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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this manuscript we propose a novel approach for the analysis of longitudinal data that have informative dropout. We jointly model the slopes of covariates of interest and the censoring process for which we assume a survival model with logistic non-constant dropout hazard in a likelihood function that is integrated over the random effects. Maximization of the marginal likelihood function results in acquiring maximum likelihood estimates for the population slopes and empirical Bayes estimates for the individual slopes that are predicted using Gaussian quadrature. Our simulation study results indicated that the performance of this model is superior in terms of accuracy and validity of the estimates compared to other models such as logistic non-constant hazard censoring model that does not include covariates, logistic constant censoring model with covariates, bootstrapping approach as well as mixed models. Sensitivity analyses for the dropout hazard and non-Gaussian errors were also undertaken to assess robustness of the proposed approach to such violations. Our model was illustrated using a cohort of renal transplant patients with estimated glomerular filtration rate as the outcome of interest.

Joint modeling of covariates and censoring process assuming non-constant dropout hazard