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Activity Number: 534 - Contributed Poster Presentations: Section on Statistics in Epidemiology
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #330153
Title: Estimating the Causal Effect of Antidepressant Use on Time-To-Dementia for Incident MCI Patients Using Marginal Structural Fine-Gray Model
Author(s): Ran Duan* and Erin L Abner and Daniela Moga
Companies: University of Kentucky and University of Kentucky and University of Kentucky
Keywords: Antidepressants; Dementia; Marginal Structural Model; Competing Risk
Abstract:

Objective: The goal is to estimate the causal effect of antidepressant among participants with incident MCI on time-to-dementia with death as a competing event. We used the Marginal Structural Fine-Gray Model, given the possibility of indication bias. Methods: A retrospective cohort study was conducted using the National Alzheimer's Coordinating Center Uniform Data Set. Records from September 2005 to December 2017 were included. Eligible participants (N=960) were incident MCI cases aged at least 65 years with at least one visit following the MCI diagnosis. A Marginal Structural Fine-Gray Model with Inverse Probability Weighting was used to assess the causal effect of antidepressant usage on time-to-dementia with death as a competing event, adjusting for the effects of identified confounders. Indication bias was addressed by this method as well. Results: Among all participants, mean age was 80.5, 60.5% were females, and 19.9% were taking antidepressants at their MCI diagnosis visit. The adjusted subdistribution hazard ratio of dementia for participants who reported using antidepressants at MCI diagnosis visit is 1.75 (1.33, 2.31) times that for participants who did not.


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

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