{"product_id":"applied-logistic-regression-3e-9780470582473","title":"Applied Logistic Regression 3e","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eA new edition of the definitive guide to logistic regression modeling\u003c\/b\u003e \u003cb\u003efor health science and other applications\u003c\/b\u003e \u003cp\u003eThis thoroughly expanded \u003ci\u003eThird Edition\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eApplied Logistic Regression\u003c\/i\u003e, 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 state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eA chapter on the analysis of correlated outcome data\u003c\/li\u003e \u003cli\u003eA wealth of additional material for topics ranging from Bayesian methods to assessing model fit\u003c\/li\u003e \u003cli\u003eRich data sets from real-world studies that demonstrate each method under discussion\u003c\/li\u003e \u003cli\u003e\n\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e“In conclusion, the index was mercifully complete, and all items searched for were found (nice cross-referencing too)  In summary:  Highly recommended.”  (\u003ci\u003eScientific Computing\u003c\/i\u003e, 1 May 2013)\u003c\/p\u003e\n\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface to the Third Edition xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to the Logistic Regression Model 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Fitting the Logistic Regression Model 8\u003c\/p\u003e \u003cp\u003e1.3 Testing for the Significance of the Coefficients 10\u003c\/p\u003e \u003cp\u003e1.4 Confidence Interval Estimation 15\u003c\/p\u003e \u003cp\u003e1.5 Other Estimation Methods 20\u003c\/p\u003e \u003cp\u003e1.6 Data Sets Used in Examples and Exercises 22\u003c\/p\u003e \u003cp\u003e1.6.1 The ICU Study 22\u003c\/p\u003e \u003cp\u003e1.6.2 The Low Birth Weight Study 24\u003c\/p\u003e \u003cp\u003e1.6.3 The Global Longitudinal Study of Osteoporosis in Women 24\u003c\/p\u003e \u003cp\u003e1.6.4 The Adolescent Placement Study 26\u003c\/p\u003e \u003cp\u003e1.6.5 The Burn Injury Study 27\u003c\/p\u003e \u003cp\u003e1.6.6 The Myopia Study 29\u003c\/p\u003e \u003cp\u003e1.6.7 The NHANES Study 31\u003c\/p\u003e \u003cp\u003e1.6.8 The Polypharmacy Study 31\u003c\/p\u003e \u003cp\u003eExercises 32\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The Multiple Logistic Regression Model 35\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 35\u003c\/p\u003e \u003cp\u003e2.2 The Multiple Logistic Regression Model 35\u003c\/p\u003e \u003cp\u003e2.3 Fitting the Multiple Logistic Regression Model 37\u003c\/p\u003e \u003cp\u003e2.4 Testing for the Significance of the Model 39\u003c\/p\u003e \u003cp\u003e2.5 Confidence Interval Estimation 42\u003c\/p\u003e \u003cp\u003e2.6 Other Estimation Methods 45\u003c\/p\u003e \u003cp\u003eExercises 46\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Interpretation of the Fitted Logistic Regression Model 49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 49\u003c\/p\u003e \u003cp\u003e3.2 Dichotomous Independent Variable 50\u003c\/p\u003e \u003cp\u003e3.3 Polychotomous Independent Variable 56\u003c\/p\u003e \u003cp\u003e3.4 Continuous Independent Variable 62\u003c\/p\u003e \u003cp\u003e3.5 Multivariable Models 64\u003c\/p\u003e \u003cp\u003e3.6 Presentation and Interpretation of the Fitted Values 77\u003c\/p\u003e \u003cp\u003e3.7 A Comparison of Logistic Regression and Stratified Analysis for 2 × 2 Tables 82\u003c\/p\u003e \u003cp\u003eExercises 87\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Model-Building Strategies and Methods for Logistic