The longitudinal sequence may terminate due to an event or censoring. It is of interest to learn about longitudinal progression as well as risk factors and the implications of these on time-to-event. It is known that joint models, accounting for a joint distribution, for longitudinal and time-to-event data, are necessary to analyse these data.
The Latent variable models allows to obtain more accurate health indicators in the presence of measurement error, identification of classes/cluster within the data, identification of the true prevalence and sensitivity and specificity without the presence of gold-standard in diagnostic tests, and also calibration of several indicators with the presence of missing data. Bayesian and frequentist approaches can be used to estimate these types of models.
Such statistical models are being developed and applied in nutrition data, cancer, peritoneal dialysis and kidney transplantation survival and cardiovascular diseases research.
The conducted research aims for: