PredictABEL-package: An R package for the analysis of (genetic) risk prediction...
In PredictABEL: Assessment of Risk Prediction Models
Description
Details
Acknowledgements
Note
Author(s)
References
Description
An R package for the analysis of (genetic) risk prediction studies.
Details
Fueled by the substantial gene discoveries from genome-wide association
studies, there is increasing interest in investigating the predictive
ability of genetic risk models. To assess the performance of genetic risk
models, PredictABEL includes functions for the various measures and plots
that have been used in empirical studies, including univariate and
multivariate odds ratios (ORs) of the predictors, the c-statistic (or AUC),
Hosmer-Lemeshow goodness of fit test, reclassification table, net
reclassification improvement (NRI) and integrated discrimination
improvement (IDI). The plots included are the ROC plot, calibration plot,
discrimination box plot, predictiveness curve, and several risk distributions.
These functions can be applied to predicted risks that are obtained using
logistic regression analysis, to weighted or unweighted risk scores, for
which the functions are included in this package. The functions can also be
used to assess risks or risk scores that are constructed using other methods, e.g., Cox Proportional
Hazards regression analysis, which are not included in the current version.
Risks obtained from other methods can be imported into R for assessment
of the predictive performance.
The functions to construct the risk models using logistic regression analyses
are specifically written for models that include genetic variables,
eventually in addition to non-genetic factors, but they can also be applied
to construct models that are based on non-genetic risk factors only.
Before using the functions fitLogRegModel for constructing
a risk model or riskScore for computing risk
scores, the following checks on the dataset are advisable to be done:
(1) Missing values: The logistic regression analyses and computation of
the risk score are done only for subjects that have no missing data. In case
of missing values, individuals with missing data can be removed from the
dataset or imputation strategies can be used to fill in missing data.
Subjects with missing data can be removed with the R function na.omit
(available in stats package).
Example: DataFileNew <- na.omit(DataFile)
will make a new dataset (DataFileNew) with no missing values;
(2) Multicollinearity: When there is strong correlation between the
predictor variables, regression coefficients may be estimated imprecisely
and risks scores may be biased because the assumption of independent effects
is violated. In genetic risk prediction studies, problems with
multicollinearity should be expected when single nucleotide polymorphisms
(SNPs) located in the same gene are
in strong linkage disequilibrium (LD). For SNPs in LD it is common to select
the variant with the lowest p-value in the model;
(3) Outliers: When the data contain significant outliers, either clinical
variables with extreme values of the outcomes or extreme values resulting
from errors in the data entry, these may impact the construction of the risk models and
computation of the risks scores. Data should be carefully checked and outliers
need to be removed or replaced, if justified;
(4) Recoding of data: In the computation of unweighted risk scores, it is assumed
that the genetic variants are coded 0,1,2
representing the number of alleles carried. When variants
are coded 0,1 representing a dominant or recessive effect of the alleles,
the variables need to be recoded before unweighted risk scores can be computed.
To import data into R several alternative strategies can be used. Use the
Hmisc package for importing SPSS and SAS data into R.
Use "ExampleData <- read.table("DataName.txt", header=T, sep="\t")" for text
files where variable names are included as column headers and data are
separated by tabs.
Use "ExampleData <- read.table("Name.csv", sep=",", header=T)"
for comma-separated files with variable names as column headers.
Use "setwd(dir)" to set the working directory to "dir". The datafile
needs to be present in the working directory.
To export datafiles from R tables to a tab-delimited textfile with the first row as
the name of the variables,
use "write.table(R_Table, file="Name.txt", row.names=FALSE, sep="\t")" and
when a comma-separated textfile is requested and variable names are provided in the first row,
use "write.table(R_Table, file="Name.csv", row.names=FALSE, sep=",")".
When the directory is not specified, the file will be
saved in the working directory. For exporting R data into SPSS, SAS and
Stata data, use functions in the the foreign package.
Several functions in this package depend on other R packages:
(1) Hmisc, is used to compute NRI and IDI;
(2) ROCR, is used to produce ROC plots;
(3) epitools, is used to compute univariate odds ratios;
(4) PBSmodelling, is used to produce predictiveness curve.
