LEADER 06561nam 2200805 a 450 001 9910139038403321 005 20230421124143.0 010 $a1-118-54838-8 010 $a1-118-54835-3 010 $a1-299-40240-2 010 $a1-118-54839-6 035 $a(CKB)2550000001017843 035 $a(EBL)1138225 035 $a(SSID)ssj0000832665 035 $a(PQKBManifestationID)11482997 035 $a(PQKBTitleCode)TC0000832665 035 $a(PQKBWorkID)10899962 035 $a(PQKB)11527943 035 $a(DLC) 2013010300 035 $a(Au-PeEL)EBL1138225 035 $a(CaPaEBR)ebr10677827 035 $a(CaONFJC)MIL471490 035 $a(OCoLC)830124707 035 $a(CaSebORM)9781118548356 035 $a(MiAaPQ)EBC1138225 035 $a(PPN)191455547 035 $a(EXLCZ)992550000001017843 100 $a20130311d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aApplied logistic regression$b[electronic resource] $eDavid W. Hosmer, Stanley Lemeshow, Rodney X. Sturdivant 205 $a3rd ed. 210 $aHoboken, N.J. $cWiley$d2013 215 $a1 online resource (528 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-470-58247-2 320 $aIncludes bibliographical references and index. 327 $aApplied Logistic Regression; Contents; Preface to the Third Edition; 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 327 $a1.6.8 The Polypharmacy StudyExercises; 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; Exercises; 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 327 $a3.7 A Comparison of Logistic Regression and Stratified Analysis for 2 x 2 TablesExercises; 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; Exercises 327 $a5 Assessing the Fit of the Model5.1 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; Exercises; 6 Application of Logistic Regression with Different Sampling Models; 6.1 Introduction 327 $a6.2 Cohort Studies6.3 Case-Control Studies; 6.4 Fitting Logistic Regression Models to Data from Complex Sample Surveys; Exercises; 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; Exercises; 8 Logistic Regression Models for Multinomial and Ordinal Outcomes; 8.1 The Multinomial Logistic Regression Model 327 $a8.1.1 Introduction to the Model and Estimation of Model Parameters 330 $a"A new edition of the definitive guide to logistic regression modeling for health science and other applicationsThis thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include: A chapter on the analysis of correlated outcome data. A wealth of additional material for topics ranging from Bayesian methods to assessing model fit Rich data sets from real-world studies that demonstrate each method under discussion. Detailed examples and interpretation of the presented results as well as exercises throughout Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines"--$cProvided by publisher. 410 0$aWiley series in probability and statistics. 606 $aRegression analysis 606 $aAnàlisi de regressió$2thub 606 $aAnàlisi multivariable$2thub 606 $aEstadística$2thub 608 $aLlibres electrònics$2thub 615 0$aRegression analysis. 615 7$aAnàlisi de regressió 615 7$aAnàlisi multivariable 615 7$aEstadística 676 $a519.5/36 686 $aMAT029030$2bisacsh 700 $aHosmer$b David W$0251740 701 $aLemeshow$b Stanley$0102417 701 $aSturdivant$b Rodney X$0618338 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139038403321 996 $aApplied logistic regression$91073950 997 $aUNINA