This is the scond book of Lyle Broemeling that I am reviewing for Amazon. I met him at the Joint Statistical Meetings a few years ago when he was just retiring from M.D. Anderson. in recent years M. D. Anderson has become a leader in designing Bayesian adaptive designs of clinical trials. This is mainly due to the leadership of Don Berry who came to head up the biostatistics group at M. D. Anderson several years ago when he was attracted away from Duke. Broemeling benefitted from the arrival of Berry because he was establishe there as a Bayesian and had written a book on Bayesian analysis many years earlier. Now that he is retired from M. D. Anderson he is writing applied biostatistics texts applying Bayesian methods to specialized topics. The first one which I reviewed earlier on amazon was on diagnostic testing and this one is to analyze measures of agreement among judges. The two books are both scholarly written and authoritative and clear. They both also provide many real examples based on Lyle's vast experience at M. D. Anderson.A few years ago I was supporting the company BioImaging in the development of their protocols for medical imaging data from patients in oncology clinical trials. I learned that an important aspect of determining the efficacy of a drug against a particular cancer tumor. This performance is usually measured by individual ranking from radiologist who read the scans over time and assess growth or shrinkage of the tumor after being treated by a drug. Typically there are two or three readers and the rating of progression or remission depends on a concensus of the radiologists assessments.This is exactly the problem Broemeling faced at at M. D. Anderson and he has a wealth of applications in the setting of oncology trials. Broemeling details the history of the develop of methods used to reach a conclusion. He provides a wealth of examples and also includes interesting examples from sports including an analysis of a famous boxing match between Lennox Lewis and Evander Holyfield. He deals methodically with the case of two raters (where an adjudicator general resolve the conflicting cases) and then three or more raters where things get more complicated.Modern Bayesian approaches are demonstrated using the winBugs software. Broemeling provides the code in the winBugs language to handle various examples. This approach involves Markov Chain Monte Carlo methods. Examples are explained in detail and illustrated very carefully.Broemeling also provides a history of the various statistics used to measure agrrement between readers or judges. Another example that struck me as very interesting is a forgery case where a signature was forged to produce a fake will. Usually in forgery cases the methods are used to find differences in the signature that are large enough to assert that they came from different people. However in this example the forged signature was traced from the original persons sample signature. So in the case the objective was to show that the cases are too similar not to have been forged. We are able to do this because we can show repeated signatures from the same hand will have more variability than the traced signature. So in this case the hired statisticians showed that the two signatures are much too similar for the second one to be real and independent of each other.Bayesian sample size estimation is also covered in the text. It is a great reference book for anyone who does oncology trials and appreciates the advantages of the Bayesian approach. The Kappa measure is the one that is given the most attention in the book.
Author(s): Lyle D. Broemeling
Series: Chapman & Hall/CRC Biostatistics Series
Edition: 1
Publisher: Chapman and Hall/CRC
Year: 2009
Language: English
Pages: 335
Tags: Математика;Теория вероятностей и математическая статистика;Математическая статистика;Прикладная математическая статистика;
Cover Page......Page 1
Bayesian Methods for Measures of Agreement......Page 2
Contents......Page 7
Preface......Page 11
Acknowledgments......Page 13
Author......Page 14
1.1 Introduction......Page 15
1.2 Agreement and Statistics......Page 17
1.3 The Bayesian Approach......Page 19
1.4.1 The Lennox Lewis– Evander Holyfield Bout......Page 21
1.4.3 Agreement on Tumor Size......Page 24
1.4.4 Wine Tasting......Page 29