Acknowledgements
The authors would like to acknowledge Lennart Karssen,
Maksim Struchalin and Linda Broer from the Department of Epidemiology,
Erasmus Medical Center, Rotterdam for their valuable comments and
suggestions to make this package.
Note
The current version of the package includes the basic measures
and plots that are used in the assessment of (genetic) risk prediction models and the
function to construct a simulated dataset that contains individual genotype
data, estimated genetic risk and disease status, used for the evaluation of
genetic risk models (see Janssens et al, Genet Med 2006).
Planned extensions of the package include functions to construct risk
models using Cox Proportional Hazards analysis for prospective data and
assess the performance of risk models for time-to-event data.
Author(s)
Suman Kundu
Yurii S. Aulchenko
A. Cecile J.W. Janssens
References
S Kundu, YS Aulchenko, CM van Duijn, ACJW Janssens. PredictABEL:
an R package for the assessment of risk prediction models.
Eur J Epidemiol. 2011;26:261-4.
ACJW Janssens, JPA Ioannidis, CM van Duijn, J Little, MJ Khoury.
Strengthening the Reporting of Genetic Risk Prediction Studies: The GRIPS
Statement Proposal. Eur J Epidemiol. 2011;26:255-9.
ACJW Janssens, JPA Ioannidis, S Bedrosian, P Boffetta, SM Dolan, N Dowling,
I Fortier, AN. Freedman, JM Grimshaw, J Gulcher, M Gwinn, MA Hlatky, H Janes,
P Kraft, S Melillo, CJ O'Donnell, MJ Pencina, D Ransohoff, SD Schully,
D Seminara, DM Winn, CF Wright, CM van Duijn, J Little, MJ Khoury.
Strengthening the reporting of genetic risk prediction studies
(GRIPS)-Elaboration and explanation. Eur J Epidemiol. 2011;26:313-37.
Aulchenko YS, Ripke S, Isaacs A, van Duijn CM. GenABEL: an R package for genome-wide
association analysis. Bioinformatics 2007;23(10):1294-6.
PredictABEL documentation built on March 13, 2020, 3:15 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Details Acknowledgements Note Author(s) References
Description
An R package for the analysis of (genetic) risk prediction studies.
Details
Fueled by the substantial gene discoveries from genome-wide association studies, there is increasing interest in investigating the predictive ability of genetic risk models. To assess the performance of genetic risk models, PredictABEL includes functions for the various measures and plots that have been used in empirical studies, including univariate and multivariate odds ratios (ORs) of the predictors, the c-statistic (or AUC), Hosmer-Lemeshow goodness of fit test, reclassification table, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). The plots included are the ROC plot, calibration plot, discrimination box plot, predictiveness curve, and several risk distributions.
These functions can be applied to predicted risks that are obtained using logistic regression analysis, to weighted or unweighted risk scores, for which the functions are included in this package. The functions can also be used to assess risks or risk scores that are constructed using other methods, e.g., Cox Proportional Hazards regression analysis, which are not included in the current version. Risks obtained from other methods can be imported into R for assessment of the predictive performance.
The functions to construct the risk models using logistic regression analyses
are specifically written for models that include genetic variables,
eventually in addition to non-genetic factors, but they can also be applied
to construct models that are based on non-genetic risk factors only.
Before using the functions fitLogRegModel for constructing
a risk model or riskScore for computing risk
scores, the following checks on the dataset are advisable to be done:
(1) Missing values: The logistic regression analyses and computation of
the risk score are done only for subjects that have no missing data. In case
of missing values, individuals with missing data can be removed from the
dataset or imputation strategies can be used to fill in missing data.
Subjects with missing data can be removed with the R function na.omit
(available in stats package).
