A Bayesian joint bent-cable model for longitudinal measurements and survival time with heterogeneous random-effects distributions.

Biomarkers are measured repeatedly in clinical studies until a pre-defined endpoint, such as death from certain causes, is reached. Such repeated measurements may present a dynamic process for understanding when to expect the study's endpoint. Joint modelling is often employed to handle such a model.

Socio-demographic determinants of children home learning experiences during COVID 19 school closure.

There were concerns about the inclusivity of learning for children living in countries with limited technology facilities during the COVID 19 school closure. This study investigates the socio-demographic determinant of engagement in home learning and the type of activity engagement for school children across Nigeria during the COVID 19 school closure. Regression and descriptive analysis of 1121 respondents revealed that household size, school communication and perceived socioeconomic status of parents were related to engagement in home learning while household wealth was associated to all types of activity engagement.

Bayesian model selection in linear mixed models for longitudinal data.

Linear mixed models (LMMs) are popular to analyze repeated measurements with a Gaussian response. For longitudinal studies, the LMMs consist of a fixed part expressing the effect of covariates on the mean evolution in time and a random part expressing the variation of the individual curves around the mean curve. Selecting the appropriate fixed and random effect parts is an important modeling exercise.