This paper presents the application of Semi-Markov decision Process (SMDP) for a multi-
state disease under random environments to determine the optimal treatment strategy. The …

Markov models are commonly used to simulate diseases and allow modeling of multiple
health states and outcomes. Starting with the well known Le Bras multistate model …

In this article, we review one of the two major models in multivariate survival analysis, the
multi-state model (the other is the shared frailty model). Just like the whole multivariate …

The current availability of large volumes of health care data makes it a promising data
source to new views on disease interaction. Most of the times, patients have multiple …

We develop a simulation tool to support policy-decisions about healthcare for chronic
diseases in defined populations. Incident disease-cases are generated in-silico from an age …

Mathematical models of disease progression predict disease outcomes and are useful
epidemiological tools for planners and evaluators of health interventions. The 𝖱 package …

Prognostic studies are essential to understand the role of particular prognostic factors and,
thus, improve prognosis. In most studies, disease progression trajectories of individual …

Stochasticity and spatial distribution of the pathogen play a critical role in determining the
outcome of an infection. 1 in a million immune system cells are specific to a particular …

This thesis contains some co ntributions for statistical models studying the disease
progression. Methods developed in this thesis are largely motivated by the applications to …

Multistate modelling is becoming increasingly popular due to the availability of richer
longitudinal health data. When the times at which the events characterising disease …