Document Detail


Development and validation of an infant morbidity index using latent variable models.
MedLine Citation:
PMID:  17573677     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Birth defect, abnormal condition of the newborn, developmental delay or disability and low birth weight are four major infant morbidity outcomes. Most studies have focused on assessment of the effects of risk factors on each of these outcomes or of the relationship among these outcomes or both. Little attention has been paid to the development of a composite index, which is a summary construct of infant morbidity outcomes. In this paper, we develop extended latent variable (LV) models and modified Gauss-Newton algorithms for multiple multinomial morbidity outcomes with complete responses. By assuming the marginal distribution of the LV to be log-normal, we model the conditional probability of each outcome as a nonlinear function of the LV, 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 data set. 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 is then assessed in detail. It is shown that the IMI is correlated with each of the individual outcomes, with infant mortality and with a face-valid index of morbidity outcomes, and can be used in future research as a measure of propensity for infant morbidity.
Authors:
Xuefeng Liu; Jeffrey Roth
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Statistics in medicine     Volume:  27     ISSN:  0277-6715     ISO Abbreviation:  Stat Med     Publication Date:  2008 Mar 
Date Detail:
Created Date:  2008-02-13     Completed Date:  2008-06-03     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  England    
Other Details:
Languages:  eng     Pagination:  971-89     Citation Subset:  IM    
Copyright Information:
Copyright (c) 2007 John Wiley & Sons, Ltd.
Affiliation:
Translational Research and Clinical Epidemiology, Department of Internal Medicine at the Wayne State University, 540 E. Canfield, Detroit, MI 48201, U.S.A. xli@med.wayne.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Congenital Abnormalities / epidemiology
Developmental Disabilities / epidemiology
Florida / epidemiology
Humans
Infant, Low Birth Weight
Infant, Newborn
Models, Statistical*
Morbidity*
Reproducibility of Results
Risk Factors

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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