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Regression analysis of sparse asynchronous longitudinal data
Author(s) -
Cao Hongyuan,
Zeng Donglin,
Fine Jason P.
Publication year - 2015
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/rssb.12086
Subject(s) - covariate , asynchronous communication , estimator , computer science , missing data , estimating equations , invariant (physics) , smoothness , kernel (algebra) , mathematics , statistics , computer network , mathematical analysis , combinatorics , mathematical physics
Summary We consider estimation of regression models for sparse asynchronous longitudinal observations, where time‐dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel‐weighted estimating equations are proposed for generalized linear models with either time invariant or time‐dependent coefficients under smoothness assumptions for the covariate processes which are similar to those for synchronous data. For models with either time invariant or time‐dependent coefficients, the estimators are consistent and asymptotically normal but converge at slower rates than those achieved with synchronous data. Simulation studies evidence that the methods perform well with realistic sample sizes and may be superior to a naive application of methods for synchronous data based on an ad hoc last value carried forward approach. The practical utility of the methods is illustrated on data from a study on human immunodeficiency virus.