Joint model-based clustering of nonlinear longitudinal trajectories and associated time-to-event data analysis,linked by latent class membership: with application to AIDS clinical studies

Longitudinal and time-to-event data are often observed together. Finite mixture models are currently used to analyze nonlinear heterogeneous longitudinal data, which, by releasing the homogeneity restriction of nonlinear mixed-effects (NLME) models, can cluster individuals into one of the pre-specified classes with class membership probabilities. This clustering may have clinical significance, and be associated with clinically important time-to-event data. This article develops a joint modeling approach to a finite mixture of NLME models for longitudinal data and proportional hazard Cox model for time-to-event data, linked by individual latent class indicators, under a Bayesian framework. The proposed joint models and method are applied to a real AIDS clinical trial data set, followed by simulation studies to assess the performance of the proposed joint model and a naive two-step model, in which finite mixture model and Cox model are fitted separately.     

Intra-Couple Wealth Inequality: What’s Socio-Demographics Got to Do with it?

Existing literature shows that on average and across countries, men have higher levels of wealth than women. However, very little is known about the gender-specific wealth gap within couples. This paper studies this phenomenon for the first time in Austria. The particular focus of the paper is on the relationship between the socio-demographic characteristics of the couple and the couple’s gender wealth gap. We focus on how age, education, marital status, fertility, migratory background, and the gender of the respondent are related to the wealth gap within a couple. In both bivariate and multivariate analyses, we find evidence in support of the hypothesis that bargaining power plays an important role in the intra-couple gender wealth gap in Austria. Immigrant women living in a couple with native men, and, among natives, couples in which the man is much older on average, have larger gender wealth gaps. Furthermore, couples in which the woman is the “financially most knowledgeable person” in the household have consistently lower gender wealth gaps.