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Activity Number: 89 - Nonparametric Methods for Modern Data
Type: Contributed
Date/Time: Monday, August 9, 2021 : 10:00 AM to 11:50 AM
Sponsor: Section on Nonparametric Statistics
Abstract #318110
Title: Evaluating the Dynamic Association Between Bivariate Binary Variables in Longitudinal Studies
Author(s): Zhuangzhuang Liu* and Hyunkeun (Ryan) Cho
Companies: University of Iowa and University of Iowa
Keywords: Kernel density; Longitudinal data; Nonparametric estimation; Unstructured models; Time-varying odds ratio
Abstract:

As longitudinal data with two binary outcome variables where the outcomes are measured repeatedly over time, it enables us to examine the association at a given time and explore changes in the association over time. However, assessing the association of the two variables at the given time can be challenging in longitudinal studies in which the two variables are collected at different time points. In our paper, we develop an unstructured nonparametric model that describes the dynamic association between the two variables and allows us to vary the associations along with times. We propose an estimation procedures that examine the association from the longitudinal samples in which the two variables are repeatedly measured at the same time or different times. A univariate and bivariate kernel density function are used to yield a consistent estimate of the association. The performance of the proposed estimation procedure is studied in two scenarios where the two variables are measured concurrently and differently and confirm that the proposed estimation procedure performs similarly in both scenarios. We also illustrate the proposed approach by analyzing the Framingham Heart Study data.


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

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