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Abstract Details

Activity Number: 463
Type: Topic Contributed
Date/Time: Wednesday, August 4, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract - #306887
Title: Longitudinal Data Analysis with Event Time as a Covariate
Author(s): Xuewen Lu*+ and Bin Nan and Peter Song and MaryFran Sowers
Companies: University of Calgary and University of Michigan and University of Michigan and University of Michigan
Address: Dept. of Math and Stats, Calgary, AB, T2N 1N4, Canada
Keywords: censoring ; hormone profile ; longitudinal data ; semiparametric mixed model ; spline smoothing

We consider the estimation of a nonparametric smooth function of some event time in a semiparametric mixed effects model from repeatedly measured data when the event time is subject to right censoring. The within-subject correlation is captured by both cross-sectional and time-dependent random effects. When the censoring probability depends on other variables in the model, the event time data are not missing completely at random. Hence, the complete case analysis by eliminating all the censored observations may yield biased estimates of the regression parameters including the smooth function of the event time, and is less efficient. To remedy, we derive the likelihood function for the observed data by modeling the event time distribution given other covariates. This research is motivated by the project of hormone profile estimation in the Michigan Bone Health and Metabolism Study.

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