Shared parameter models under random effects misspecification
By Dimitris Rizopoulos, Geert Verbeke, Geert Molenberghs. 2008
138 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/f47ff3212125c0c918e48f23ec2e38550c8f8f9d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Dimitris Rizopoulos
- Joseph G. Ibrahim
- Geert Molenberghs
- Geert Verbeke
- Emmanuel Lesaffre
- Matthew J. Gurka
- G. V. van Breukelen
- Nicholas J. Horton
In Statistics and Probability
- Dimitris Rizopoulos
- Geert Molenberghs
- Joseph G. Ibrahim
- Geert Verbeke
- Emmanuel Lesaffre
- Matthew J. Gurka
- Mohsen Pourahmadi
- G. V. van Breukelen
Citations in the corpus
- JM: An R package for the joint modelling of longitudinal and time-to-event data (2010, methodology): “4, we observe that the leading term in both integrands is the density p(yi | bi; θy) = ∏ j p{yi(tij) | bi; θy} (Rizopoulos et al. 2008).”
- Fully exponential Laplace approximations for the joint modelling of survival and longitudinal data (2009, background): “This feature has also been theoretically corroborated by Rizopoulos et al. (2008) and Hsieh et al. (2006), and therefore we assume that bi follows a multivariate normal distribution with mean 0 and variance–covariance matrix D, and we do not further investigate this assumption.”
- Random Effects Models for Longitudinal Data (2010, unknown): “This feature has been further theoretically corroborated by Rizopoulos, Verbeke, and Molenberghs (2008), who showed that, as the number of repeated measurements per subject ni increases, misspecification of the random-effects distribution has a minimal effect in parameter estimators and standard…”
- Missing data methods in longitudinal studies: a review (2009, background): “This approach is extended to semiparametric models for the dropout mechanism by Scharfstein et al. (1999). Lipsitz et al.”
- Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time‐to‐Event Data (2011, background): “First, Rizopoulos, Verbeke, and Molenberghs (2008) have recently shown that, as ni increases, the leading term of the log posterior distribution of the random effects is the logarithm of the density of the linear mixed model log p{Yi (t) | bi ; θ(l)} = ∑ j log p{yi (tij ) | bi ; θ(l)}, which is…”