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 …

Longitudinal Structural Equation Modeling is a comprehensive resource that reviews
structural equation modeling (SEM) strategies for longitudinal data to help readers …

Quantitative bias analysis serves several objectives in epidemiological research. First, it
provides a quantitative estimate of the direction, magnitude and uncertainty arising from …

Bias analysis quantifies the infiuence of systematic error on an epidemiology study's
estimate of occurrence or estimate of association. The fundamental methods of bias analysis …

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 The objective of this review was to assess the quality and strength of the
evidence provided by human observational studies for a causal association between …

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 …