Linear Mixed Models in Practice (eBook, PDF)

A SAS-Oriented Approach
Redaktion: Verbeke, Geert; Molenberghs, Geert

• Format: PDF

Produktbeschreibung

A comprehensive treatment of linear mixed models, focusing on examples from designed experiments and longitudinal studies. Aimed at applied statisticians and biomedical researchers in industry, public health organisations, contract research organisations, and academia, this book is explanatory rather than mathematical rigorous. Although most analyses were done with the MIXED procedure of the SAS software package, and many of its features are clearly elucidated, considerable effort was put into presenting the data analyses in a software-independent fashion.

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Produktdetails

• Verlag: Springer US
• Seitenzahl: 306
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9781461222941
• Artikelnr.: 44052650

Inhaltsangabe

1 Introduction.- 2 An Example-Based Tour in Linear Mixed Models.- 2.1 Fixed Effects and Random Effects in Mixed Models.- 2.2 General Linear Mixed Models.- 2.3 Variance Components Estimation and Best Linear Unbiased Prediction.- 2.4 Fixed Effects: Estimation and Hypotheses Testing.- 2.5 Case Studies.- 3 Linear Mixed Models for Longitudinal Data.- 3.1 Introduction.- 3.2 The Study of Natural History of Prostate Disease.- 3.3 A Two-Stage Analysis.- 3.4 The General Linear Mixed-Effects Model.- 3.5 Example.- 3.6 The RANDOM and REPEATED Statements.- 3.7 Testing and Estimating Contrasts of Fixed Effects.- 3.8 PROC MIXED versus PROC GLM.- 3.9 Tests for the Need of Random Effects.- 3.10 Comparing Non-Nested Covariance Structures.- 3.11 Estimating the Random Effects.- 3.12 General Guidelines for Model Construction.- 3.13 Model Checks and Diagnostic Tools ?.- 4 Case Studies.- 4.1 Example 1: Variceal Pressures.- 4.2 Example 2: Growth Curves.- 4.3 Example 3: Blood Pressures.- 4.4 Example 4: Growth Data.- 5 Linear Mixed Models and Missing Data.- 5.1 Introduction.- 5.2 Missing Data.- 5.3 Approaches to Incomplete Data.- 5.4 Complete Case Analysis.- 5.5 Simple Forms of Imputation.- 5.6 Available Case Methods.- 5.7 Likelihood-Based Ignorable Analysis and PROC MIXED.- 5.8 How Ignorable Is Missing At Random ? ?.- 5.9 The Expectation-Maximization Algorithm ?.- 5.10 Multiple Imputation ?.- 5.11 Exploring the Missing Data Process.- A Inference for Fixed Effects.- A.1 Estimation.- A.2 Hypothesis Testing.- A.3 Determination of Degrees of Freedom.- A.4 Satterthwaite's Procedure.- B Variance Components and Standard Errors.- C Details on Table 2.10: Expected Mean Squares.- D Example 2.8: Cell Proliferation.- References.