Title | Regression analysis of sparse asynchronous longitudinal data. |
Publication Type | Journal Article |
Year of Publication | 2015 |
Authors | Cao, Hongyuan, Donglin Zeng, and Jason P. Fine |
Journal | J R Stat Soc Series B Stat Methodol |
Volume | 77 |
Issue | 4 |
Pagination | 755-776 |
Date Published | 2015 Sep |
ISSN | 1369-7412 |
Abstract | 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 last value carried forward approach. The practical utility of the methods is illustrated on data from a study on human immunodeficiency virus. |
DOI | 10.1111/rssb.12086 |
Alternate Journal | J R Stat Soc Series B Stat Methodol |
Original Publication | Regression analysis of sparse asynchronous longitudinal data. |
PubMed ID | 26568699 |
PubMed Central ID | PMC4643299 |
Grant List | P01 CA142538 / CA / NCI NIH HHS / United States R01 GM047845 / GM / NIGMS NIH HHS / United States U01 NS082062 / NS / NINDS NIH HHS / United States |
Regression analysis of sparse asynchronous longitudinal data.
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