Statistics and Its Interface

Volume 9 (2016)

Number 3

Semiparametric random effects models for longitudinal data with informative observation times

Pages: 333 – 341

DOI: https://dx.doi.org/10.4310/SII.2016.v9.n3.a7

Authors

Yang Li (Department of Mathematics and Statistics, University of North Carolina, Charlotte, N.C., U.S.A.)

Yanqing Sun (Department of Mathematics and Statistics, University of North Carolina, Charlotte, N.C., U.S.A.)

Abstract

Longitudinal data frequently arise in many fields such as medical follow-up studies focusing on specific longitudinal responses. In such situations, the responses are recorded only at discrete observation times. Most existing approaches for longitudinal data analysis assume that the observation or follow-up times are independent of the underlying response process, either completely or given some known covariates. We present a joint analysis approach in which possible correlations among the responses, observation and follow-up times can be characterized by time-dependent random effects. Estimating equations are developed for parameter estimation and the resulting estimates are shown to be consistent and asymptotically normal. A simulation study is conducted to assess the finite sample performance of the approach and the method is applied to data arising from a skin cancer study.

Keywords

estimating equations, informative censoring, informative observation process, joint analysis approach, longitudinal data

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Published 27 January 2016