Statistical Modelling 5 (2005), 159–172
A latent variable scorecard for neonatal baby frailty
Jack Bowden
Department of Health Sciences.
University of Leicester
UK
Joe Whittaker
Department of Mathematics and Statistics,
Lancaster University,
Lancaster LA1 4YF
UK
eMail:
joe.whittaker@lancaster.ac.uk
Abstract:
A latent variable frailty model is built for data coming from a neonatal study
conducted to investigate whether the presence of a particular hospital service
given to families with premature babies has a positive effect on their care
requirements within the first year of life. The predicted value of the latent
frailty term from information obtained from the family in advance of the birth
furnishes an overall measure of the quality of health of the baby. This
identifies families at risk. Maximum likelihood and Bayesian approaches are
used to estimate the effect of the variables on the value of the latent baby
frailty and for prediction of health complications. It is found that these
give much the same estimates of regression coefficients, but that the variance
components are the more difficult to estimate. We indicate how the findings
from the model may be presented as a scorecard for predicting frailty, and
so be useful to doctors working in hospital neonatal units. New information
about a baby is automatically combined with the current score to provide an
up-to-date score, so that rapid decisions for taking appropriate action are
made more possible. A diagnostic procedure is proposed to assess how well the
independence assumptions of the model are met in fitting to this data. It is
concluded that the frailty model provides an informative summary of the data
from this neonatal study.
Keywords:
COMMUNITY NEONATAL SERVICES; CONDITIONAL INDEPENDENCE;
EMPIRICAL BAYES PREDICTION; FRAILTY SCORECARD; GLLAMM;
HAND AND CROWDER QUALITY MODEL; JAGS; MIMIC MODEL; NEONATAL UNIT;
PREMATURE BIRTH
Downloads:
Example
data in zipped archive
back