Joint modeling of cross-sectional health outcomes and longitudinal predictors via mixtures of means and variances

• Published in: Biometrics Vol. 71; no. 2; pp. 487 - 497
• Main Authors: Jiang, Bei, Elliott, Michael R., Sammel, Mary D., Wang, Naisyin
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.06.2015; International Biometric Society
• Subjects: Area Under Curve; BIOMETRIC PRACTICE; Biometrics; Biometry; Computer Simulation; Cross-Sectional Studies; Female; Follicle Stimulating Hormone, Human - metabolism; Hot Flashes - etiology; Hot Flashes - metabolism; Humans; Joint model; Latent class; Long-term trend; Longitudinal Studies; Menopause; Model misspecification; Models, Statistical; Outcome Assessment (Health Care) - statistics & numerical data; Predictive performance; Regression Analysis; Risk Factors; Shared random effects and variances; Short-term variability; Simulation; Womens health; Latent class; Short-term variability; Long-term trend; Shared random effects and variances; Joint model; Model misspecification; Predictive performance

Joint modeling methods have become popular tools to link important features extracted from longitudinal data to a primary event. While most modeling strategies have focused on the association between the longitudinal mean trajectories and risk of an event, we consider joint models that incorporate i...