A nonlinear mixed-effects model for multivariate longitudinal data with partially observed outcomes with application to HIV disease dynamics
DOI10.1080/02664763.2016.1177494OpenAlexW2344140522MaRDI QIDQ5138548
Henry G. Mwambi, Artz G. Luwanda
Publication date: 4 December 2020
Published in: Journal of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02664763.2016.1177494
incomplete datamultivariate responseleft-censored dataHIV dynamical systempartial marker dropoutstochastic approximation expectation-maximisation algorithm
Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10) General nonlinear regression (62J02)
Cites Work
- Random-Effects Models for Longitudinal Data
- Missing data methods in longitudinal studies: a review
- Convergence of a stochastic approximation version of the EM algorithm
- Practical identifiability of HIV dynamics models
- Maximum Likelihood Estimation of Long-Term HIV Dynamic Models and Antiviral Response
- Multivariate Repeated-Measurement or Growth Curve Models with Multivariate Random-Effects Covariance Structure
- An EM Algorithm for Nonlinear Random Effects Models
- A Random-Effects Model for Multiple Characteristics With Possibly Missing Data
- An EM algorithm for the estimation of parametric and nonparametric hierarchical nonlinear models
- REML Estimation of Variance Parameters in Nonlinear Mixed Effects Models Using the SAEM Algorithm
- Statistical methods for HIV dynamic studies in AIDS clinical trials
- Maximum Likelihood Estimation in Dynamical Models of HIV
- Simultaneous Inference for Semiparametric Nonlinear Mixed‐Effects Models with Covariate Measurement Errors and Missing Responses
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