Regression 89\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 89\u003c\/p\u003e \u003cp\u003e4.2 Purposeful Selection of Covariates 89\u003c\/p\u003e \u003cp\u003e4.2.1 Methods to Examine the Scale of a Continuous Covariate in the Logit 94\u003c\/p\u003e \u003cp\u003e4.2.2 Examples of Purposeful Selection 107\u003c\/p\u003e \u003cp\u003e4.3 Other Methods for Selecting Covariates 124\u003c\/p\u003e \u003cp\u003e4.3.1 Stepwise Selection of Covariates 125\u003c\/p\u003e \u003cp\u003e4.3.2 Best Subsets Logistic Regression 133\u003c\/p\u003e \u003cp\u003e4.3.3 Selecting Covariates and Checking their Scale Using Multivariable Fractional Polynomials 139\u003c\/p\u003e \u003cp\u003e4.4 Numerical Problems 145\u003c\/p\u003e \u003cp\u003eExercises 150\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Assessing the Fit of the Model 153\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 153\u003c\/p\u003e \u003cp\u003e5.2 Summary Measures of Goodness of Fit 154\u003c\/p\u003e \u003cp\u003e5.2.1 Pearson Chi-Square Statistic, Deviance, and Sum-of-Squares 155\u003c\/p\u003e \u003cp\u003e5.2.2 The Hosmer–Lemeshow Tests 157\u003c\/p\u003e \u003cp\u003e5.2.3 Classification Tables 169\u003c\/p\u003e \u003cp\u003e5.2.4 Area Under the Receiver Operating Characteristic Curve 173\u003c\/p\u003e \u003cp\u003e5.2.5 Other Summary Measures 182\u003c\/p\u003e \u003cp\u003e5.3 Logistic Regression Diagnostics 186\u003c\/p\u003e \u003cp\u003e5.4 Assessment of Fit via External Validation 202\u003c\/p\u003e \u003cp\u003e5.5 Interpretation and Presentation of the Results from a Fitted Logistic Regression Model 212\u003c\/p\u003e \u003cp\u003eExercises 223\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Application of Logistic Regression with Different Sampling Models 227\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 227\u003c\/p\u003e \u003cp\u003e6.2 Cohort Studies 227\u003c\/p\u003e \u003cp\u003e6.3 Case-Control Studies 229\u003c\/p\u003e \u003cp\u003e6.4 Fitting Logistic Regression Models to Data from Complex Sample Surveys 233\u003c\/p\u003e \u003cp\u003eExercises 242\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Logistic Regression for Matched Case-Control Studies 243\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 243\u003c\/p\u003e \u003cp\u003e7.2 Methods For Assessment of Fit in a 1–\u003ci\u003eM \u003c\/i\u003eMatched Study 248\u003c\/p\u003e \u003cp\u003e7.3 An Example Using the Logistic Regression Model in a 1–1 Matched Study 251\u003c\/p\u003e \u003cp\u003e7.4 An Example Using the Logistic Regression Model in a 1–\u003ci\u003eM \u003c\/i\u003eMatched Study 260\u003c\/p\u003e \u003cp\u003eExercises 267\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Logistic Regression Models for Multinomial and Ordinal Outcomes 269\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 The Multinomial Logistic Regression Model 269\u003c\/p\u003e \u003cp\u003e8.1.1 Introduction to the Model and Estimation of Model Parameters 269\u003c\/p\u003e \u003cp\u003e8.1.2 Interpreting and Assessing the Significance of the Estimated Coefficients 272\u003c\/p\u003e \u003cp\u003e8.1.3 Model-Building Strategies for Multinomial Logistic Regression 278\u003c\/p\u003e \u003cp\u003e8.1.4 Assessment of Fit and Diagnostic Statistics for the Multinomial Logistic Regression Model 283\u003c\/p\u003e \u003cp\u003e8.2 Ordinal Logistic Regression Models 289\u003c\/p\u003e \u003cp\u003e8.2.1 Introduction to the Models, Methods for Fitting, and Interpretation of Model Parameters 289\u003c\/p\u003e \u003cp\u003e8.2.2 Model Building Strategies for Ordinal Logistic Regression Models 305\u003c\/p\u003e \u003cp\u003eExercises 310\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Logistic Regression Models for the Analysis of Correlated Data 313\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 313\u003c\/p\u003e \u003cp\u003e9.2 Logistic Regression Models for the Analysis of Correlated Data 315\u003c\/p\u003e \u003cp\u003e9.3 Estimation Methods for Correlated Data Logistic Regression Models 318\u003c\/p\u003e \u003cp\u003e9.4 Interpretation of Coefficients from Logistic Regression Models for the Analysis of Correlated Data 323\u003c\/p\u003e \u003cp\u003e9.4.1 Population Average Model 324\u003c\/p\u003e \u003cp\u003e9.4.2 Cluster-Specific Model 326\u003c\/p\u003e \u003cp\u003e9.4.3 Alternative Estimation Methods for the Cluster-Specific Model 333\u003c\/p\u003e \u003cp\u003e9.4.4 Comparison of Population Average and Cluster-Specific Model 334\u003c\/p\u003e \u003cp\u003e9.5 An Example of Logistic Regression Modeling with Correlated Data 337\u003c\/p\u003e \u003cp\u003e9.5.1 Choice of Model for Correlated Data Analysis 338\u003c\/p\u003e \u003cp\u003e9.5.2 Population Average Model 339\u003c\/p\u003e \u003cp\u003e9.5.3 Cluster-Specific Model 344\u003c\/p\u003e \u003cp\u003e9.5.4 Additional Points to Consider when Fitting Logistic Regression Models to Correlated Data 351\u003c\/p\u003e \u003cp\u003e9.6 Assessment of Model Fit 354\u003c\/p\u003e \u003cp\u003e9.6.1 Assessment of Population Average Model Fit 354\u003c\/p\u003e \u003cp\u003e9.6.2 Assessment of Cluster-Specific Model Fit 365\u003c\/p\u003e \u003cp\u003e9.6.3 Conclusions 374\u003c\/p\u003e \u003cp\u003eExercises 375\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Special Topics 377\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 377\u003c\/p\u003e \u003cp\u003e10.2 Application of Propensity Score Methods in Logistic Regression Modeling 377\u003c\/p\u003e \u003cp\u003e10.3 Exact Methods for Logistic Regression Models 387\u003c\/p\u003e \u003cp\u003e10.4 Missing Data 395\u003c\/p\u003e \u003cp\u003e10.5 Sample Size Issues when Fitting Logistic Regression Models 401\u003c\/p\u003e \u003cp\u003e10.6 Bayesian Methods for Logistic Regression 408\u003c\/p\u003e \u003cp\u003e10.6.1 The Bayesian Logistic Regression Model 410\u003c\/p\u003e \u003cp\u003e10.6.2 MCMC Simulation 411\u003c\/p\u003e \u003cp\u003e10.6.3 An Example of a Bayesian Analysis and Its Interpretation 419\u003c\/p\u003e \u003cp\u003e10.7 Other Link Functions for Binary Regression Models 434\u003c\/p\u003e \u003cp\u003e10.8 Mediation 441\u003c\/p\u003e \u003cp\u003e10.8.1 Distinguishing Mediators from Confounders 441\u003c\/p\u003e \u003cp\u003e10.8.2 Implications for the Interpretation of an Adjusted Logistic Regression Coefficient 443\u003c\/p\u003e \u003cp\u003e10.8.3 Why Adjust for a Mediator? 444\u003c\/p\u003e \u003cp\u003e10.8.4 Using Logistic Regression to Assess Mediation: Assumptions 445\u003c\/p\u003e \u003cp\u003e10.9 More About Statistical Interaction 448\u003c\/p\u003e \u003cp\u003e10.9.1 Additive versus Multiplicative Scale–Risk Difference versus Odds Ratios 448\u003c\/p\u003e \u003cp\u003e10.9.2 Estimating and Testing Additive Interaction 451\u003c\/p\u003e \u003cp\u003eExercises 456\u003c\/p\u003e \u003cp\u003eReferences 459\u003c\/p\u003e \u003cp\u003eIndex 479\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48864633618775,"sku":"9780470582473","price":107.96,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470582473.jpg?v=1722272820","url":"https:\/\/bookcurl.com\/products\/applied-logistic-regression-3e-9780470582473","provider":"Book Curl","version":"1.0","type":"link"}