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Activity Number: 554 - Novel Methods in Longitudinal Data Analysis
Type: Contributed
Date/Time: Thursday, August 11, 2022 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #323262
Title: Marginal Structural Models for Multinomial-Choice Outcomes with Irregularly Spaced Observations
Author(s): Taekwon Hong* and Shu Yang and Wenbin Lu and Pulak Ghosh
Companies: North Carolina State University and North Carolina State University and North Carolina State University and Indian Institute of Management Bangalore
Keywords: longitudinal data; multinomial choice; informative observation; doubly robust estimator; semiparametric model
Abstract:

Marginal Structural Models (MSMs) quantify the marginal effect of time-varying treatments, which, however, are underdeveloped for correlated multinomial choice outcomes, e.g., consumer expenditures. We propose simple-to-interpret MSMs for multinomial choices taking into account correlations of expenditure categories, with an application to study the effect of lockdown on consumer shopping behavior in the COVID-19 pandemic. Also, most existing MSMs use a discrete-time setup, which requires all subjects to be followed at the same pre-fixed time points but can be restrictive in many practical situations with irregularly spaced observations. We propose semiparametric doubly robust estimators to address time-varying confounding and irregularly-spaced observation times, capitalizing on semiparametric efficiency theory and advanced machine learning methods. Simulation studies are employed to confirm the validity of the proposed estimator. We apply the proposed estimator to investigate the lockdown effect on an individual’s economic behavior through the market transaction data of distinct regions in India with different periods of lockdown implementation.


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