Journal of Health Education Research & Development

ISSN: 2380-5439

Open Access

Antonioli D


  • Research Article
    Abilities of Statistical Models to Identify Subjects with Ghost Prognosis Factors
    Author(s): Nguyen JM, Gaultier A and Antonioli DNguyen JM, Gaultier A and Antonioli D

    Background Many tools are available to estimate prediction quality, but none are available to assess the ability, of a predictive model to identify completely missing or unknown prognostic factors, designated as ghost factors (GFs). However, it may be possible to predict whether a subject carries a GF. Methods To simulate the presence of a GF, a significant prognostic factor and all variables correlated with it were removed prior to model analysis. Public datasets and simulated data were used. A predictive statistical model was developed to assess the relationship between the presence of a GF and the predictive capacity of a given model based on the correlation between predicted outcome and GF presence. Five statistical models were compared using this procedure. Results After evaluating 6 real .. Read More»
    DOI: 10.4172/2380-5439.1000141

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