Example: DataFileNew <- na.omit(DataFile)
will make a new dataset (DataFileNew) with no missing values;
(2) Multicollinearity: When there is strong correlation between the predictor variables, regression coefficients may be estimated imprecisely and risks scores may be biased because the assumption of independent effects is violated. In genetic risk prediction studies, problems with multicollinearity should be expected when single nucleotide polymorphisms (SNPs) located in the same gene are in strong linkage disequilibrium (LD). For SNPs in LD it is common to select the variant with the lowest p-value in the model;
(3) Outliers: When the data contain significant outliers, either clinical variables with extreme values of the outcomes or extreme values resulting from errors in the data entry, these may impact the construction of the risk models and computation of the risks scores. Data should be carefully checked and outliers need to be removed or replaced, if justified;
(4) Recoding of data: In the computation of unweighted risk scores, it is assumed
that the genetic variants are coded 0,1,2
representing the number of alleles carried. When variants
are coded 0,1 representing a dominant or recessive effect of the alleles,
the variables need to be recoded before unweighted risk scores can be computed.
To import data into R several alternative strategies can be used. Use the
Hmisc package for importing SPSS and SAS data into R.
Use "ExampleData <- read.table("DataName.txt", header=T, sep="\t")" for text
files where variable names are included as column headers and data are
separated by tabs.
Use "ExampleData <- read.table("Name.csv", sep=",", header=T)"
for comma-separated files with variable names as column headers.
Use "setwd(dir)" to set the working directory to "dir". The datafile
needs to be present in the working directory.
To export datafiles from R tables to a tab-delimited textfile with the first row as
the name of the variables,
use "write.table(R_Table, file="Name.txt", row.names=FALSE, sep="\t")" and
when a comma-separated textfile is requested and variable names are provided in the first row,
use "write.table(R_Table, file="Name.csv", row.names=FALSE, sep=",")".
When the directory is not specified, the file will be
saved in the working directory. For exporting R data into SPSS, SAS and
Stata data, use functions in the the foreign package.
Several functions in this package depend on other R packages:
(1) Hmisc, is used to compute NRI and IDI;
(2) ROCR, is used to produce ROC plots;
(3) epitools, is used to compute univariate odds ratios;
(4) PBSmodelling, is used to produce predictiveness curve.
Acknowledgements
The authors would like to acknowledge Lennart Karssen, Maksim Struchalin and Linda Broer from the Department of Epidemiology, Erasmus Medical Center, Rotterdam for their valuable comments and suggestions to make this package.
Note
The current version of the package includes the basic measures and plots that are used in the assessment of (genetic) risk prediction models and the function to construct a simulated dataset that contains individual genotype data, estimated genetic risk and disease status, used for the evaluation of genetic risk models (see Janssens et al, Genet Med 2006). Planned extensions of the package include functions to construct risk models using Cox Proportional Hazards analysis for prospective data and assess the performance of risk models for time-to-event data.
Author(s)
Suman Kundu
Yurii S. Aulchenko
A. Cecile J.W. Janssens
References
S Kundu, YS Aulchenko, CM van Duijn, ACJW Janssens. PredictABEL:
an R package for the assessment of risk prediction models.
Eur J Epidemiol. 2011;26:261-4.
ACJW Janssens, JPA Ioannidis, CM van Duijn, J Little, MJ Khoury.
Strengthening the Reporting of Genetic Risk Prediction Studies: The GRIPS
Statement Proposal. Eur J Epidemiol. 2011;26:255-9.
ACJW Janssens, JPA Ioannidis, S Bedrosian, P Boffetta, SM Dolan, N Dowling,
I Fortier, AN. Freedman, JM Grimshaw, J Gulcher, M Gwinn, MA Hlatky, H Janes,
P Kraft, S Melillo, CJ O'Donnell, MJ Pencina, D Ransohoff, SD Schully,
D Seminara, DM Winn, CF Wright, CM van Duijn, J Little, MJ Khoury.
Strengthening the reporting of genetic risk prediction studies
(GRIPS)-Elaboration and explanation. Eur J Epidemiol. 2011;26:313-37.
Aulchenko YS, Ripke S, Isaacs A, van Duijn CM. GenABEL: an R package for genome-wide association analysis. Bioinformatics 2007;23(10):1294-6.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.