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Activity Number: 375
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #319401
Title: Generalized Latent Trait Models for Multiple Correlated Health Endpoints
Author(s): Chris Liu*
Companies: University of Michigan
Keywords: Latent variable models ; latent traits ; Modified Gauss-Newton algorithm ; Non-linear least square method ; Infant mobidity index

Latent trait models have an abroad application in education, health science, psychology and other areas. There are two common assumptions in latent trait models: local independence of manifest outcomes and normal distribution of latent traits. In practice, these assumptions may not be satisfied, especially for the normality of latent traits. In this study, a class of generalized latent trait models and modified Gauss-Newton algorithms for multiple outcomes are proposed. Instead of assuming latent traits to be normal, we specify a skew normal distribution for latent traits of which a normal distribution is a special case, and then model the conditional probability of each outcome as a nonlinear quadratic function of latent traits, which has properties similar to the logistic function. The estimated generalized nonlinear least-square method is used to solve equations for parameters of interest. The models are applied to an infant morbidity study to develop a new single variable, called infant morbidity index (IMI) that functions as a summary of four infant morbidity outcomes and represents propensity for infant morbidity, is developed. The validity of this index as a measure of propensity for infant morbidity needs to be further investigated in future research.

Authors who are presenting talks have a * after their name.

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