On Latent-Variable Model Misspecification in Structural Measurement Error Models for Binary Response
Article first published online: 29 SEP 2008
© 2008, The International Biometric Society
Volume 65, Issue 3, pages 710–718, September 2009
How to Cite
Huang, X. and Tebbs, J. M. (2009), On Latent-Variable Model Misspecification in Structural Measurement Error Models for Binary Response. Biometrics, 65: 710–718. doi: 10.1111/j.1541-0420.2008.01128.x
- Issue published online: 14 SEP 2009
- Article first published online: 29 SEP 2008
- Received January 2008. Revised May 2008. Accepted June 2008.
- Group testing;
- Latent variable;
- Measurement error;
- Pooled response;
- Reliability ratio;
- Simulation extrapolation
Summary We consider structural measurement error models for a binary response. We show that likelihood-based estimators obtained from fitting structural measurement error models with pooled binary responses can be far more robust to covariate measurement error in the presence of latent-variable model misspecification than the corresponding estimators from individual responses. Furthermore, despite the loss in information, pooling can provide improved parameter estimators in terms of mean-squared error. Based on these and other findings, we create a new diagnostic method to detect latent-variable model misspecification in structural measurement error models with individual binary response. We use simulation and data from the Framingham Heart Study to illustrate our methods.