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Activity Number: 173 - Recent Advances in Statistical Learning and Missing Data Handling
Type: Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
Sponsor: Korean International Statistical Society
Abstract #309854
Title: Imputation Approach Based on Latent Class Trajectory for Handling Missing Values in Self-Reported Data
Author(s): MinJae Lee*
Companies: University of Texas Southwestern Medical Center
Keywords: Imputation; Bayesian Quantile Regression; latent class ; medication usage; longitudinal anlaysis

Evaluating the association between diseases and the longitudinal pattern of pharmacological therapy has become increasingly important. However, in many longitudinal studies, self-reported medication usage data collected at patients' follow up visits could be missing for various reasons. These pieces of missing or inaccurate/untenable information complicate determining the trajectory of medication use and its complete effects for patients. Although longitudinal models can deal with specific types of missing data, inappropriate handling of this issue can lead to a biased estimation of regression parameters especially when missing data mechanisms are complex and depend upon multiple sources of variation over time. We propose a latent class based imputation approach through Bayesian quantile regression that incorporates cluster of unobserved heterogeneity for handling medication usage data with missing values. Findings from our simulation study indicate that the proposed method performs better than traditional imputation methods under certain scenarios of data distribution. We also demonstrate applications of the proposed method to real data obtained from the longitudinal study.

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

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