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Modeling Binary Correlated Responses using SAS, SPSS and R

:	Jeffrey R. Wilson, Kent A. Lorenz
:	Modeling Binary Correlated Responses using SAS, SPSS and R
:	Springer-Verlag
:	9783319238050
:	1
:	CHF 47.50
:

:	Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik
:	English

:	283
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

Statistical tools to analyze correlated binary data are spread out in the existing literature. This book makes these tools accessible to practitioners in a single volume. Chapters cover recently developed statistical tools and statistical packages that are tailored to analyzing correlated binary data. The authors showcase both traditional and new methods for application to health-related research. Data and computer programs will be publicly available in order for readers to replicate model development, but learning a new statistical language is not necessary with this book. The inclusion of code for R, SAS, and SPSS allows for easy implementation by readers. For readers interested in learning more about the languages, though, there are short tutorials in the appendix. Accompanying data sets are available for download through the book s website. Data analysis presented in each chapter will provide step-by-step instructions so these new methods can be readily applied to projects. Researchers and graduate students in Statistics, Epidemiology, and Public Health will find this book particularly useful.

	Preface	8
	Part I: Introduction and Review of Modeling Uncorrelated Observations	10
	Part II: Analyzing Correlated Data Through Random Component	11
	Part III: Analyzing Correlated Data Through Systematic Components	15
	Part IV: Analyzing Correlated Data Through the Joint Modeling of Mean and Variance	16
	Contents	18
	Part I: Introduction and Review of Modeling Uncorrelated Observations	25
	Chapter 1: Introduction to Binary Logistic Regression	26
	1.1 Motivating Example	26
	1.2 Definition and Notation	27
	1.2.1 Notations	27
	1.2.2 Definitions	27
	Categorical Variable in the Form of a Series of Binary Variables	28
	Relationship Between Response and Predictor Variables	29
	1.3 Exploratory Analyses	29
	1.4 Statistical Models	31
	1.4.1 Chapter 3: Standard Binary Logistic Regression Model	31
	1.4.2 Chapter 4: Overdispersed Logistic Regression Model	31
	1.4.3 Chapter 5: Survey Data Logistic Regression Model	32
	1.4.4 Chapter 6: Generalized Estimating Equations Logistic Regression Model	32
	1.4.5 Chapter 7: Generalized Method of Moments Logistic Regression Model	32
	1.4.6 Chapter 8: Exact Logistic Regression Model	32
	1.4.7 Chapter 9: Two-Level Nested Logistic Regression Model	33
	1.4.8 Chapter 10: Hierarchical Logistic Regression Model	33
	1.4.9 Chapter 11: Fixed Effects Logistic Regression Model	33
	1.4.10 Chapter 12: Heteroscedastic Logistic Regression Model	33
	1.5 Analysis of Data	34
	1.5.1 SAS Programming	35
	1.5.2 SPSS Programming	35
	1.5.3 R Programming	35
	1.6 Conclusions	36
	1.7 Related Examples	37
	1.7.1 Medicare Data	37
	1.7.2 Philippines Data	37
	1.7.3 Household Satisfaction Survey	38
	1.7.4 NHANES: Treatment for Osteoporosis	38
	References	39
	Chapter 2: Short History of the Logistic Regression Model	40
	2.1 Motivating Example	40
	2.2 Definition and Notation	41
	2.2.1 Notation	41
	2.2.2 Definition	41
	2.3 Exploratory Analyses	42
	2.4 Statistical Model	43
	2.5 Analysis of Data	45
	2.6 Conclusions	45
	References	46
	Chapter 3: Standard Binary Logistic Regression Model	48
	3.1 Motivating Example	49
	3.1.1 Study Hypotheses	49
	3.2 Definition and Notation	49
	3.3 Exploratory Analyses	51
	3.4 Statistical Models	54
	3.4.1 Probability	55
	3.4.2 Odds	55
	3.4.3 Logits	56
	3.4.4 Logistic Regression Versus Ordinary Least Squares	56
	3.4.5 Generalized Linear Models	57
	3.4.6 Response Probability Distributions	58
	3.4.7 Log-Likelihood Functions	58
	3.4.8 Maximum Likelihood Fitting	58
	3.4.9 Goodness of Fit	59
	3.4.10 Other Fit Statistics	59
	3.4.11 Assumptions for Logistic Regression Model	60
	3.4.12 Interpretation of Coefficients	60
	3.4.13 Interpretation of Odds Ratio (OR)	60
	3.4.14 Model Fit	61
	3.4.15 Null Hypothesis	61
	3.4.16 Predicted Probabilities	62
	3.4.17 Computational Issues Encountered with Logistic Regression	63
	3.5 Analysis of Data	63
	3.5.1 Medicare Data	64
	SAS Output	69
	SAS Output	69
	3.6 Conclusions	74
	3.7 Related Examples	74
	Appendix: Partial Medicare Data time=1	75
	References	76
	Part II: Analyzing Correlated Data Through Random Component	78
	Chapter 4: Overdispersed Logistic Regression Model