Multivariable model-building: a pragmatic approach to regression anaylsis based on fractional polynomials for modelling continuous variables

Multivariable regression models are of fundamental importance in all areas ofscience 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 approach, developed in part by the authors over the last decade, is a compromise which promotes interpretable, comprehensible and transportable models. INDICE: Preface 1 Introduction 1.1 Real-Life Problems as Motivation for Model Building 1.2 Issues in Modelling Continuous Predictors 1.3 Types of Regression Model Considered 1.4 Role 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 1.8 General Notation 2 Selection of Variables 2.1 Introduction 2.2Background 2.3 Preliminaries for a Multivariable Analysis 2.4 Aims of Multivariable Models 2.5 Prediction: Summary Statistics and Comparisons 2.6 Procedures for Selecting Variables 2.7 Comparison of Selection Strategies in Examples 2.8 Selection and Shrinkage 2.9 Discussion 3 Handling Categorical and Continuous Predictors 3.1 Introduction 3.2 Types of Predictor 3.3 Handling Ordinal Predictors 3.4 Handling Counting and Continuous Predictors: Categorization 3.5 Example: Issues in Model Building with Categorized Variables 3.6 Handling Counting and Continuous Predictors: Functional Form 3.7 Empirical Curve Fitting 3.8 Discussion 4 Fractional Polynomials for One Variable 4.1 Introduction 4.2 Background 4.3 Definition and Notation 4.4 Characteristics 4.5 Examples of Curve Shapes with FP1 and FP2 Functions 4.6 Choice of Powers 4.7 Choice of Origin 4.8 Model Fitting and Estimation 4.9 Inference 4.10 Function Selection Procedure 4.11 Scaling and Centering 4.12 FP Powers as Approximations to Continuous Powers 4.13 Presentation of Fractional Polynomial Functions 4.14 Worked Example 4.15 Modelling Covariates with a Spike at Zero 4.16 Power of Fractional Polynomial Analysis 4.17 Discussion 5 Some Issues with Univariate Fractional PolynomialModels 5.1 Introduction 5.2 Susceptibility to Influential Covariate Observations 5.3 A Diagnostic Plot for Influential Points in FP Models 5.4 Dependence on Choice of Origin 5.5 Improving Robustness by Preliminary Transformation 5.6 Improving Fit by Preliminary Transformation 5.7 Higher Order Fractional Polynomials 5.8 When Fractional Polynomial Models are Unsuitable 5.9 Discussion 6 MFP: Multivariable Model-Building with Fractional Polynomials 6.1 Introduction 6.2 Motivation 6.3 The MFP Algorithm 6.4 Presenting the Model 6.5 Model Criticism 6.6 Further Topics 6.7 Further Examples 6.8 Simple Versus Complex Fractional Polynomial Models 6.9 Discussion 7 Interactions 7.1 Introduction 7.2 Background 7.3 General Considerations 7.4 The MFPI Procedure 7.5 Example 1: Advanced Prostate Cancer 7.6 Example 2: GBSG Breast Cancer Study 7.7 Categorization 7.8STEPP 7.9 Example 3: Comparison of STEPP with MFPI 7.10 Comment on Type I Error of MFPI 7.11 Continuous-by-Continuous Interactions 7.12 Multi-Category Variables 7.13 Discussion 8 Model Stability 8.1 Introduction 8.2 Background 8.3 Using the Bootstrap to Explore Model Stability 8.4 Example 1: Glioma Data 8.5 Example 2: Educational Body-Fat Data 8.6 Example 3: Breast Cancer Diagnosis 8.7 Model Stability for Functions 8.8 Example 4: GBSG Breast Cancer Data 8.9 Discussion 9 Some Comparisons of MFP with Splines 9.1 Introduction 9.2 Background 9.3 MVRS: A Procedure for Model Building with Regression Splines 9.4 MVSS: A Procedure for Model Building with Cubic Smoothing Splines 9.5 Example 1: Boston Housing Data 9.6 Example 2: GBSG Breast Cancer Study 9.7 Example 3: Pima Indians 9.8 Example 4: PBC 9.9 Discussion 10 How ToWork with MFP 10.1 Introduction 10.2 The Dataset 10.3 Univariate Analyses 10.4 MFP Analysis 10.5 Model Criticism 10.6 Stability Analysis 10.7 Final Model 10.8 Issues to be Aware of 10.9 Discussion 11 Special Topics Involving Fractional Polynomials 11.1 Time-Varying Hazard Ratios in the Cox Model 11.2 Age-specific Reference Intervals 11.3 Other Topics 12 Epilogue 12.1 Introduction 12.2 Towards Recommendations for Practice 12.3 Omitted Topics and Future Directions 12.4 Conclusion Appendix A: Data and Software Resources A.1 Summaries of Datasets A.2 Datasets used more than once A.2.1 Research Body Fat A.2.2 GBSG Breast Cancer A.2.3 Educational Body Fat A.2.4 Glioma A.2.5 Prostate Cancer A.2.6 Whitehall I A.2.7 PBC A.2.8 Oral Cancer A.2.9 Kidney Cancer A.3 Software Appendix B: Glossary of Abbreviations References Index

• ISBN: 978-0-470-02842-1
• Editorial: John Wiley & Sons
• Encuadernacion: Cartoné
• Páginas: 328
• Fecha Publicación: 02/05/2008
• Nº Volúmenes: 1
• Idioma: Inglés