(Ebook) Applied Longitudinal Analysis 2nd Edition by Garrett M Fitzmaurice, Nan M Laird, James H Ware ISBN 0470380276 9780470380277

(Ebook) Applied Longitudinal Analysis 2nd Edition by Garrett M Fitzmaurice, Nan M Laird, James H Ware ISBN 0470380276 9780470380277

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ISBN 10: 0470380276 
ISBN 13: 9780470380277
Author: Garrett M Fitzmaurice, Nan M Laird, James H Ware

Praise for the First Edition ". . . [this book] should be on the shelf of everyone interested in . . . longitudinal data analysis." —Journal of the American Statistical Association Features newly developed topics and applications of the analysis of longitudinal data Applied Longitudinal Analysis, Second Edition presents modern methods for analyzing data from longitudinal studies and now features the latest state-of-the-art techniques. The book emphasizes practical, rather than theoretical, aspects of methods for the analysis of diverse types of longitudinal data that can be applied across various fields of study, from the health and medical sciences to the social and behavioral sciences. The authors incorporate their extensive academic and research experience along with various updates that have been made in response to reader feedback. The Second Edition features six newly added chapters that explore topics currently evolving in the field, including: Fixed effects and mixed effects models Marginal models and generalized estimating equations Approximate methods for generalized linear mixed effects models Multiple imputation and inverse probability weighted methods Smoothing methods for longitudinal data Sample size and power Each chapter presents methods in the setting of applications to data sets drawn from the health sciences. New problem sets have been added to many chapters, and a related website features sample programs and computer output using SAS, Stata, and R, as well as data sets and supplemental slides to facilitate a complete understanding of the material. With its strong emphasis on multidisciplinary applications and the interpretation of results, Applied Longitudinal Analysis, Second Edition is an excellent book for courses on statistics in the health and medical sciences at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for researchers and professionals in the medical, public health, and pharmaceutical fields as well as those in social and behavioral sciences who would like to learn more about analyzing longitudinal data.

(Ebook) Applied Longitudinal Analysis 2nd Table of contents:

Part I: Introduction to Longitudinal and Clustered Data
Chapter 1: Longitudinal and Clustered Data
1.1 Introduction
1.2 Longitudinal and Clustered Data
1.3 Examples
1.4 Regression Models for Correlated Responses
1.5 Organization of the Book
1.6 Further Reading
Chapter 2: Longitudinal Data: Basic Concepts
2.1 Introduction
2.2 Objectives of Longitudinal Analysis
2.3 Defining Features of Longitudinal Data
2.4 Example: Treatment of Lead-Exposed Children Trial
2.5 Sources of Correlation in Longitudinal Data
2.6 Further Reading
Part II: Linear Models for Longitudinal Continuous Data
Chapter 3: Overview of Linear Models for Longitudinal Data
3.1 Introduction
3.2 Notation and Distributional Assumptions
3.3 Simple Descriptive Methods of Analysis
3.4 Modeling the Mean
3.5 Modeling the Covariance
3.6 Historical Approaches
3.7 Further Reading
Chapter 4: Estimation and Statistical Inference
4.1 Introduction
4.2 Estimation: Maximum Likelihood
4.3 Missing Data Issues
4.4 Statistical Inference
4.5 Restricted Maximum Likelihood (REML) Estimation
4.6 Further Reading
Chapter 5: Modeling the Mean: Analyzing Response Profiles
5.1 Introduction
5.2 Hypotheses Concerning Response Profiles
5.3 General Linear Model Formulation
5.4 Case Study
5.5 One-Degree-of-Freedom Tests for Group by Time Interaction
5.6 Adjustment for Baseline Response
5.7 Alternative Methods of Adjusting for Baseline Response
5.8 Strengths and Weaknesses of Analyzing Response Profiles
5.9 Computing: Analyzing Response Profiles Using PROC MIXED in SAS
5.10 Further Reading
Chapter 6: Modeling the Mean: Parametric Curves
6.1 Introduction
6.2 Polynomial Trends in Time
6.3 Linear Splines
6.4 General Linear Model Formulation
6.5 Case Studies
6.6 Computing: Fitting Parametric Curves Using PROC MIXED in SAS
6.7 Further Reading
Chapter 7: Modeling the Covariance
7.1 Introduction
7.2 Implications of Correlation among Longitudinal Data
7.3 Unstructured Covariance
7.4 Covariance Pattern Models
7.5 Choice among Covariance Pattern Models
7.6 Case Study
7.7 Discussion: Strengths and Weaknesses of Covariance Pattern Models
7.8 Computing: Fitting Covariance Pattern Models Using PROC MIXED in SAS
7.9 Further Reading
Chapter 8: Linear Mixed Effects Models
8.1 Introduction
8.2 Linear Mixed Effects Models
8.3 Random Effects Covariance Structure
8.4 Two-Stage Random Effects Formulation
8.5 Choice among Random Effects Covariance Models
8.6 Prediction of Random Effects
8.7 Prediction and Shrinkage
8.8 Case Studies
8.9 Computing: Fitting Linear Mixed Effects Models Using PROC MIXED in SAS
8.10 Further Reading
Chapter 9: Fixed Effects versus Random Effects Models
9.1 Introduction
9.2 Linear Fixed Effects Models
9.3 Fixed Effects versus Random Effects: Bias-Variance Trade-off
9.4 Resolving the Dilemma of Choosing Between Fixed and Random Effects Models
9.5 Longitudinal and Cross-sectional Information
9.6 Case Study
9.7 Computing: Fitting Linear Fixed Effects Models Using PROC GLM in SAS
9.8 Computing: Decomposition of Between-Subject and Within-Subject Effects Using PROC MIXED in SAS
9.9 Further Reading
Chapter 10: Residual Analyses and Diagnostics
10.1 Introduction
10.2 Residuals
10.3 Transformed Residuals
10.4 Aggregating Residuals
10.5 Semi-Variogram
10.6 Case Study
10.7 Summary
10.8 Further Reading
Part III: Generalized Linear Models for Longitudinal Data
Chapter 11: Review of Generalized Linear Models
11.1 Introduction
11.2 Salient Features of Generalized Linear Models
11.3 Illustrative Examples
11.4 Ordinal Regression Models
11.5 Overdispersion
11.6 Computing: Fitting Generalized Linear Models Using PROC GENMOD in SAS
11.7 Overview of Generalized Linear Models
11.8 Further Reading
Chapter 12: Marginal Models: Introduction and Overview
12.1 Introduction
12.2 Marginal Models for Longitudinal Data
12.3 Illustrative Examples of Marginal Models
12.4 Distributional Assumptions for Marginal Models
12.5 Further Reading
Chapter 13: Marginal Models: Generalized Estimating Equations (GEE)
13.1 Introduction
13.2 Estimation of Marginal Models: Generalized Estimating Equations
13.3 Residual Analyses and Diagnostics
13.4 Case Studies
13.5 Marginal Models and Time-Varying Covariates
13.6 Computing: Generalized Estimating Equations Using PROC GENMOD in SAS
13.7 Further Reading
Chapter 14: Generalized Linear Mixed Effects Models
14.1 Introduction
14.2 Incorporating Random Effects in Generalized Linear Models
14.3 Interpretation of Regression Parameters
14.4 Overdispersion
14.5 Estimation and Inference
14.6 A Note on Conditional Maximum Likelihood
14.7 Case Studies
14.8 Computing: Fitting Generalized Linear Mixed Models Using PROC GLIMMIX in SAS
14.9 Further Reading
Chapter 15: Generalized Linear Mixed Effects Models: Approximate Methods of Estimation
15.1 Introduction
15.2 Penalized Quasi-Likelihood
15.3 Marginal Quasi-Likelihood
15.4 Cautionary Remarks on the Use of PQL and MQL
15.5 Case Studies
15.6 Computing: Fitting GLMMs Using PROC GLIMMIX in SAS
15.7 Basis of PQL and MQL Approximations
15.8 Further Reading
Chapter 16: Contrasting Marginal and Mixed Effects Models
16.1 Introduction
16.2 Linear Models: A Special Case
16.3 Generalized Linear Models
16.4 Simple Numerical Illustration
16.5 Case Study
16.6 Conclusion
16.7 Further Reading
Part IV: Missing Data and Dropout
Chapter 17: Missing Data and Dropout: Overview of Concepts and Methods
17.1 Introduction
17.2 Hierarchy of Missing Data Mechanisms
17.3 Implications for Longitudinal Analysis
17.4 Dropout
17.5 Common Approaches for Handling Dropout
17.6 Bias of Last Value Carried Forward Imputation
17.7 Further Reading
Chapter 18: Missing Data and Dropout: Multiple Imputation and Weighting Methods
18.1 Introduction
18.2 Multiple Imputation
18.3 Inverse Probability Weighted Methods
18.4 Case Studies
18.5 “Sandwich” Variance Estimator Adjusting for Estimation of Weights
18.6 Computing: Multiple Imputation Using PROC MI in SAS
18.7 Computing: Inverse Probability Weighted (IPW) Methods in SAS
18.8 Further Reading
Part V: Advanced Topics for Longitudinal and Clustered Data
Chapter 19: Smoothing Longitudinal Data: Semiparametric Regression Models
19.1 Introduction
19.2 Penalized Splines for a Univariate Response
19.3 Case Study
19.4 Penalized Splines for Longitudinal Data
19.5 Case Study
19.6 Fitting Smooth Curves to Individual Longitudinal Data
19.7 Case Study
19.8 Computing: Fitting Smooth Curves Using PROC MIXED in SAS
19.9 Further Reading
Chapter 20: Sample Size and Power
20.1 Introduction
20.2 Sample Size for a Univariate Continuous Response
20.3 Sample Size for a Longitudinal Continuous Response
20.4 Sample Size for a Longitudinal Binary Response
20.5 Summary
20.6 Computing: Sample Size Calculation Using Pseudo-Data
20.7 Further Reading
Chapter 21: Repeated Measures and Related Designs
21.1 Introduction
21.2 Repeated Measures Designs
21.3 Multiple Source Data
21.4 Case Study 1: Repeated Measures Experiment
21.5 Case Study 2: Multiple Source Data
21.6 Summary
21.7 Further Reading
Chapter 22: Multilevel Models
22.1 Introduction
22.2 Multilevel Data
22.3 Multilevel Linear Models
22.4 Multilevel Generalized Linear Models
22.5 Summary
22.6 Further Reading

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