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Activity Number: 505 - A Variety of Problems in Statistical Inference
Type: Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #313739
Title: Dyadic Hidden Markov Model
Author(s): Ruijin Lu* and Zhen Chen
Companies: National Institute of Health and NICHD/NIH
Keywords: hidden Markov model; hierarchical model; survey data; mixed effects model
Abstract:

Adherence to medication regimens is important in effective management of Type 1 diabetes (T1D). In families of youths with T1D, this task requires coordination and collaboration between parents and child and can be challenging given preadolescent hormonal fluctuations in youths. The NICHD Family Management of Diabetes (FMOD) clinical trial investigated a clinic-integrated behavioral intervention in glycemic control and found that the intervention is effective in glycemic control. To investigate the mechanism, we propose a novel dyadic hidden Markov model (dHMM) to estimate family relationship trajectories and their relationship to the intervention. In addition to the usual transition probabilities, the dHMM facilitates estimation of perception matrices to capture how family relationship is differentially perceived by parent and child. It shows that the clinic-integrated behavioral intervention helps improving family relationship. It also helps to project an optimistic view of the family relation in the eyes of child.


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