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2016 On last observation carried forward and asynchronous longitudinal regression analysis
Hongyuan Cao, Jialiang Li, Jason P. Fine
Electron. J. Statist. 10(1): 1155-1180 (2016). DOI: 10.1214/16-EJS1141

Abstract

In many longitudinal studies, the covariates and response are often intermittently observed at irregular, mismatched and subject-specific times. Last observation carried forward (LOCF) is one of the most commonly used methods to deal with such data when covariates and response are observed asynchronously. However, this can lead to considerable bias. In this paper, we propose a weighted LOCF estimation using asynchronous longitudinal data for the generalized linear model. We further generalize this approach to utilize previously observed covariates in addition to the most recent observation. In comparison to earlier methods, the current methods are valid under weaker assumptions on the covariate process and allow informative observation times which may depend on response even conditional on covariates. Extensive simulation studies provide numerical support for the theoretical findings. Data from an HIV study is used to illustrate our methodology.

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Hongyuan Cao. Jialiang Li. Jason P. Fine. "On last observation carried forward and asynchronous longitudinal regression analysis." Electron. J. Statist. 10 (1) 1155 - 1180, 2016. https://doi.org/10.1214/16-EJS1141

Information

Received: 1 November 2015; Published: 2016
First available in Project Euclid: 3 May 2016

zbMATH: 1349.62544
MathSciNet: MR3499524
Digital Object Identifier: 10.1214/16-EJS1141

Subjects:
Primary: 60G20
Secondary: 60G05

Keywords: Asynchronous longitudinal data , Kernel weighted estimation , last observation carried forward , Nonparametric regression

Rights: Copyright © 2016 The Institute of Mathematical Statistics and the Bernoulli Society

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Vol.10 • No. 1 • 2016
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