1.4.5 Forgery: Robinson versus Mandell......Page 35
1.5 Sources of Information......Page 36
1.6 Software and Computing......Page 37
1.7 A Preview of the Book......Page 38
Exercises......Page 44
References......Page 45
2.1 Introduction......Page 48
2.2 The Design of Agreement Studies......Page 49
2.3 Precursors of Kappa......Page 51
2.4 Chance Corrected Measures of Agreement......Page 54
2.5 Conditional Kappa......Page 55
2.6 Kappa and Stratification......Page 58
2.7 Weighted Kappa......Page 62
2.8 Intraclass Kappa......Page 64
2.9 Other Measures of Agreement......Page 68
2.10 Agreement with a Gold Standard......Page 72
2.11 Kappa and Association......Page 75
2.12 Consensus......Page 78
Exercises......Page 79
References......Page 82
3.1 Introduction......Page 84
3.2 Kappa with Many Raters......Page 85
3.3 Partial Agreement......Page 91
3.4 Stratified Kappa......Page 96
3.5 Intraclass Kappa......Page 104
3.6 The Fleiss Generalized Kappa......Page 107
3.7 The G Coefficient and Other Indices......Page 110
3.8 Kappa and Homogeneity......Page 112
3.9 Introduction to Model Based Approaches......Page 113
3.10 Agreement and Matching......Page 116
Exercises......Page 117
References......Page 122
4.1 Introduction......Page 123
4.2 An Example of Paired Observations......Page 124
4.3 The Oden Pooled Kappa and Schouten Weighted Kappa......Page 130
4.4 A Generalized Correlation Model......Page 131
4.5 The G Coefficient and Other Indices of Agreement......Page 136
4.6 Homogeneity with Dependent Data......Page 137
4.7 Logistic Regression and Agreement......Page 142
Exercises......Page 150
References......Page 154
5.1 Introduction......Page 156
5.2 Nominal Responses......Page 158
5.3 Ordinal Responses......Page 178
5.4 More than Two Raters......Page 184
5.5 Other Methods for Patterns of Agreement......Page 188
5.6 Summary of Modeling and Agreement......Page 193
Exercises......Page 194
References......Page 202
6.1 Introduction......Page 203
6.2 Regression and Correlation......Page 204
6.3 The Analysis of Variance......Page 209
6.4 Intraclass Correlation Coefficient for Agreement......Page 214
6.5 Agreement with Covariates......Page 219
6.6.1 Introduction......Page 222
6.6.2 Bayesian Bland– Altman......Page 223
6.6.3 More on Variance Components......Page 225
6.6.4 Fixed Effects for Agreement......Page 226
6.6.5 Multivariate Techniques......Page 230
Exercises......Page 232
References......Page 243
7.1 Introduction......Page 245
7.2 The Classical and Bayesian Approaches to Power Analysis......Page 248
7.3 The Standard Populations: Classical and Bayesian Approaches......Page 249
7.4 Kappa, the G Coefficient, and Other Indices......Page 255
7.5 The Logistic Linear Model......Page 262
7.6 Regression and Correlation......Page 265
7.7 The Intraclass Correlation......Page 270
7.8 Bayesian Approaches to Sample Size......Page 276
Exercises......Page 277
References......Page 281
A1 Introduction......Page 283
A2 Bayes Theorem......Page 284
A3 Prior Information......Page 286
A4 Posterior Information......Page 288
A5.2 Estimation......Page 290
A5.3.1 Introduction......Page 292
A5.3.2 A Binomial Example......Page 293
A5.3.4 A Sharp Null Hypothesis for the Normal Mean......Page 294
A5.3.5 Comparing Two Normal Populations......Page 295
A6.1 Introduction......Page 296
A6.3 Forecasting from a Normal Population......Page 297
A7.1 Introduction......Page 298
A7.2 Sampling from an Exponential, but Assuming a Normal Population......Page 299
A7.3 Speed of Light Study of Newcomb......Page 300
A7.5 Measuring Tumor Size......Page 302
A7.6 Testing the Multinomial Assumption......Page 303
A8.1 Introduction......Page 304
A8.2 The One Sample Binomial......Page 305
A8.3 One Sample Binomial with Prior Information......Page 306
A8.4 Comparing Two Binomial Populations......Page 307
A9.1 Introduction......Page 308
A9.2 Direct Methods......Page 309
A9.3.2 The Metropolis Algorithm......Page 313
A9.3.3 Gibbs Sampling......Page 314
A9.3.4 The Common Mean of Normal Populations......Page 315
A9.3.5 MCMC Sampling with WinBUGS......Page 319
Exercises......Page 322
References......Page 323
B1 Introduction......Page 325
B4 Execution......Page 326
B4.3 The Update Tool......Page 328
B5 Output......Page 329
B6 Examples......Page 330
B6.1 Kappa......Page 331
B6.2 Logistic Linear Model......Page 333
References......Page 335
