by Sophia Rabe-Hesketh


cme is a wrapper for gllamm to estimate generalized linear models with covariate measurement error by maximum likelihood using adaptive quadrature. The covariate measurement error model comprises three submodels: the outcome model, the measurement model and the true covariate model.

The outcome model is a generalized linear model including both observed covariates and the true, unobserved or latent covariate.

The measurement model assumes that the continuous repeated measurements of the true covariate are independently normally distributed with mean equal to the true covariate and constant variance (classical measurement model).

The true covariate model is a linear regression of the true covariate on the observed covariates. Use tcovmod() to use a different set of observed covariates in the true covariate model than in the outcome model.

The commands option causes cme to print out all data manipulation commands and the gllamm command for estimating the model. gllamm itself can then be used to extend the model or to make predictions or simule data gllapred or gllasim.

Reference: Rabe-Hesketh, Skrondal, and Pickles (2003). Maximum likelihood estimation of generalized linear models with covariate measurement error. The Stata Journal 3 (4), 385-410.


The command requires Stata 8 or later (available from Stata Corporation) and the latest version of gllamm (see here for installation instructions). cme can be installed from Statistical Software Components (SSC):

   . ssc describe cme
   . ssc install cme
   . ssc install cme, replace /* to replace previous version */

or downloaded directly from here: cme.ado, cme.hlp