JSM 2014 Home
Online Program Home
My Program

Abstract Details

Activity Number: 537
Type: Invited
Date/Time: Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract #310800
Title: Estimating Longitudinal Trajectories Nonparametrically with Informative Yet Explainable Dropouts
Author(s): Lu Wang*+ and Xihong Lin
Companies: University of Michigan and Harvard School of Public Health
Keywords: nonparametric regression ; missing data ; kernel GEE ; seemingly unrelated kernel estimating equations ; estimation efficiency ; longitudinal study
Abstract:

We consider nonparametric regression for longitudinal data subject to dropout with explainable mechanism. The reason people drop out may depend on the history of both outcome and covariates, but is independent of future outcome and covariates. We propose inverse probability weighted (IPW) kernel generalized estimating equations (kernel GEEs) and IPW seemingly unrelated (SUR) kernel estimating equations using either complete cases or all available cases. We show that all these IPW kernel estimators are consistent when the probability of dropout is known by design or is estimated using a correctly specified parametric model. The most efficient IPW kernel GEE estimator is obtained by ignoring the within-subject correlation, while in contrast the most efficient IPW SUR kernel estimator is obtained by accounting for the within-subject correlation, and is more efficient than the most efficient IPW kernel GEE counterpart. When appropriate covariance matrices are used, the IPW kernel estimators obtained using all available cases are more efficient than those using complete cases only. We perform simulations to evaluate the finite sample performance of the proposed methods.


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

Back to the full JSM 2014 program




2014 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Professional Development program, please contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

ASA Meetings Department  •  732 North Washington Street, Alexandria, VA 22314  •  (703) 684-1221  •  meetings@amstat.org
Copyright © American Statistical Association.