Joint longitudinal data analysis in detecting determinants of CD4 cell count change and adherence to highly active antiretroviral therapy at Felege Hiwot Teaching and …
A Seyoum, P Ndlovu, Z Temesgen - AIDS research and therapy, 2017 - Springer
Background Adherence and CD4 cell count change measure the progression of the disease
in HIV patients after the commencement of HAART. Lack of information about associated …
in HIV patients after the commencement of HAART. Lack of information about associated …
Bayesian joint ordinal and survival modeling for breast cancer risk assessment
We propose a joint model to analyze the structure and intensity of the association between
longitudinal measurements of an ordinal marker and time to a relevant event. The …
longitudinal measurements of an ordinal marker and time to a relevant event. The …
Joint modelling of longitudinal response and time-to-event data using conditional distributions: a Bayesian perspective
S Dutta, G Molenberghs… - Journal of Applied Statistics, 2022 - Taylor & Francis
Over the last 20 or more years a lot of clinical applications and methodological development
in the area of joint models of longitudinal and time-to-event outcomes have come up. In …
in the area of joint models of longitudinal and time-to-event outcomes have come up. In …
Joint analysis of survival time and longitudinal categorical outcomes
In biomedical or public health research, it is common for both survival time and longitudinal
categorical outcomes to be collected for a subject, along with the subject's characteristics or …
categorical outcomes to be collected for a subject, along with the subject's characteristics or …
A Proposed Approach for Joint Modeling of the Longitudinal and Time‐To‐Event Data in Heterogeneous Populations: An Application to HIV/AIDS's Disease
N Roustaei, SMT Ayatollahi… - BioMed research …, 2018 - Wiley Online Library
In recent years, the joint models have been widely used for modeling the longitudinal and
time‐to‐event data simultaneously. In this study, we proposed an approach (PA) to study the …
time‐to‐event data simultaneously. In this study, we proposed an approach (PA) to study the …
On the convergence properties of the mini-Batch EM and MCEM algorithms
The EM algorithm is one of the most popular algorithm for inference in latent data models.
For large datasets, each iteration of the algorithm can be numerically involved. To alleviate …
For large datasets, each iteration of the algorithm can be numerically involved. To alleviate …
Penalized likelihood approach for simultaneous analysis of survival time and binary longitudinal outcome
In this paper we consider simultaneous analysis of survival time and binary longitudinal
outcome where random effects are introduced to account for the dependence between the …
outcome where random effects are introduced to account for the dependence between the …
Joint modeling of survival time and longitudinal outcomes with flexible random effects
Joint models with shared Gaussian random effects have been conventionally used in
analysis of longitudinal outcome and survival endpoint in biomedical or public health …
analysis of longitudinal outcome and survival endpoint in biomedical or public health …
Two-timescale stochastic em algorithms
The Expectation-Maximization (EM) algorithm is a popular choice for learning latent variable
models. Variants of the EM have been initially introduced by [1], using incremental updates …
models. Variants of the EM have been initially introduced by [1], using incremental updates …
A Class of Two-Timescale Stochastic EM Algorithms for Nonconvex Latent Variable Models
The Expectation-Maximization (EM) algorithm is a popular choice for learning latent variable
models. Variants of the EM have been initially introduced, using incremental updates to …
models. Variants of the EM have been initially introduced, using incremental updates to …