73370
document
Covariance analysis in generalized linear measurement error models . We summarize some of the recent work on the errors-in-variables problem in generalized linear models . The focus is on covariance analysis , and in particular testing for and estimation of treatment effects . There is a considerable difference between the randomized and non-randomized models when testing for an effect . In randomized studies , simple techniques exist for testing for a treatment effect . In some instances , such as linear and multiplicative regression , simple methods exist for estimating the treatment effect . In other examples such as logistic regression , estimating a treatment effect requires careful attention to measurement error . In non-randomized studies , there is no recourse to understanding and modelling measurement error . In particular ignoring measurement error can lead to the wrong conclusions , for example the true but unobserved data may indicate a positive effect for treatment , while the observed data indicate the opposite . Some of the possible methods are outlined and compared .
73371
Intervention_Physical
Covariance analysis