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Regression analysis of longitudinal data with informative observation times and application to medical cost data
Author(s) -
Zhu Liang,
Sun Jianguo,
Tong Xingwei,
Pounds Stanley
Publication year - 2011
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4198
Subject(s) - computer science , longitudinal data , regression analysis , statistics , regression , econometrics , data mining , mathematics , machine learning
Longitudinal data analysis is one of the most discussed and applied areas in statistics and a great deal of literature has been developed for it. However, most of the existing literature focus on the situation where observation times are fixed or can be treated as fixed constants. This paper considers the situation where these observation times may be random variables and more importantly, they may be related to the underlying longitudinal variable or process of interest. Furthermore, covariate effects may be time‐varying. For the analysis, a joint modeling approach is proposed and in particular, for estimation of time‐varying regression parameters, an estimating equation‐based procedure is developed. Both asymptotic and finite sample properties of the proposed estimates are established. The methodology is applied to an acute myeloid leukemia trial that motivated this study. Copyright © 2011 John Wiley & Sons, Ltd.