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E-raamat: Bayesian Methods in Epidemiology

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(Broemeling and Associates Inc., USA.)
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Bayesian Methods in EpidemiologySuurem pilt
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Written by a biostatistics expert with over 20 years of experience in the field, Bayesian Methods in Epidemiology presents statistical methods used in epidemiology from a Bayesian viewpoint. It employs the software package WinBUGS to carry out the analyses and offers the code in the text and for download online.

The book examines study designs that investigate the association between exposure to risk factors and the occurrence of disease. It covers introductory adjustment techniques to compare mortality between states and regression methods to study the association between various risk factors and disease, including logistic regression, simple and multiple linear regression, categorical/ordinal regression, and nonlinear models. The text also introduces a Bayesian approach for the estimation of survival by life tables and illustrates other approaches to estimate survival, including a parametric model based on the Weibull distribution and the Cox proportional hazards (nonparametric) model. Using Bayesian methods to estimate the lead time of the modality, the author explains how to screen for a disease among individuals that do not exhibit any symptoms of the disease.

With many examples and end-of-chapter exercises, this book is the first to introduce epidemiology from a Bayesian perspective. It shows epidemiologists how these Bayesian models and techniques are useful in studying the association between disease and exposure to risk factors.

Arvustused

" the only book that I am aware of in literature that introduces epidemiology from a Bayesian point of view. Each concept has been introduced in its standard definition as well as its analogy in the Bayesian approach, thereby making it easier for non-Bayesian researchers to understand. The book is very well written and structured in a way that makes the comprehension of the introduced concepts very smooth. The comprehensive range of covered topics is impressive but not daunting. a great textbook for any researcher with considerable statistical background who is interested in applying Bayesian methods to public health data. useful as a refresher course I would highly recommend Bayesian Methods in Epidemiology as an introductory text to the subject as a whole. also a valuable resource for experienced Bayesian analysts." Statistics in Medicine, 34, 2015

"This is a useful and interesting book This book will help initiate those unfamiliar with Bayesian principles in Bayesian thinking and further implement Bayesian methodology in solving problems of import in medical applications. an exciting book, which I believe has the potential to help public health practitioners identify important risk factors for disease and make important policy decisions." International Statistical Review, 82, 2014

"This book consists of some examples of Bayesian methods applications to the analysis of epidemiological statistical data. It covers analysis of associations between risk exposures and diseases; adjustment of mortality data; regression methods; life tables analysis; survival analysis with Bayesian Kaplan-Meyer methods and Cox proportional hazards models; and screening for diseases." R.E. Maiboroda, Zentralblatt MATH

"by reading the book and working through the authors examples, and the examples at the end of each chapter, analysts working in epidemiology are supposed to gain an understand

