title: Multiple indicators, multiple causes measurement error models.
abstract: Multiple indicators, multiple causes (MIMIC) models are often employed by researchers studying the effects of an unobservable latent variable on a set of outcomes, when causes of the latent variable are observed. There are times, however, when the causes of the latent variable are not observed because measurements of the causal variable are contaminated by measurement error. The objectives of this paper are as follows: (i) to develop a novel model by extending the classical linear MIMIC model to allow both Berkson and classical measurement errors, defining the MIMIC measurement error (MIMIC ME) model; (ii) to develop likelihood-based estimation methods for the MIMIC ME model; and (iii) to apply the newly defined MIMIC ME model to atomic bomb survivor data to study the impact of dyslipidemia and radiation dose on the physical manifestations of dyslipidemia. As a by-product of our work, we also obtain a data-driven estimate of the variance of the classical measurement error associated with an estimate of the amount of radiation dose received by atomic bomb survivors at the time of their exposure.
authors: Department of Epidemiology and Biostatistics, Texas A&M University, 1266 TAMU, College Station, TX, 77843-1266, U.S.A.
authors: Carmen D
authors: CD
authors: Tekwe
authors: Randy L
authors: RL
authors: Carter
authors: Harry M
authors: HM
authors: Cullings
authors: Raymond J
authors: RJ
authors: Carroll
mesh_terms: D050171:Dyslipidemias / Q000097:blood*; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D054043:Nuclear Weapons; D011829:Radiation Dosage; D017741:Survivors
keywords: Berkson error; MIMIC models; atomic bomb survivor data; dyslipidemia; instrumental variables; latent variables; measurement error