Work

Regression Analysis of Sparse Asynchronous Longitudinal Data

Year: 2014

Type: article

Abstract: 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.

Source: Journal of the Royal Statistical Society Series B (Statistical Methodology)

DOI: 10.1111/rssb.12086

Authors: Hongyuan Cao, Donglin Zeng, Jason P. Fine

Institutions: University of Chicago, University of North Carolina at Chapel Hill

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Institutes of Health, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Neurological Disorders and Stroke

Awards: Identifying Huntington's disease markers by modern statistical learning methods., Statistical Methods for Cancer Clinical Trials, Semiparametric Analysis of Censored Data in Current Medical Studies