Applied Longitudinal Analysis

By Geert Molenberghs. 2005

2,537 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/50ec0f7c57529a6763a7123db81b1449ddfb4e26

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Citations in the corpus

Random Effects Models for Longitudinal Data (2010, background): “We refer to Henderson et al. (1959), Harville (1974, 1976, 1977), Laird and Ware (1982), Verbeke and Molenberghs (2000), and Fitzmaurice, Laird, and Ware (2004) for more details about estimation and inference in linear mixed models.”
Statistical Analysis of Longitudinal Neuroimage Data with Linear Mixed Effects Models (2012, methodology): “Linear Mixed Effects (LME) models offer a more powerful and versatile framework for the analysis of longitudinal data thanmany other popular methods (Fitzmaurice et al., 2011).”
Analyzing ‘visual world’ eyetracking data using multilevel logistic regression (2008, methodology): “Modeling time using parametric curves can make the observations within trials trial conditionally independent from one another given the model (Cohen et al., 2003; Fitzmaurice et al., 2004), but there are other sources of nonindependence that must also be addressed.”
Multilevel and related models for longitudinal data (2008, neutral): “[20].”
A protocol for data exploration to avoid common statistical problems (2010, methodology): “This approach is more difficult if there is no clear sequence to the observations (e.g. multiple observations on the same object), but in this case one can include a dependence structure using random effects (Pinheiro & Bates 2000; Fitzmaurice et al. 2004; Brown & Prescott 2006; Zuur et al. 2009a).”
Applied Linear Regression (2004, background): “Useful and more comprehensive references include Fitzmaurice et al. (2011), Goldstein (2010), McCulloch et al.”
A Stochastic Variational Framework for Fitting and Diagnosing Generalized Linear Mixed Models (2012, methodology): “Fitzmaurice et al. (2004) modelled the marginal probability of obesity as a logistic function of gender and linear and quadratic age.”
Mixed effects models (2013, methodology): “4 Muscle building example The data for the muscle building example was taken from Applied Longitudinal Analysis by Fitzmaurice et al. (2004). The muscle building data is from a study of exercise therapies where 37 patients were assigned to one of two weightlifting programs.”
Lower protein in infant formula is associated with lower weight up to age 2 y: a randomized clinical trial. (2009, methodology): “Multilevel linear growth models and piecewise-linear-randomcoefficient models as described by Singer and Willet (28) and Fitzmaurice et al (29) were applied to model growth differences between the lower- and higher-protein formula groups by using all available measurements from baseline to 24 mo.”
Encyclopedia of Statistical Sciences (2007, background): “The book by Fitzmaurice, Laird, and Ware (2004) includes chapters that directly contrast marginal and mixed-effects models, along with an “advanced topics” collection including a chapter on study design.”
Creating a Progress–Monitoring System in Reading for Middle–School Students: Tracking Progress toward Meeting High–Stakes Standards (2010, methodology): “…slope with t ij = j − 1, and which is the random effects structure with b 0 i being the deviation of an individual’s individual intercept from the mean intercept, b 1 i being the deviation of an individual’s slope from the mean slope, and e ij is random error (Fitzmaurice et al., 2004, ch. 8).”