1 Introduction to Bayesian Methods in Epidemiology
1(52)
1.1 Introduction
1(1)
1.2 Review of Statistical Methods in Epidemiology
1(3)
1.3 Preview of the Book
4(41)
1.3.1
Chapter 2: A Bayesian Perspective of Association between Risk Exposure and Disease
4(5)
1.3.2
Chapter 3: Bayesian Methods of Adjustment of Data
9(7)
1.3.3
Chapter 4: Regression Methods for Adjustment
16(5)
1.3.4
Chapter 5: A Bayesian Approach to Life Tables
21(6)
1.3.5
Chapter 6: A Bayesian Approach to Survival Analysis
27(5)
1.3.6
Chapter 7: Screening for Disease
32(4)
1.3.7
Chapter 8: Statistical Models for Epidemiology
36(9)
1.4 Preview of the Appendices
45(3)
1.4.1 Appendix A: Introduction to Bayesian Statistics
45(2)
1.4.2 Appendix B: Introduction to WinBUGS
47(1)
1.5 Comments and Conclusions
48(5)
Exercises
48(1)
References
49(4)
2 A Bayesian Perspective of Association between Risk Exposure and Disease
53(28)
2.1 Introduction
53(1)
2.2 Incidence and Prevalence for Mortality and Morbidity
54(3)
2.3 Association between Risk and Disease in Cohort Studies
57(4)
2.4 Retrospective Studies: Association between Risk and Disease in Case-Control Studies
61(4)
2.5 Cross-Sectional Studies
65(4)
2.6 Attributable Risk
69(4)
2.7 Comments and Conclusions
73(8)
Exercises
75(5)
References
80(1)
3 Bayesian Methods of Adjustment of Data
81(40)
3.1 Introduction
81(1)
3.2 Direct Adjustment of Data
82(8)
3.3 Indirect Standardization Adjustment
90(6)
3.3.1 Introduction
90(1)
3.3.2 Indirect Standardization
91(1)
3.3.3 Bayesian Inferences for Indirect Adjustment
92(1)
3.3.4 Example of Indirect Standardization
93(3)
3.4 Stratification and Association between Disease and Risk Exposure
96(7)
3.4.1 Introduction
96(1)
3.4.2 Interaction and Stratification
97(4)
3.4.3 An Example of Stratification
101(2)
3.5 Mantel-Haenszel Estimator of Association
103(4)
3.6 Matching to Adjust Data in Case-Control Studies
107(2)
3.7 Comments and Conclusions
109(12)
Exercises
110(10)
References
120(1)
4 Regression Methods for Adjustment
121(48)
4.1 Introduction
121(2)
4.2 Logistic Regression
123(11)
4.2.1 Introduction
123(1)
4.2.2 An Example of Heart Disease
124(7)
4.2.3 An Example with Several Independent Variables
131(2)
4.2.4 Goodness of Fit
133(1)
4.3 Linear Regression Models
134(15)
4.3.1 Introduction
134(1)
4.3.2 Simple Linear Regression
135(3)
4.3.3 Another Example of Simple Linear Regression
138(3)
4.3.4 More on Multiple Linear Regression
141(5)
4.3.5 An Example for Public Health
146(3)
4.4 Weighted Regression
149(7)
4.5 Ordinal and Other Regression Models
156(1)
4.6 Comments and Conclusions
156(13)
Exercises
158(10)
References
168(1)
5 A Bayesian Approach to Life Tables
169(44)
5.1 Introduction
169(1)
5.2 Basic Life Table
170(8)
5.2.1 Life Table Generalized
174(3)
5.2.2 Another Generalization of the Life Table
177(1)
5.3 Disease-Specific Life Tables
178(3)
5.4 Life Tables for Medical Studies
181(6)
5.4.1 Introduction
181(2)
5.4.2 California Tumor Registry 1942-1963
183(4)
5.5 Comparing Survival
187(7)
5.5.1 Introduction
187(1)
5.5.2 Direct Bayesian Approach for Comparison of Survival
188(2)
5.5.3 Indirect Bayesian Comparison of Survival
190(1)
5.5.3.1 Introduction
190(1)
5.5.3.2 Mantel-Haenszel Odds Ratio
191(3)
5.6 Kaplan-Meier Test
194(7)
5.7 Comments and Conclusions
201(12)
Exercises
202(8)
References
210(3)
6 A Bayesian Approach to Survival Analysis
213(66)
6.1 Introduction
213(1)
6.2 Notation and Basic Table for Survival
214(3)
6.3 Kaplan-Meier Survival Curves
217(15)
6.3.1 Introduction
217(4)
6.3.2 Bayesian Kaplan-Meier Method
221(4)
6.3.3 Kaplan-Meier Plots for Recurrence of Leukemia Patients
225(1)
6.3.4 Log-Rank Test for Difference in Recurrence Times
226(6)
6.4 Survival Analysis
232(29)
6.4.1 Introduction
232(1)
6.4.2 Parametric Models for Survival Analysis
233(15)
6.4.3 Cox Proportional Hazards Model
248(5)
6.4.4 Cox Model with Covariates
253(4)
6.4.5 Testing for Proportional Hazards in the Cox Model
257(4)
6.5 Comments and Conclusions
261(18)
Exercises
263(13)
References
276(3)
7 Screening for Disease
279(64)
7.1 Introduction
279(1)
7.2 Principles of Screening
280(1)
7.3 Evaluation of Screening Programs
281(15)
7.3.1 Introduction
281(2)
7.3.2 Classification Probabilities
283(3)
7.3.3 Predictive Values
286(1)
7.3.4 Diagnostic Likelihood Ratios
287(1)
7.3.5 ROC Curve
288(4)
7.3.6 UK Trial for Early Detection
292(4)
7.4 HIP Study (Health Insurance Plan of Greater New York)
296(34)
7.4.1 Introduction
296(3)
7.4.2 Descriptive Statistics
299(5)
7.4.3 Estimating the Lead Time
304(5)
7.4.4 Estimating and Comparing Survival
309(1)
7.4.4.1 Life Tables
309(13)
7.4.4.2 Survival Models
322(8)
7.5 Comments and Conclusions
330(13)
Exercises
333(7)
References
340(3)
8 Statistical Models for Epidemiology
343(64)
8.1 Introduction
343(1)
8.2 Review of Models for Epidemiology
343(3)
8.3 Categorical Regression Models
346(9)
8.4 Nonlinear Regression Models
355(10)
8.5 Repeated Measures Model
365(10)
8.6 Spatial Models for Epidemiology
375(21)
8.7 Comments and Conclusions
396(11)
Exercises
398(6)
References
404(3)
Appendix A Introduction to Bayesian Statistics 407(30)
Appendix B Introduction to WinBUGS 437(10)
Index 447
Lyle D. Broemeling
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
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