JSM 2004 - Toronto

Abstract #300798

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Activity Number: 295
Type: Topic Contributed
Date/Time: Wednesday, August 11, 2004 : 8:30 AM to 10:20 AM
Sponsor: Section on Health Policy Statistics
Abstract - #300798
Title: Handling Missing Longitudinal Covariates in Child-development Study: A Functional Multiple Imputation Approach
Author(s): Yulei He*+ and Trivellore E. Raghunathan
Companies: University of Michigan and University of Michigan
Address: Dept. of Biostatistics, Ann Arbor, MI, 48109,
Keywords: children's health development ; functional mixed model ; longitudinal data ; missing covariate ; multiple imputation ; PSID
Abstract:

In longitudinal studies, information from the same set of subjects are collected repeatedly over time and such information can be used as covariates for subsequent analysis. However, repeatedly measured covariates are often subject to missing data, which imposes a serious difficulty to further analysis. For missing time-dependent covariate problem in longitudinal studies, the commonly used complete-case, available-case, and last value carried forward methods may give misleading results. We propose a multiple imputation approach based on functional mixed model, which characterize the covariate trends by nonparametric functions. Gibbs sampling algorithm is used to impute the missing values from their posterior predictive distributions. We compare various methods by Monte Carlo simulations and find that the proposed multiple imputation methods performs the best. We apply our method to data from the panel study of income dynamics (PSID). Our post-imputation analysis results suggest that family's economic status at the critical child developmental period may have a significant impact on the children's health development.

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