Unified Methods for Censored Longitudinal Data and Causality

1 Introduction.- 1.1 Motivation, Bibliographic History, and an Overview of the book.- 1.2 Tour through the General Estimation Problem.- 1.3 Example: Causal Effect of Air Pollution on Short-Term Asthma Response.- 1.4 Estimating Functions.- 1.5 Robustness of Estimating Functions.- 1.6 Doubly robust estimation in censored data models.- 1.7 Using Cross-Validation to Select Nuisance Parameter Models.- 2 General Methodology.- 2.1 The General Model and Overview.- 2.2 Full Data Estimating Functions.- 2.3 Mapping into Observed Data Estimating Functions.- 2.4 Optimal Mapping into Observed Data Estimating Functions.- 2.5 Guaranteed Improvement Relative to an Initial Estimating Function.- 2.6 Construction of Confidence Intervals.- 2.7 Asymptotics of the One-Step Estimator.- 2.8 The Optimal Index.- 2.9 Estimation of the Optimal Index.- 2.10 Locally Efficient Estimation with Score-Operator Representation.- 3 Monotone Censored Data.- 3.1 Data Structure and Model.- 3.2 Examples.- 3.3 Inverse Probability Censoring Weighted (IPCW) Estimators.- 3.4 Optimal Mapping into Estimating Functions.- 3.5 Estimation of Q.- 3.6 Estimation of the Optimal Index.- 3.7 Multivariate failure time regression model.- 3.8 Simulation and data analysis for the nonparametric full data model.- 3.9 Rigorous Analysis of a Bivariate Survival Estimate.- 3.10 Prediction of Survival.- 4 Cross-Sectional Data and Right-Censored Data Combined.- 4.1 Model and General Data Structure.- 4.2 Cause Specific Monitoring Schemes.- 4.3 The Optimal Mapping into Observed Data Estimating Functions.- 4.4 Estimation of the Optimal Index in the MGLM.- 4.5 Example: Current Status Data with Time-Dependent Covariates.- 4.6 Example: Current Status Data on a Process Until Death.- 5 Multivariate Right-Censored Multivariate Data.- 5.1 GeneralData Structure.- 5.2 Mapping into Observed Data Estimating Functions..- 5.3 Bivariate Right-Censored Failure Time Data.- 6 Unified Approach for Causal Inference and Censored Data.- 6.1 General Model

From the reviews:



"This book provides a rigourous statistical framework for the analysis of complex large longitudinal data. It provides a comprehensive description of optimal estimation techniques based on time-dependent data structures ... . This is an excellent book for Ph.D. level students in Biostatistics and Statistics who have a strong background in mathematics. It is also suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data." (Subhash C. Kochar, Sankhya: The Indian Journal of Statistics, Vol. 66 (1), 2004)

"This book provides a fundamental statistical framework for the analysis of complex longitudinal data. It provides the first comprehensive description of optimal estimation techniques based on time-dependent data structures ... . The book can be used to teach masters-level and Ph.D. students in biostatistics and statistics and is suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data." (P. Rochus, Mathematical Reviews, 2003m)

"This book by two major research workers in the field addresses in generality important problems involving multivariate longitudinal data ... . it is an important book dealing with important problems. Therefore, experts in modern semi-parametric theory should certainly read the book. Those with an interest focussed more on applications and able to draw together a reading group with appropriate expertise are very likely to profit greatly from a sustained study of the book." (D.R. Cox, Short Book Reviews, Vol. 23 (2), 2003)