Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 286 - Missing Data Methods
Type: Contributed
Date/Time: Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
Sponsor: Biometrics Section
Abstract #319200
Title: A joint latent class model of longitudinal and survival data with time-varying membership probability and covariance modelling
Author(s): Ruoyu Miao* and Christiana Charalambous
Companies: University of Manchester and University of Manchester
Keywords:
Abstract:

Joint latent class modelling has been developed considerably in the past two decades. In some instances, the models are linked by the latent class k (i.e. the number of subgroups), in others they are joined by the shared random effects or a heterogeneous random covariance matrix. We propose an extension to the joint latent class model (JLCM) in which probabilities of subjects being in latent class k can be set to vary with time. This can be a more flexible way to analyze the effect of treatments to patients. For example, a patient may be in period I at the first visit time and may move to period II at the second visit time, implying the treatment the patient had before might be noneffective at the following visit time. For a dataset with these particular features, the joint latent class model which allows jumps among different subgroups can potentially provide more information as well as more accurate estimation results compared to the basic JLCM. A Bayesian approach is used to do the estimation and a DIC criterion is used to decide the optimal number of classes. Preliminary simulation results indicate that the proposed model produces accurate results and we are currently exploring ways to compare the performance of the proposed model to the basic JLCM. An extension of the proposed JLCM with additional random effects covariance modelling is also considered.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2021 program