An R package for the analysis of (genetic) risk prediction studies.

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.

A hypothetical dataset that is used to demonstrate all functions.

Description

ExampleData is a hypothetical dataset constructed to demonstrate all functions in the package. ExampleData is a data frame containing a binary outcome variable (e.g., disease present/absent) and genetic and non-genetic predictor variables for 10,000 persons. In this dataset, column 1 is the ID number, column 2 is the outcome variable, columns 3-10 are non-genetic variables and columns 11-16 are genetic variables.

Usage

data(ExampleData)

Examples

data(ExampleData)
# show first 5 records (rows) of the dataset
head(ExampleData,5)

An example code to construct a risk model using logistic regression analysis.

Description

ExampleModels constructs two risk models using logistic regression analysis. Most of the functions in this package require a logistic regression model as an input and estimate predicted risks from this fitted model. To illustrate these functions without repeating the construction of a logistic regression model, this example code has been created. The function returns two different risk models, riskModel1 which is based on non-genetic predictors and riskModel2 which includes genetic and non-genetic predictors.

Examples

# specify dataset with outcome and predictor variables
data(ExampleData)
# specify column number of the outcome variable
cOutcome <- 2
# specify column numbers of non-genetic predictors
cNonGenPred1 <- c(3:10)
cNonGenPred2 <- c(3:10)
# specify column numbers of non-genetic predictors that are categorical
cNonGenPredCat1 <- c(6:8)
cNonGenPredCat2 <- c(6:8)
# specify column numbers of genetic predictors
cGenPred1 <- c(0)
cGenPred2 <- c(11:16)
# specify column numbers of genetic predictors that are categorical
cGenPredsCat1 <- c(0)
cGenPredsCat2 <- c(0)

# fit logistic regression models
riskmodel1 <- fitLogRegModel(data=ExampleData, cOutcome=cOutcome,
cNonGenPreds=cNonGenPred1, cNonGenPredsCat=cNonGenPredCat1,
cGenPreds=cGenPred1, cGenPredsCat=cGenPredsCat1)
riskmodel2 <- fitLogRegModel(data=ExampleData, cOutcome=cOutcome,
cNonGenPreds=cNonGenPred2, cNonGenPredsCat=cNonGenPredCat2,
cGenPreds=cGenPred2, cGenPredsCat=cGenPredsCat2)

# combine output in a list 
ExampleModels <- list(riskModel1=riskmodel1, riskModel2=riskmodel2)

Function to obtain multivariate odds ratios from a logistic regression model.

Description

The function estimates multivariate (adjusted) odds ratios (ORs) with 95% confidence intervals (CIs) for all the genetic and non-genetic variables in the risk model.

Usage

ORmultivariate(riskModel, filename)

Arguments

Details

The function requires that first a logistic regression model is fitted either by using GLM function or the function fitLogRegModel. In addition to the multivariate ORs, the function returns summary statistics of model performance, namely the Brier score and the Nagelkerke's R^2 value. The Brier score quantifies the accuracy of risk predictions by comparing predicted risks with observed outcomes at individual level (where outcome values are either 0 or 1). The Nagelkerke's R^2 value indicates the percentage of variation of the outcome explained by the predictors in the model.

Value

The function returns:

References

Brier GW. Verification of forecasts expressed in terms of probability. Monthly weather review 1950;78:1-3.

Nagelkerke NJ. A note on a general definition of the coefficient of determination. Biometrika 1991;78:691-692.

See Also

fitLogRegModel

Examples

# specify dataset with outcome and predictor variables
data(ExampleData)
# specify column number of outcome variable
cOutcome <- 2
# specify column numbers of non-genetic predictors
cNonGenPred <- c(3:10)
# specify column numbers of non-genetic predictors that are categorical
cNonGenPredCat <- c(6:8)
# specify column numbers of genetic predictors
cGenPred <- c(11,13:16)
# specify column numbers of genetic predictors that are categorical
cGenPredCat <- c(0)

# fit logistic regression model
riskmodel <- fitLogRegModel(data=ExampleData, cOutcome=cOutcome,
cNonGenPreds=cNonGenPred, cNonGenPredsCat=cNonGenPredCat,
cGenPreds=cGenPred, cGenPredsCat=cGenPredCat)

# obtain multivariate OR(95% CI) for all predictors of the fitted model
ORmultivariate(riskModel=riskmodel)

Function to fit a logistic regression model.

Description

The function fits a standard GLM function for the logistic regression model.

Usage

fitLogRegModel(data, cOutcome, cNonGenPreds, cNonGenPredsCat, 
cGenPreds, cGenPredsCat)

Arguments

Details

The function fits a standard GLM function for the logistic regression model. This function can be used to construct a logistic regression model based on genetic and non-genetic predictors. The function also allows to enter the genetic predictors as a single risk score. For that purpose, the function requires that the dataset additionally includes the risk score. A new dataset can be constructed using "NewExampleData <- cbind(ExampleData,riskScore)". The genetic risk scores can be obtained using the function riskScore in this package or be imported from other methods.

Value

No value returned.

See Also

predRisk, ORmultivariate, riskScore

Examples

# specify dataset with outcome and predictor variables 
data(ExampleData)
# specify column number of outcome variable
cOutcome <- 2 
# specify column numbers of non-genetic predictors
cNonGenPred <- c(3:10)
# specify column numbers of non-genetic predictors that are categorical      
cNonGenPredCat <- c(6:8)
# specify column numbers of genetic predictors
cGenPred <- c(11,13:16)
# specify column numbers of genetic predictors that are categorical
cGenPredCat <- c(0) 

# fit logistic regression model
riskmodel <- fitLogRegModel(data=ExampleData, cOutcome=cOutcome, 
cNonGenPreds=cNonGenPred, cNonGenPredsCat=cNonGenPredCat, 
cGenPreds=cGenPred, cGenPredsCat=cGenPredCat)

# show summary details for the fitted risk model
summary(riskmodel)

Function for calibration plot and Hosmer-Lemeshow goodness of fit test.

