Modelling of multi-state panel data : the importance of the model assumptions

[摘要] ENGLISH ABSTRACT: A multi-state model is a way of describing a process in which a subject moves through a seriesof states in continuous time. The series of states might be the measurement of a disease forexample in state 1 we might have subjects that are free from disease, in state 2 we might havesubjects that have a disease but the disease is mild, in state 3 we might have subjects having asevere disease and in last state 4 we have those that die because of the disease. So Markovmodels estimates the transition probabilities and transition intensity rates that describe themovement of subjects between these states. The transition might be for example a particularsubject or patient might be slightly sick at age 30 but after 5 years he or she might be worse.So Markov model will estimate what probability will be for that patient for moving from state2 to state 3.Markov multi-state models were studied in this thesis with the view of assessing the Markovmodels assumptions such as homogeneity of the transition rates through time, homogeneity ofthe transition rates across the subject population and Markov property or assumption.The assessments of these assumptions were based on simulated panel or longitudinal datasetwhich was simulated using the R package named msm package developed by ChristopherJackson (2014). The R code that was written using this package is attached as appendix.Longitudinal dataset consists of repeated measurements of the state of a subject and the timebetween observations. The period of time with observations in longitudinal dataset is beingmade on subject at regular or irregular time intervals until the subject dies then the study ends.

[发布机构] Stellenbosch University