Dropout is a common occurrence in longitudinal studies. Building upon the pattern-mixture modeling approach within the Bayesian paradigm, we propose a general framework of varying-coefficient models for longitudinal data with informative dropout, where measurement times can be irregular and dropout can occur at any point in continuous time (not just at observation times) together with administr...

UNLABELLED BACKGROUND Pulmonary rehabilitation (PR) and self-management (SM) support programmes are effective in the management of patients with chronic obstructive pulmonary disease (COPD), but these interventions are not widely implemented in routine care. One reason may be poor patient participation and retention. We conducted a systematic review to determine a true estimate of participat...

We propose a modified version of the three-step estimation method for the latent class model with covariates, which may be used to estimate a latent Markov (LM) model with individual covariates and possible dropout. We illustrate the proposed approach through an application finalized to the study of the health status of elderly people hosted in Italian nursing homes. This application is based o...

We introduce DropConnect, a generalization of Dropout (Hinton et al., 2012), for regularizing large fully-connected layers within neural networks. When training with Dropout, a randomly selected subset of activations are set to zero within each layer. DropConnect instead sets a randomly selected subset of weights within the network to zero. Each unit thus receives input from a random subset of ...

A very commonly faced issue while training prediction models using machine learning is overfitting. Dropout is a recently developed technique designed to counter this issue in deep neural networks and has also been extended to other algorithms like SVMs. In this project, we formulate and study the application of Dropout to Hidden Unit Conditional Random Fields (HUCRFs). HUCRFs use binary stocha...

Previous reviews of premature termination have yet to examine whether disparate psychotherapy treatments differ in their dropout rates for specific disorders. Using data from 587 studies, a series of meta-analyses were conducted comparing dropout rates between treatment approaches for 12 separate disorder categories. Although, significant differences between treatment approaches were found for ...

INTRODUCTION The dropout level from the Danish medical schools is high, but we have only little insight into this problem. The purpose of this study was to qualify the ongoing discussions concerning dropout. MATERIAL AND METHODS In this retrospective cohort study, relevant variables were extracted from the established database of Aarhus University for the 639 students initiating medicine stud...

Many psychology applications assess measurement invariance of a construct (e.g., depression) over time. These applications are often characterized by few time points (e.g., 3), but high rates of dropout. Although such applications routinely assume that the dropout mechanism is ignorable, this assumption may not always be reasonable. In the presence of nonignorable dropout, fitting a conventiona...

We investigate a local reparameterizaton technique for greatly reducing the variance of stochastic gradients for variational Bayesian inference (SGVB) of a posterior over model parameters, while retaining parallelizability. This local reparameterization translates uncertainty about global parameters into local noise that is independent across datapoints in the minibatch. Such parameterizations ...

Informative dropout is a vexing problem for any biomedical study. Most existing statistical methods attempt to correct estimation bias related to this phenomenon by specifying unverifiable assumptions about the dropout mechanism. We consider a cohort study in Africa that uses an outreach program to ascertain the vital status for dropout subjects. These data can be used to identify a number of r...