7 citations · 7 across the 5 of their papers we have counts for
5 papers
Bayesian semiparametric modelling of biomarker variability in joint models
Sida Chen, Jessica K. Barrett, Marco Palma +2
There is growing interest in the role of within-individual variability (WIV) in biomarker trajectories for assessing disease risk and progression. A trajectory-based definition tha…
Bayesian shared parameter joint models for heterogeneous populations
Sida Chen, Danilo Alvares, Marco Palma +1
Joint models (JMs) for longitudinal and time-to-event data are an important class of biostatistical models in health and medical research. When the study population consists of het…
Bayesian blockwise inference for joint models of longitudinal and multistate processes
Sida Chen, Danilo Alvares, Christopher Jackson +1
Joint models (JM) for longitudinal and survival data have gained increasing interest and found applications in a wide range of clinical and biomedical settings. These models facili…
Prediction and Control of Focal Seizure Spread: Random Walk with Restart on Heterogeneous Brain Networks
Chen Wang, Sida Chen, Liang Huang +1
Whole-brain models offer a promising method of predicting seizure spread, which is critical for successful surgery treatment of focal epilepsy. Existing methods are largely based o…
Bayesian spline-based hidden Markov models with applications to actimetry data and sleep analysis
Sida Chen, Bärbel Finkenstädt Rand
B-spline-based hidden Markov models employ B-splines to specify the emission distributions, offering a more flexible modelling approach to data than conventional parametric HMMs. W…