Abstract Machine learning has been the corner stone in analysing and extracting information
from data and often a problem of missing values is encountered. Missing values occur …

Most statistical developments in the joint modelling area have focused on the shared
random-effect models that include characteristics of the longitudinal marker as predictors in …

Diabetes is a major cause of end stage renal disease (ESRD), yet the natural history of
diabetic kidney disease is not well understood. We aimed to identify patterns of estimated …

Background Available methods for the joint modelling of longitudinal and time-to-event
outcomes have typically only allowed for a single longitudinal outcome and a solitary event …

The past decade has seen a noticeable shift in missing data handling techniques that
assume a missing at random (MAR) mechanism, where the propensity for missing data on …

This article uses a general latent variable framework to study a series of models for
nonignorable missingness due to dropout. Nonignorable missing data modeling …

Missing data are exceedingly common across a variety of disciplines, such as educational,
social, and behavioral science areas. Missing not at random (MNAR) mechanism where …

Quantile regression provides a detailed and robust picture of the distribution of a response
variable, conditional on a set of observed covariates. Recently, it has be been extended to …

Longitudinal studies are those in which the same variable is repeatedly measured at
different times. These studies are more likely than others to suffer from missing values. Since …

Objectives The extent to which heterogeneity in childhood risk trajectories may underlie later
heterogeneity in schizophrenia (SZ), bipolar disorder (BP), and major depressive disorder …