Score Test for Conditional Independence Between Longitudinal Outcome and Time to Event Given the Classes in the Joint Latent Class Model

DOI10.1111/J.1541-0420.2009.01234.XzbMath1187.62170OpenAlexW1990485464WikidataQ42620690 ScholiaQ42620690MaRDI QIDQ3561796

Cécile Proust-Lima, Hélène Jacqmin-Gadda, Daniel Commenges

Publication date: 26 May 2010

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://hdl.handle.net/2027.42/79185

zbMATH Keywords

mixture modeljoint modellatent class modelmodel diagnosis

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50) Testing in survival analysis and censored data (62N03)

Related Items (7)

Choice of Prognostic Estimators in Joint Models by Estimating Differences of Expected Conditional Kullback-Leibler Risks ⋮ Joint latent class model of survival and longitudinal data: an application to CPCRA study ⋮ Spatial joint models through Bayesian structured piecewise additive joint modelling for longitudinal and time-to-event data ⋮ A Gaussian copula joint model for longitudinal and time-to-event data with random effects ⋮ Assessing importance of biomarkers: A Bayesian joint modelling approach of longitudinal and survival data with semi-competing risks ⋮ Analysis of longitudinal and survival data: joint modeling, inference methods, and issues ⋮ A likelihood based approach for joint modeling of longitudinal trajectories and informative censoring process

Cites Work

• Random-Effects Models for Longitudinal Data
• Joint modelling of multivariate longitudinal outcomes and a time-to-event: a nonlinear latent class approach
• Estimating the dimension of a model
• A Linear Mixed-Effects Model With Heterogeneity in the Random-Effects Population
• Modeling Longitudinal Data with Nonignorable Dropouts Using a Latent Dropout Class Model
• Latent class regression on latent factors
• Latent Pattern Mixture Models for Informative Intermittent Missing Data in Longitudinal Studies
• Latent Variable Regression for Multiple Discrete Outcomes
• Latent Class Models for Joint Analysis of Longitudinal Biomarker and Event Process Data
• Test of Homogeneity of Binary Data with Explanatory Variables
• Informative Drop-Out in Longitudinal Data Analysis
• A Latent‐Class Mixture Model for Incomplete Longitudinal Gaussian Data
• Joint modelling of longitudinal measurements and event time data

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