This paper describes the methodologies used to develop a prediction model to assist health workers in developing countries in facing one of the most difficult health problems in all parts of the world : the presentation of an acutely ill young infant.
Statistical approaches for developing the clinical prediction model faced at least two major difficulties.
First, the number of predictor variables, especially clinical signs and symptoms, is very large, necessitating the use of data reduction techniques that are blinded to the outcome.
Second, there is no uniquely accepted continuous outcome measure or final binary diagnostic criterion.
For example, the diagnosis of neonatal sepsis is ill-defined.
Clinical decision makers must identify infants likely to have positive cultures as well as to grade the severity of illness.
In the WHO/ARI Young Infant Multicentre Study we have found an ordinal outcome scale made up of a mixture of laboratory and diagnostic markers to have several clinical advantages as well as to increase the power of tests for risk factors.
Such a mixed ordinal scale does present statistical challenges because it may violate constant slope assumptions of ordinal regression models.
In this paper we develop and validate an ordinal predictive model after choosing a data reduction technique.
We show how ordinality of the outcome is checked against each predictor. (...)
Mots-clés Pascal : Enfant, Homme, Prédiction, Modèle statistique, Analyse ordinale, Méthodologie, Estimation paramètre, Etude multicentrique, Etiologie, Diagnostic, Facteur risque
Mots-clés Pascal anglais : Child, Human, Prediction, Statistical model, Ordinal analysis, Methodology, Parameter estimation, Multicenter study, Etiology, Diagnosis, Risk factor
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Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 98-0266080
Code Inist : 002B28F. Création : 11/09/1998.