Suman Kundu, Yurii S. Aulchenko, A. Cecile J.W. Janssens
Suman Kundu <s.kundu@erasmusmc.nl>, A. Cecile J.W. Janssens
<a.janssens@erasmusmc.nl>
R (>= 2.9.0), Hmisc, ROCR, epitools, PBSmodelling
PredictABEL includes functions to assess the performance
of risk models. The package contains functions for the various
measures that are used in empirical studies, including
univariate and multivariate odds ratios (OR) of the predictors,
the c-statistic (or area under the receiver operating
characteristic (ROC) curve (AUC)), Hosmer-Lemeshow goodness of
fit test, reclassification table, net reclassification
improvement (NRI) and integrated discrimination improvement
(IDI). Also included are functions to create plots, such as
risk distributions, ROC curves, calibration plot,
discrimination box plot and predictiveness curves. In addition
to functions to assess the performance of risk models, the
package includes functions to obtain weighted and unweighted
risk scores as well as predicted risks using logistic
regression analysis. These logistic regression functions are
specifically written for models that include genetic variables,
but they can also be applied to models that are based on
non-genetic risk factors only.

R (>= 2.12.0), Hmisc, ROCR, epitools, PBSmodelling
PredictABEL includes functions to assess the performance
of risk models. The package contains functions for the various
measures that are used in empirical studies, including
univariate and multivariate odds ratios (OR) of the predictors,
the c-statistic (or area under the receiver operating
characteristic (ROC) curve (AUC)), Hosmer-Lemeshow goodness of
fit test, reclassification table, net reclassification
improvement (NRI) and integrated discrimination improvement
(IDI). Also included are functions to create plots, such as
risk distributions, ROC curves, calibration plot,
discrimination box plot and predictiveness curves. In addition
to functions to assess the performance of risk models, the
package includes functions to obtain weighted and unweighted
risk scores as well as predicted risks using logistic
regression analysis. These logistic regression functions are
specifically written for models that include genetic variables,
but they can also be applied to models that are based on
non-genetic risk factors only.  Finally, the package includes
function to construct a simulated dataset with genotypes,
genetic risks, and disease status for a hypothetical
population, which is used for the evaluation of genetic risk
models.