Using Stata's sem to adjust for covariate measurement error

Covariate measurement error is a common issue in epidemiology. Many statistical methods have been developed for allowing for covariate measurement error over the last three decades or so. I've been playing around with Stata's structural equation modelling builder, which enables one to allow for covariate measurement error using maximum likelihood for estimation. I'm still very much a beginner with structural equation models and Stata's implementation of them, but hopefully the following YouTube video is a useful illustration of just one of the things that's possible with them:

Adjusting for covariate misclassification in logistic regression - predictive value weighting

When we fit regression models, we implicitly assume that the values in our dataset are accurate measurements of the variables of interest. In many settings, the measurements we actually have are imperfect. In the case of a categorical variable, for some of the records in our dataset the observed value may differ from the true value, due to misclassification. Misclassification arises for many different reasons. In epidemiology, instruments are often used to measure conditions imperfectly - sometimes observations which should be recorded as 1 are recorded as 0, and vice-versa. In this post I'll focus on the common situation where logistic regression is used to model an outcome Y, and one of the covariates is subject to misclassification.

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