Many applications of biomedical science involve unobservable constructs, from measurement of health states to severity of complex diseases. The primary aim of measurement is to identify relevant pieces of observable information that thoroughly describe the construct of interest. Validation of the construct is often performed separately. Noting the increasing popularity of latent variable methods in biomedical research, we propose a Multiple Indicator Multiple Cause (MIMIC) latent variable model that combines item reduction and validation. Our joint latent variable model accounts for the bias that occurs in the traditional 2-stage process. The methods are motivated by an example from the Physical Activity and Lymphedema clinical trial in which the objectives were to describe lymphedema severity through self-reported Likert scale symptoms and to determine the relationship between symptom severity and a "gold standard" diagnostic measure of lymphedema. The MIMIC model identified 1 symptom as a potential candidate for removal. We present this paper as an illustration of the advantages of joint latent variable models and as an example of the applicability of these models for biomedical research.
