TY  - GEN
AU  - Hosmer, David W.
AU  - Lemeshow, Stanley
AU  - Sturdivant, Rodney X.
TI  - Applied logistic regression
SN  - 9780470582473
U1  - 519.536 
PY  - 2013///
CY  - Hoboken, New Jersey : 
PB  - Wiley
KW  - Logistic models
KW  - Logistic regression
KW  - Regresion analysis
KW  - Mathemetical probability
N1  - Contents:

Preface to the Third Edition
Chapter 1 Introduction to the Logistic Regression Model
1.1 Introduction
1.2 Fitting the Logistic Regression Model
1.3 Testing for the Significance of the Coefficients
1.4 Confidence Interval Estimation
1.5 Other Estimation Methods
1.6 Data Sets Used in Examples and Exercises
1.6.1 The ICU Study
1.6.2 The Low Birth Weight Study
1.6.3 The Global Longitudinal Study of Osteoporosis in Women
1.6.4 The Adolescent Placement Study
1.6.5 The Burn Injury Study
1.6.6 The Myopia Study
1.6.7 The NHANES Study
1.6.8 The Polypharmacy Study
Chapter 2 The Multiple Logistic Regression Model
2.1 Introduction
2.2 The Multiple Logistic Regression Model
2.3 Fitting the Multiple Logistic Regression Model
2.4 Testing for the Significance of the Model
2.5 Confidence Interval Estimation
2.6 Other Estimation Methods
Chapter 3 Interpretation of the Fitted Logistic Regression Model
3.1 Introduction
3.2 Dichotomous Independent Variable
3.3 Polychotomous Independent Variable
3.4 Continuous Independent Variable
3.5 Multivariable Models
3.6 Presentation and Interpretation of the Fitted Values
3.7 A Comparison of Logistic Regression and Stratified Analysis for 2 x 2 Tables
Chapter 4 Model-Building Strategies and Methods for Logistic Regression
4.1 Introduction
4.2 Purposeful Selection of Covariates
4.2.1 Methods to Examine the Scale of a Continuous Covariate in the Logit
4.2.2 Examples of Purposeful Selection
4.3 Other Methods for Selecting Covariates
4.3.1 Stepwise Selection of Covariates
4.3.2 Best Subsets Logistic Regression.
4.3.3 Selecting Covariates and Checking their Scale Using Multivariable Fractional Polynomials
4.4 Numerical Problems
Chapter 5 Assessing the Fit of the Model
5.1 Introduction
5.2 Summary Measures of Goodness of Fit
5.2.1 Pearson Chi-Square Statistic, Deviance, and Sum-of-Squares
5.2.2 The Hosmer-Lemeshow Tests
5.2.3 Classification Tables
5.2.4 Area Under the Receiver Operating Characteristic Curve
5.2.5 Other Summary Measures
5.3 Logistic Regression Diagnostics
5.4 Assessment of Fit via External Validation
5.5 Interpretation and Presentation of the Results from a Fitted Logistic Regression Model
Chapter 6 Application of Logistic Regression with Different Sampling Models
6.1 Introduction
6.2 Cohort Studies
6.3 Case-Control Studies
6.4 Fitting Logistic Regression Models to Data from Complex Sample Surveys
Chapter 7 Logistic Regression for Matched Case-Control Studies
7.1 Introduction
7.2 Methods For Assessment of Fit in a 1-M Matched Study
7.3 An Example Using the Logistic Regression Model in a 1-1 Matched Study
7.4 An Example Using the Logistic Regression Model in a 1-M Matched Study
Chapter 8 Logistic Regression Models for Multinomial and Ordinal Outcomes
8.1 The Multinomial Logistic Regression Model
8.1.1 Introduction to the Model and Estimation of Model Parameters
8.1.2 Interpreting and Assessing the Significance of the Estimated Coefficients
8.1.3 Model-Building Strategies for Multinomial Logistic Regression
8.1.4 Assessment of Fit and Diagnostic Statistics for the Multinomial Logistic Regression Model
8.2 Ordinal Logistic Regression Models
8.2.1 Introduction to the Models, Methods for Fitting, and Interpretation of Model Parameters.
8.2.2 Model Building Strategies for Ordinal Logistic Regression Models
Chapter 9 Logistic Regression Models for the Analysis of Correlated Data
9.1 Introduction
9.2 Logistic Regression Models for the Analysis of Correlated Data
9.3 Estimation Methods for Correlated Data Logistic Regression Models
9.4 Interpretation of Coefficients from Logistic Regression Models for the Analysis of Correlated Data
9.4.1 Population Average Model
9.4.2 Cluster-Specific Model
9.4.3 Alternative Estimation Methods for the Cluster-Specific Model
9.4.4 Comparison of Population Average and Cluster-Specific Model
9.5 An Example of Logistic Regression Modeling with Correlated Data
9.5.1 Choice of Model for Correlated Data Analysis
9.5.2 Population Average Model
9.5.3 Cluster-Specific Model
9.5.4 Additional Points to Consider when Fitting Logistic Regression Models to Correlated Data
9.6 Assessment of Model Fit
9.6.1 Assessment of Population Average Model Fit
9.6.2 Assessment of Cluster-Specific Model Fit
9.6.3 Conclusions
Chapter 10 Special Topics
10.1 Introduction
10.2 Application of Propensity Score Methods in Logistic Regression Modeling
10.3 Exact Methods for Logistic Regression Models
10.4 Missing Data
10.5 Sample Size Issues when Fitting Logistic Regression Models
10.6 Bayesian Methods for Logistic Regression
10.6.1 The Bayesian Logistic Regression Model
10.6.2 MCMC Simulation
10.6.3 An Example of a Bayesian Analysis and Its Interpretation
10.7 Other Link Functions for Binary Regression Models
10.8 Mediation
10.8.1 Distinguishing Mediators from Confounders
10.8.2 Implications for the Interpretation of an Adjusted Logistic Regression Coefficient
10.8.3 Why Adjust for a Mediator?.
10.8.4 Using Logistic Regression to Assess Mediation: Assumptions
10.9 More About Statistical Interaction
10.9.1 Additive versus Multiplicative Scale-Risk Difference versus Odds Ratios
10.9.2 Estimating and Testing Additive Interaction
References
Index
Series Page
N2  - 
Summary:

In this revised and updated edition, the authors continue to provide an accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets. They extend the discussion from biostatistics and epidemiology to cutting-edge applications in data mining and machine learning, guiding readers step-by-step through the use of modeling techniques for dichotomous data in diverse fields. Ample new topics and expanded discussions of existing material are accompanied by real-world examples-with extensive data sets available over the Internet. From the reviews of the First Edition. "An interesting, useful, and well-written book on logistic regression models . . . Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references." -Choice "Well written, clearly organized, and comprehensive . . . the authors carefully walk the reader through the estimation of interpretation of coefficients from a wide variety of logistic regression models . . . their careful explication of the quantitative re-expression of coefficients from these various models is excellent." -Contemporary Sociology "An extremely well-written book that will certainly prove an invaluable acquisition to the practicing statistician who finds other literature on analysis of discrete data hard to follow or heavily theoretical." -The Statistician In this revised and updated edition of their popular book, David Hosmer and Stanley Lemeshow continue to provide an amazingly accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets. Hosmer and Lemeshow extend the discussion from biostatistics and epidemiology to cutting-edge applications in data mining and machine learning, guiding readers step-by-step through the use of modeling techniques for dichotomous data in diverse fields. Ample new topics and expanded discussions of existing material are accompanied by a wealth of real-world examples-with extensive data sets available over the Internet
ER  -