LEADER 05637nam 2200733 a 450 001 9911020049903321 005 20200520144314.0 010 $a9786611841003 010 $a9781281841001 010 $a1281841005 010 $a9780470770771 010 $a0470770775 010 $a9780470770788 010 $a0470770783 035 $a(CKB)1000000000549390 035 $a(EBL)366774 035 $a(OCoLC)476201818 035 $a(SSID)ssj0000206842 035 $a(PQKBManifestationID)11180050 035 $a(PQKBTitleCode)TC0000206842 035 $a(PQKBWorkID)10246504 035 $a(PQKB)10229985 035 $a(MiAaPQ)EBC366774 035 $a(PPN)263348644 035 $a(Perlego)2772175 035 $a(EXLCZ)991000000000549390 100 $a20080124d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMultivariable model-building $ea pragmatic approach to regression analysis based on fractional polynomials for modelling continuous variables /$fPatrick Royston, Willi Sauerbrei 210 $aChichester, England ;$aHoboken, NJ $cJohn Wiley$dc2008 215 $a1 online resource (323 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 08$a9780470028421 311 08$a0470028424 320 $aIncludes bibliographical references (p. 271-283) and index. 327 $aMultivariable Model-Building; Contents; Preface; 1 Introduction; 1.1 Real-Life Problems as Motivation for Model Building; 1.1.1 Many Candidate Models; 1.1.2 Functional Form for Continuous Predictors; 1.1.3 Example 1: Continuous Response; 1.1.4 Example 2: Multivariable Model for Survival Data; 1.2 Issues in Modelling Continuous Predictors; 1.2.1 Effects of Assumptions; 1.2.2 Global versus Local Influence Models; 1.2.3 Disadvantages of Fractional Polynomial Modelling; 1.2.4 Controlling Model Complexity; 1.3 Types of Regression Model Considered; 1.3.1 Normal-Errors Regression 327 $a1.3.2 Logistic Regression1.3.3 Cox Regression; 1.3.4 Generalized Linear Models; 1.3.5 Linear and Additive Predictors; 1.4 Role of Residuals; 1.4.1 Uses of Residuals; 1.4.2 Graphical Analysis of Residuals; 1.5 Role of Subject-Matter Knowledge in Model Development; 1.6 Scope of Model Building in our Book; 1.7 Modelling Preferences; 1.7.1 General Issues; 1.7.2 Criteria for a Good Model; 1.7.3 Personal Preferences; 1.8 General Notation; 2 Selection of Variables; 2.1 Introduction; 2.2 Background; 2.3 Preliminaries for a Multivariable Analysis; 2.4 Aims of Multivariable Models 327 $a2.5 Prediction: Summary Statistics and Comparisons2.6 Procedures for Selecting Variables; 2.6.1 Strength of Predictors; 2.6.2 Stepwise Procedures; 2.6.3 All-Subsets Model Selection Using Information Criteria; 2.6.4 Further Considerations; 2.7 Comparison of Selection Strategies in Examples; 2.7.1 Myeloma Study; 2.7.2 Educational Body-Fat Data; 2.7.3 Glioma Study; 2.8 Selection and Shrinkage; 2.8.1 Selection Bias; 2.8.2 Simulation Study; 2.8.3 Shrinkage to Correct for Selection Bias; 2.8.4 Post-estimation Shrinkage; 2.8.5 Reducing Selection Bias; 2.8.6 Example; 2.9 Discussion 327 $a2.9.1 Model Building in Small Datasets2.9.2 Full, Pre-specified or Selected Model?; 2.9.3 Comparison of Selection Procedures; 2.9.4 Complexity, Stability and Interpretability; 2.9.5 Conclusions and Outlook; 3 Handling Categorical and Continuous Predictors; 3.1 Introduction; 3.2 Types of Predictor; 3.2.1 Binary; 3.2.2 Nominal; 3.2.3 Ordinal, Counting, Continuous; 3.2.4 Derived; 3.3 Handling Ordinal Predictors; 3.3.1 Coding Schemes; 3.3.2 Effect of Coding Schemes on Variable Selection; 3.4 Handling Counting and Continuous Predictors: Categorization 327 $a3.4.1 'Optimal' Cutpoints: A Dangerous Analysis3.4.2 Other Ways of Choosing a Cutpoint; 3.5 Example: Issues in Model Building with Categorized Variables; 3.5.1 One Ordinal Variable; 3.5.2 Several Ordinal Variables; 3.6 Handling Counting and Continuous Predictors: Functional Form; 3.6.1 Beyond Linearity; 3.6.2 Does Nonlinearity Matter?; 3.6.3 Simple versus Complex Functions; 3.6.4 Interpretability and Transportability; 3.7 Empirical Curve Fitting; 3.7.1 General Approaches to Smoothing; 3.7.2 Critique of Local and Global Influence Models; 3.8 Discussion; 3.8.1 Sparse Categories 327 $a3.8.2 Choice of Coding Scheme 330 $aMultivariable regression models are of fundamental importance in all areas of science in which empirical data must be analyzed. This book proposes a systematic approach to building such models based on standard principles of statistical modeling. The main emphasis is on the fractional polynomial method for modeling the influence of continuous variables in a multivariable context, a topic for which there is no standard approach. Existing options range from very simple step functions to highly complex adaptive methods such as multivariate splines with many knots and penalisation. This new approa 410 0$aWiley series in probability and statistics. 606 $aRegression analysis 606 $aPolynomials 606 $aVariables (Mathematics) 615 0$aRegression analysis. 615 0$aPolynomials. 615 0$aVariables (Mathematics) 676 $a519.5/36 700 $aRoyston$b Patrick$01341128 701 $aSauerbrei$b Willi$01839202 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911020049903321 996 $aMultivariable model-building$94418364 997 $aUNINA