Online Program Home
My Program

Abstract Details

Activity Number: 526 - Contributed Poster Presentations: Section on Statistical Consulting
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Consulting
Abstract #307125
Title: Comparing Statistical Methods Modeling Disease Progression in Presence of Informative Censoring
Author(s): Tahmineh Romero* and Tristan Grogan and David Elashoff
Companies: and Department of Medicine Statistics Core (DOMStat) and UCLA
Keywords: Joint model; Inverse Probability Weighting (IPW); GLMM; Longitudinal data; NPI-Q; Dementia

Background: Modeling the progression of the disease using standard approaches to modeling longitudinal trajectories, such as generalized linear mixed model (GLMMs), assume missingness mechanism satisfies the assumption of missing at random (MAR). When this assumption is violated, alternative approaches should be considered for unbiased statistical inferences. Method: We studied a national cohort of surviving patients with dementia to model the change in Neuropsychiatric Inventory Questionnaire (NPI-Q) severity score over a 4-years period. In this dataset patients frequently dropped out due to progression of the disease. The methods we evaluated include jointly modeling patient dropouts and change in NPI-Q severity over time, Inverse Probability weighted (IPW) GLMMs, combining Multiple Imputation (MI) and IPW for a doubly robust estimator, and the naïve approach of the complete case analysis.

Conclusion: We found the joint modeling and inverse probability weighted methods will lead to a similar result, and in the applied field the IPW could be more intuitive and interpretable approach.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program