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June 2009 An estimating equations approach to fitting latent exposure models with longitudinal health outcomes
Brisa N. Sánchez, Esben Budtz-Jørgensen, Louise M. Ryan
Ann. Appl. Stat. 3(2): 830-856 (June 2009). DOI: 10.1214/08-AOAS226

Abstract

The analysis of data arising from environmental health studies which collect a large number of measures of exposure can benefit from using latent variable models to summarize exposure information. However, difficulties with estimation of model parameters may arise since existing fitting procedures for linear latent variable models require correctly specified residual variance structures for unbiased estimation of regression parameters quantifying the association between (latent) exposure and health outcomes. We propose an estimating equations approach for latent exposure models with longitudinal health outcomes which is robust to misspecification of the outcome variance. We show that compared to maximum likelihood, the loss of efficiency of the proposed method is relatively small when the model is correctly specified. The proposed equations formalize the ad-hoc regression on factor scores procedure, and generalize regression calibration. We propose two weighting schemes for the equations, and compare their efficiency. We apply this method to a study of the effects of in-utero lead exposure on child development.

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Brisa N. Sánchez. Esben Budtz-Jørgensen. Louise M. Ryan. "An estimating equations approach to fitting latent exposure models with longitudinal health outcomes." Ann. Appl. Stat. 3 (2) 830 - 856, June 2009. https://doi.org/10.1214/08-AOAS226

Information

Published: June 2009
First available in Project Euclid: 22 June 2009

zbMATH: 1166.62084
MathSciNet: MR2750684
Digital Object Identifier: 10.1214/08-AOAS226

Keywords: Dimension reduction , Factor score regression , lead exposure , measurement error

Rights: Copyright © 2009 Institute of Mathematical Statistics

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Vol.3 • No. 2 • June 2009
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