An estimating equations approach to fitting latent exposure models with longitudinal health outcomes

Details An estimating equations approach to fitting latent exposure models with longitudinal health outcomes An estimating equations approach to fitting latent exposure models with longitudinal health outcomes

2009-08-20

arxiv.org/abs/0908.2886v1

Annals of Applied Statistics 2009, Vol. 3, No. 2, 830-856

Statistics

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Published in at http://dx.doi.org/10.1214/08-AOAS226 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

10.1214/08-AOAS226

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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