News

Seminar on "Multi-state survival models for disease progression"

Speaker: Dr.Ardo van den Hout (UCL)

Title: Multi-state survival models for disease progression

Time: Wed 17th March 2021 at 13:00

Venue: MS Teams

Abstract: Multi-state models are routinely used in research where change of status over time is of interest. In epidemiology and medical statistics, for example, the models are used to describe health-related processes over time, where status is defined by a disease or a condition. In social statistics and in demography, the models are used to study processes such as region of residence, work history, or marital status.

Part of the talk will be an introduction to continuous-time multi-state survival models. I will discuss longitudinal data requirements, the link with stochastic processes, and maximum likelihood inference. An important distinction is whether or not exact times are observed for transitions between the states. In many applications, we do not have exact times and it is important to take this into account in the statistical analysis.

Two applications will be discussed. (i) For an illness-death process, I will illustrate estimating time spent in states. In ageing research, this can be used to distinguish total residual life expectancies from healthy residual life expectancy. (ii) For cancer progression, I will discuss a model based on data from a two-arm design with a screening group and a control group.

Seminar on "An overview of latent class analysis and its application to multimorbidity clustering"

Speaker: Dr. Mizanur Khondoker, UEA

Title: An overview of latent class analysis and its application to multimorbidity clustering

Time: Wed 12nd May 2021 at 13:00

Venue: MS Teams


Abstract: The co-occurrence of two or more health conditions within one person is termed multimorbidity. This poses a substantial burden and challenge on the healthcare system as it may be with poorer health outcomes, more complex clinical management, a higher use of health services and associated cost. Identification of patterns multimorbid conditions is important in multimorbidity research, as the findings facilitate quantifying the effect of multimorbidity on health-related outcomes. This talk will present a model-based clustering method, Latent Class Analysis (LCA), for identifying patterns of multimorbidity. LCA, a special case of a more general structural equation modelling (SEM), will be introduced using combinations of causal diagrams and mathematical notions followed by maximum likelihood estimation and class prediction using maximum posterior probability. An application of the method for identifying multimorbidity patterns will be presented using the UK Biobank community cohort. Application will demonstrate selection of number of clusters using information statistics (AIC and BIC) in a training sample followed by cluster validation using an independent test sample.