000  09012nam a2200481 i 4500  

001  7294  
003  BDDhEWU  
005  20180128152109.0  
008  131226s2013 njua g b 001 0 eng d  
010  _a 2013000403  
020  _a9780470582473 (hardback)  
020  _a9781118514948 (oBook ISBN)  
020  _a9781118514924 (ePDF ISBN)  
020  _a9781118514931 (ePub ISBN)  
020  _a9781118514900 (eMOBI ISBN)  
035 
_a(OCoLC)843434502 _a(BDDhEWU)7294 

040 
_aDLC _beng _cDLC _erda _dDLC _dBDDhEWU 

041  _aeng  
050  0  0 
_aQA278.2 _b.H67 2013 
082  0  4 
_a519.536 HOA _b2013 
084 
_aMAT029030 _2bisacsh 

100  1 
_aHosmer, David W. _92708 

245  1  0  _aApplied logistic regression. 
250  _aThird edition /  
300 
_axvi, 500 pages : _billustrations ; _c24 cm. 

490  0  _aWiley series in probability and statistics  
504  _aIncludes bibliographical references (pages 459478) and index.  
505  _aPreface to the Third Edition xiii 1 Introduction to the Logistic Regression Model 1 1.1Introduction, 1 1.2 Fitting the Logistic Regression Model, 8 1.3 Testing for the Significance of the Coefficients, 10 1.4 Confidence Interval Estimation, 15 1.5 Other Estimation Methods, 20 1.6 Data Sets Used in Examples and Exercises, 22 1.6.1 The ICU Study, 22 1.6.2 The Low Birth Weight Study, 24 1.6.3 The Global Longitudinal Study of Osteoporosis in Women, 24 1.6.4 The Adolescent Placement Study, 26 1.6.5 The Burn Injury Study, 27 1.6.6 The Myopia Study, 29 1.6.7 The NHANES Study, 31 1.6.8 The Polypharmacy Study, 31 Exercises, 32 2 The Multiple Logistic Regression Model 35 2.1 Introduction, 35 2.2 The Multiple Logistic Regression Model, 35 2.3 Fitting the Multiple Logistic Regression Model, 37 2.4 Testing for the Significance of the Model, 39 2.5 Confidence Interval Estimation, 42 2.6 Other Estimation Methods, 45 Exercises, 46 3 Interpretation of the Fitted Logistic Regression Model 49 3.1 Introduction, 49 3.2 Dichotomous Independent Variable, 50 3.3 Polychotomous Independent Variable, 56 3.4 Continuous Independent Variable, 62 3.5 Multivariable Models, 64 3.6 Presentation and Interpretation of the Fitted Values, 77 3.7 A Comparison of Logistic Regression and Stratified Analysis for 2 × 2 Tables, 82 Exercises, 87 4 ModelBuilding Strategies and Methods for Logistic Regression 89 4.1 Introduction, 89 4.2 Purposeful Selection of Covariates, 89 4.2.1 Methods to Examine the Scale of a Continuous Covariate in the Logit, 94 4.2.2 Examples of Purposeful Selection, 107 4.3 Other Methods for Selecting Covariates, 124 4.3.1 Stepwise Selection of Covariates, 125 4.3.2 Best Subsets Logistic Regression, 133 4.3.3 Selecting Covariates and Checking their Scale Using Multivariable Fractional Polynomials, 139 4.4 Numerical Problems, 145 Exercises, 150 5 Assessing the Fit of the Model 153 5.1 Introduction, 153 5.2 Summary Measures of Goodness of Fit, 154 5.2.1 Pearson ChiSquare Statistic, Deviance, and SumofSquares, 155 5.2.2 The HosmerLemeshow Tests, 157 5.2.3 Classification Tables, 169 5.2.4 Area Under the Receiver Operating Characteristic Curve, 173 5.2.5 Other Summary Measures, 182 5.3 Logistic Regression Diagnostics, 186 5.4 Assessment of Fit via External Validation, 202 5.5 Interpretation and Presentation of the Results from a Fitted Logistic Regression Model, 212 Exercises, 223 6 Application of Logistic Regression with Different Sampling Models 227 6.1 Introduction, 227 6.2 Cohort Studies, 227 6.3 CaseControl Studies, 229 6.4 Fitting Logistic Regression Models to Data from Complex Sample Surveys, 233 Exercises, 242 7 Logistic Regression for Matched CaseControl Studies 243 7.1 Introduction, 243 7.2 Methods For Assessment of Fit in a 1M Matched Study, 248 7.3 An Example Using the Logistic Regression Model in a 11 Matched Study, 251 7.4 An Example Using the Logistic Regression Model in a 1M Matched Study, 260 Exercises, 267 8 Logistic Regression Models for Multinomial and Ordinal Outcomes 269 8.1 The Multinomial Logistic Regression Model, 269 8.1.1 Introduction to the Model and Estimation of Model Parameters, 269 8.1.2 Interpreting and Assessing the Significance of the Estimated Coefficients, 272 8.1.3 ModelBuilding Strategies for Multinomial Logistic Regression, 278 8.1.4 Assessment of Fit and Diagnostic Statistics for the Multinomial Logistic Regression Model, 283 8.2 Ordinal Logistic Regression Models, 289 8.2.1 Introduction to the Models, Methods for Fitting, and Interpretation of Model Parameters, 289 8.2.2 Model Building Strategies for Ordinal Logistic Regression Models, 305 Exercises, 310 9 Logistic Regression Models for the Analysis of Correlated Data 313 9.1 Introduction, 313 9.2 Logistic Regression Models for the Analysis of Correlated Data, 315 9.3 Estimation Methods for Correlated Data Logistic Regression Models, 318 9.4 Interpretation of Coefficients from Logistic Regression Models for the Analysis of Correlated Data, 323 9.4.1 Population Average Model, 324 9.4.2 ClusterSpecific Model, 326 9.4.3 Alternative Estimation Methods for the ClusterSpecific Model, 333 9.4.4 Comparison of Population Average and ClusterSpecific Model, 334 9.5 An Example of Logistic Regression Modeling with Correlated Data, 337 9.5.1 Choice of Model for Correlated Data Analysis, 338 9.5.2 Population Average Model, 339 9.5.3 ClusterSpecific Model, 344 9.5.4 Additional Points to Consider when Fitting Logistic Regression Models to Correlated Data, 351 9.6 Assessment of Model Fit, 354 9.6.1 Assessment of Population Average Model Fit, 354 9.6.2 Assessment of ClusterSpecific Model Fit, 365 9.6.3 Conclusions, 374 Exercises, 375 10 Special Topics 377 10.1 Introduction, 377 10.2 Application of Propensity Score Methods in Logistic Regression Modeling, 377 10.3 Exact Methods for Logistic Regression Models, 387 10.4 Missing Data, 395 10.5 Sample Size Issues when Fitting Logistic Regression Models, 401 10.6 Bayesian Methods for Logistic Regression, 408 10.6.1 The Bayesian Logistic Regression Model, 410 10.6.2 MCMC Simulation, 411 10.6.3 An Example of a Bayesian Analysis and Its Interpretation, 419 10.7 Other Link Functions for Binary Regression Models, 434 10.8 Mediation, 441 10.8.1 Distinguishing Mediators from Confounders, 441 10.8.2 Implications for the Interpretation of an Adjusted Logistic Regression Coefficient, 443 10.8.3 Why Adjust for a Mediator? 444 10.8.4 Using Logistic Regression to Assess Mediation: Assumptions, 445 10.9 More About Statistical Interaction, 448 10.9.1 Additive versus Multiplicative ScaleRisk Difference versus Odds Ratios, 448 10.9.2 Estimating and Testing Additive Interaction, 451 Exercises, 456 References 459 Index 479  
520  _a"A new edition of the definitive guide to logistic regression modeling for health science and other applicationsThis thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with stateoftheart techniques for building, interpreting, and assessing the performance of LR models. New and updated features include: A chapter on the analysis of correlated outcome data. A wealth of additional material for topics ranging from Bayesian methods to assessing model fit Rich data sets from realworld studies that demonstrate each method under discussion. Detailed examples and interpretation of the presented results as well as exercises throughout Applied Logistic Regression, Third Edition is a musthave guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines"  
520  _a"This Third Edition continues to focus on applications and interpretation of results from fitting regression models to categorical response variables"  
526  _aAS  
590  _aTahur Ahmed  
650  0 
_aRegression analysis. _92220 

650  7 
_aMATHEMATICS / Probability & Statistics / Regression Analysis _2bisacsh. _92709 

700  1 
_aLemeshow, Stanley. _92710 

700  1 
_aSturdivant, Rodney X. _92711 

776  0  8 
_iOnline version: _aHosmer, David W. _tApplied logistic regression. _bThird edition / David W. Hosmer, Jr., PhD, Stanley Lemeshow, PhD, Rodney X. Sturdivant, PhD. _dHoboken, New Jersey : Wiley, [2013] _z9781118548356 _w(DLC) 2013010300 
856  4  2 
_3Cover image _uhttp://catalogimages.wiley.com/images/db/jimages/9780470582473.jpg 
856  4  2 
_3OCLC _uhttp://www.worldcat.org/title/appliedlogisticregression/oclc/843434502&referer=brief_results 
856  4  2 
_3Ebook Fulltext _uhttp://lib.ewubd.edu/ebook/7294 
942 
_2ddc _cTEXT _07 

999 
_c7294 _d7294 

999 
_c7294 _d7294 