@book {rizopoulos_joint_2012,
	title = {Joint Models for Longitudinal and Time-to-Event Data: With Applications in R},
	year = {2012},
	month = {jun},
	publisher = {CRC Press},
	organization = {CRC Press},
	abstract = {In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R provides a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data. The content is primarily explanatory, focusing on applications of joint modeling, but sufficient mathematical details are provided to facilitate understanding of the key features of these models. All illustrations put forward can be implemented in the R programming language via the freely available package JM written by the author. All the R code used in the book is available at: http://jmr.r-forge.r-project.org/},
	keywords = {Mathematics / Probability \& Statistics / General, Medical / Epidemiology, Science / Life Sciences / Biology},
	isbn = {978-1-4398-7286-4},
	author = {Rizopoulos, Dimitris}
}
@book {wu_mixed_2009,
	title = {Mixed Effects Models for Complex Data},
	year = {2009},
	publisher = {CRC Press},
	organization = {CRC Press},
	abstract = {Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topicsSubsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background materialIn the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when na{\"\i}ve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.},
	keywords = {Mathematics / Probability \& Statistics / General, Medical / Epidemiology, Science / Life Sciences / Biology},
	isbn = {978-1-4200-7408-6},
	author = {WU, LANG}
}
