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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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