English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In longitudinal data a response variable is measured over time, or underdifferent conditions, for a cohort of individuals. In many situations allintended measurements are not available which results in missing values. If themissing value is never followed by an observed measurement, this leads todropout pattern. The missing values could be in the response variable, thecovariates or in both. The missingness mechanism is termed non-random when theprobability of missingness depends on the missing value and may be on theobserved values. In this case the missing values should be considered in theanalysis to avoid any potential bias. The aim of this article is to employmultiple imputations (MI) to handle missing values in covariates using. Theselection model is used to model longitudinal data in the presence ofnon-random dropout. The stochastic EM algorithm (SEM) is developed to obtainthe model parameter estimates in addition to the estimates of the dropoutmodel. The SEM algorithm does not provide standard errors of the estimates. Wedeveloped a Monte Carlo method to obtain the standard errors. The proposedapproach performance is evaluated through a simulation study. Also, theproposed approach is applied to a real data set.

Analysis of Longitudinal Data with Missing Values in the Response and Covariates Using the Stochastic EM Algorithm