We developed a decision-tree prediction model by using data from a 4-year period (2005 through 2008, n = 307,896), with validation by using external data from 2009 (n = 82,330).
The predictive ability of the model was estimated by cross-validation and by external predictions for new HIV variants; both procedures showed very high correlation between the predicted and actual susceptibility values (Q2 = 0.89 and Q2ext = 0.86).
Unfortunately, none of the aforementioned models for predicting MOE, with the exception of that by Liu et al. (2007), were supported by external validation datasets (though an internal validation technique, cross-validation, was used by Merlo et al. (2014).
The German validation emphasized external validity by comparing APAIS with several scale: the Hospital Anxiety and Depression Scale (HADS); the Self-rated symptom CheckList (SCL-9-K); The COping with Surgical Stress scale (COSS); the KASA scale and the State Operation Anxiety Scale (STOA).
As far as we know, this is the first study to use robust modeling techniques for model generation, followed by external validation to generate and validate symptom-based predictive models of endometriosis in a large prospectively recruited cohort of women across different countries and ethnicities.
The calibration of PCR and PLS-1 models was evaluated by internal validation (prediction of compounds in its own designed training set of calibration) and by external validation over laboratory prepared mixtures and pharmaceutical preparations.
These calibration models were evaluated by internal validation (prediction of compounds in its own designed training set of calibration), by cross-validation (obtaining statistical parameters that show the efficiency for a calibration fit model) and by external validation over synthetic and pharmaceutical mixtures.
The calibration PCR and PLS-1 models were evaluated by internal validation (prediction of compounds in its own designed training set of calibration), by cross-validation (obtaining statistical parameters that show the efficiency for a calibration fit model) and by external validation over laboratory-prepared mixtures and pharmaceutical preparations.
The calibration PLS-1 and PCR models were evaluated by internal validation (prediction of compounds in its own designed training set of calibration), by cross-validation (obtaining statistical parameters that show the efficiency for a calibration fit model) and by external validation over synthetic and pharmaceutical preparation.
The predictive ability of these signatures was evaluated by cross-validating the data as well as by validation in external and independent protein and mRNA data sets associated with clinical data.
