Random effects diagonal metric multidimensional scaling models

By assuming a distribution for the subject weights in a diagonal metric (INDSCAL) multidimensional scaling model, the subject weights become random effects. Including random effects in multidimensional scaling models offers several advantages over traditional diagonal metric models such as those fitted by the INDSCAL, ALSCAL, and other multidimensional scaling programs. Unlike traditional models, the number of parameters does not increase with the number of subjects, and, because the distribution of the subject weights is modeled, the construction of linear models of the subject weights and the testing of those models is immediate. Here we define a random effects diagonal metric multidimensional scaling model, give computational algorithms, describe our experiences with these algorithms, and provide an example illustrating the use of the model and algorithms.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45758/1/11336_2005_Article_BF02295730.pd

Using Graphical-Belief to Predict Risk for Coronary Artery Disease

Medical knowledge about risks consists of a combination of structural information about known biological facts and probabilistic or actuarial information about exposures to hazards and recovery rates. While both types of information present significant practical challenges, probabilistic information is especially difficult to use because (1) it requires constant maintenance as new studies provide new data and (2) it usually comes in the form of study results which are not ideally suited for making individual predictions. The program GRAPHICAL-BELIEF is an environment for building and manipulating complex risk models. Graphical models can store and manipulate both structural and probabilistic knowledge and the model graph—in which nodes represent variables and edges represent relationships—is a natural visual metaphor for the more complex mathematical model. GRAPHICAL-BELIEF provides tools for both model manipulations and maintaining the knowledge bases on which the model is built. This paper introduces GRAPHICAL-BELIEF through an extended example: a model built from a study of patients with coronary artery disease. It shows how the model can provide valuable information about risk to the patient and value of information for medical tests

Nonlinearity in Demographic and Behavioral Determinants of Morbidity

OBJECTIVE: To examine nonlinearity of determinants of morbidity in the United States DATA SOURCES: A secondary analysis of data on individuals with dietary data from the Cancer Epidemiology Supplement and National Health Interview Survey (NHIS) 1987, a cross-sectional, stratified random sample of the U.S. population (n=22,080). STUDY DESIGN: A statistical exploration using additive multiple regression models. METHODS: A Morbidity Index (0–30 points), derived from 1987 National Health Interview Survey data, combines number of conditions, hospitalizations, sick days, doctor visits, and degree of disability. Behavioral (health habits) variables were added to multivariate models containing demographic terms, with Morbidity Index and Self-assessed Health outcomes (n=17,612). Tables and graphs compare models of morbidity with self-assessed health models, with and without behavioral terms. Graphs illustrate curvilinear relationships. PRINCIPAL FINDINGS: Morbidity and health are associated nonlinearly with age, race, education, and income, as well as alcohol, diet change, vitamin supplement use, body mass index (BMI), marital status/living arrangement, and smoking. Diet change and supplement use, education, income, race/ethnicity, and age relate differently to self-assessed health status than to morbidity. Morbidity is strongly associated with income up to about $15,000 above poverty. Additional income predicts no further reduction in morbidity. Better health is strongly related to both higher income and education. After controlling for income, black race does not predict morbidity, but remains associated with lower self-assessed health. CONCLUSIONS: Good health habits, as captured in these models, are associated with a 10–20-year delay in onset and progression of morbidity