.1.3 Limitations of traditional frequentist designs |
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142 | (1) |
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4.2 Predictive probability |
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142 | (8) |
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4.2.1 Definition and basic calculations for binary data |
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143 | (3) |
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4.2.2 Derivation of the predictive process design |
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146 | (4) |
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150 | (5) |
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4.3.1 Binary stopping for futility and efficacy |
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150 | (1) |
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4.3.2 Binary stopping for futility, efficacy, and toxicity |
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151 | (3) |
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4.3.3 Monitoring event times |
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154 | (1) |
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4.4 Adaptive randomization and dose allocation |
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155 | (18) |
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4.4.1 Principles of adaptive randomization |
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155 | (8) |
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4.4.2 Dose ranging and optimal biologic dosing |
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163 | (4) |
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4.4.3 Adaptive randomization in dose finding |
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167 | (1) |
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4.4.4 Outcome adaptive randomization with delayed survival response |
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168 | (5) |
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4.5 Hierarchical models for phase II designs |
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173 | (3) |
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4.6 Decision theoretic designs |
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176 | (7) |
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4.6.1 Utility functions and their specification |
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176 | (3) |
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4.6.2 Screening designs for drug development |
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179 | (4) |
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4.7 Case studies in phase II adaptive design |
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183 | (8) |
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183 | (6) |
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189 | (2) |
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191 | (2) |
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193 | (56) |
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5.1 Introduction to confirmatory studies |
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193 | (2) |
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5.2 Bayesian adaptive confirmatory trials |
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195 | (13) |
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5.2.1 Adaptive sample size using posterior probabilities |
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196 | (4) |
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5.2.2 Futility analyses using predictive probabilities |
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200 | (4) |
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5.2.3 Handling delayed outcomes |
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204 | (4) |
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208 | (3) |
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5.4 Modeling and prediction |
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211 | (7) |
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5.5 Prior distributions and the paradigm clash |
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218 | (3) |
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5.6 Phase III cancer trials |
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221 | (7) |
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5.7 Phase II/III seamless trials |
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228 | (13) |
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5.7.1 Example phase II/III trial |
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230 | (1) |
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231 | (1) |
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5.7.3 Statistical modeling |
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232 | (1) |
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233 | (2) |
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235 | (6) |
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5.8 Case study: Ablation device to treat atrial fibrillation |
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241 | (6) |
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247 | (2) |
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249 | (32) |
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6.1 Incorporating historical data |
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249 | (11) |
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6.1.1 Standard hierarchical models |
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250 | (2) |
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6.1.2 Hierarchical power prior models |
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252 | (8) |
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260 | (8) |
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6.2.1 Statistical issues in bioequivalence |
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261 | (2) |
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6.2.2 Binomial response design |
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263 | (2) |
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6.2.3 2 x 2 crossover design |
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265 | (3) |
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268 | (8) |
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6.3.1 Assessing drug safety |
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269 | (6) |
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6.3.2 Multiplicities and false discovery rate (FDR) |
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275 | (1) |
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276 | (4) |
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276 | (1) |
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6.4.2 Bayesian decision theoretic approach |
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277 | (3) |
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280 | (1) |
| References |
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281 | (16) |
| Author index |
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297 | (6) |
| Index |
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303 | |