Description

The function produces a calibration plot and provides Hosmer-Lemeshow goodness of fit test statistics.

Usage

plotCalibration(data, cOutcome, predRisk, groups, rangeaxis, 
plottitle, xlabel, ylabel, filename, fileplot, plottype)

Arguments

Details

Hosmer-Lemeshow test statistic is a measure of the fit of the model, comparing observed and predicted risks across subgroups of the population. The default number of groups is 10.

The function requires the outcome of interest and predicted risks of all individuals. Predicted risks can be obtained from the functions fitLogRegModel and predRisk or be imported from other packages or methods.

Value

The function creates a calibration plot and returns the following measures:

References

Hosmer DW, Hosmer T, Le Cessie S, Lemeshow S. A comparison of goodness-of-fit tests for the logistic regression model. Stat Med 1997; 16:965-980.

See Also

predRisk

Examples

# specify dataset with outcome and predictor variables 
data(ExampleData)
# specify column number of the outcome variable
cOutcome <- 2

# fit a logistic regression model
# all steps needed to construct a logistic regression model are written in a function
# called 'ExampleModels', which is described on page 4-5
riskmodel <- ExampleModels()$riskModel2

# obtain predicted risks
predRisk <- predRisk(riskmodel)

# specify range of x-axis and y-axis  
rangeaxis <- c(0,1) 
# specify number of groups for Hosmer-Lemeshow test            
groups <- 10 

# compute calibration measures and produce calibration plot                          
plotCalibration(data=ExampleData, cOutcome=cOutcome, predRisk=predRisk, 
groups=groups, rangeaxis=rangeaxis)

Function for box plots of predicted risks separately for individuals with and without the outcome of interest.

Description

The function produces box plots of predicted risks for individuals with and without the outcome of interest and calculates the discrimination slope.

Usage

plotDiscriminationBox(data, cOutcome, predrisk, labels, plottitle, 
ylabel, fileplot, plottype)

Arguments

Details

The discrimination slope is the difference between the mean predicted risks of individuals with and without the outcome of interest. Predicted risks can be obtained using the fitLogRegModel and predRisk or be imported from other programs. The difference between discrimination slopes of two separate risk models is equivalent to (IDI) which is discussed in details in the reclassification function.

Value

The function creates a box plots of predicted risks for individuals with and without the outcome of interest and returns the discrimination slope.

References

Yates JF. External correspondence: decomposition of the mean probability score. Organizational Behavior and Human Performance 1982;30:132-156.

See Also

reclassification, predRisk

Examples

# specify dataset with outcome and predictor variables
data(ExampleData)
# specify column number of outcome variable
cOutcome <- 2

# fit a logistic regression model
# all steps needed to construct a logistic regression model are written in a function
# called 'ExampleModels', which is described on page 4-5
riskmodel <- ExampleModels()$riskModel2

# obtain predicted risks
predRisk <- predRisk(riskmodel)
# specify labels for the groups without and with the outcome of interest 
labels <- c("Without disease", "With disease")    

# produce discrimination box plot     
plotDiscriminationBox(data=ExampleData, cOutcome=cOutcome, predrisk=predRisk, 
labels=labels)

Function for predictiveness curve.

Description

The function creates a plot of cumulative percentage of individuals to the predicted risks.

Usage

plotPredictivenessCurve(predrisk, rangeyaxis, labels, plottitle, 
xlabel, ylabel, fileplot, plottype)

Arguments

Details

The Predictiveness curve is a plot of cumulative percentage of individuals to the predicted risks. Cumulative percentage indicates the percentage of individual that has a predicted risk equal or lower than the risk value. Predicted risks can be obtained using the functions fitLogRegModel and predRisk or be imported from other methods or packages.

Value

The function creates a predictiveness curve.

References

Pepe MS, Feng Z, Huang Y, et al. Integrating the predictiveness of a marker with its performance as a classifier. Am J Epidemiol 2008;167:362-368.

See Also

predRisk

Examples

# specify dataset with outcome and predictor variables 
data(ExampleData)

# fit logistic regression models
# all steps needed to construct a logistic regression model are written in a function
# called 'ExampleModels', which is described on page 4-5
riskmodel1 <- ExampleModels()$riskModel1
riskmodel2 <- ExampleModels()$riskModel2

# obtain predicted risks
predRisk1 <- predRisk(riskmodel1)
predRisk2 <- predRisk(riskmodel2)

# specify range of y-axis
rangeyaxis <- c(0,1) 
# specify labels of the predictiveness curves
labels <- c("without genetic factors", "with genetic factors")

# produce predictiveness curves
plotPredictivenessCurve(predrisk=cbind(predRisk1,predRisk2),
rangeyaxis=rangeyaxis, labels=labels)

Function to plot posterior risks against prior risks.

Description

Function to plot posterior risks against prior risks.

Usage

plotPriorPosteriorRisk(data, priorrisk, posteriorrisk, cOutcome, plottitle, 
xlabel, ylabel, rangeaxis, plotAll=TRUE, labels, filename, fileplot, plottype)

Arguments

Details

The function creates a plot of posterior risks (predicted risks using the updated model) against prior risks (predicted risks using the initial model). Predicted risks can be obtained using the functions fitLogRegModel and predRisk or be imported from other packages or methods.

Value

The function creates a plot of posterior risks against prior risks.

See Also

predRisk

Examples

# specify dataset with outcome and predictor variables
data(ExampleData)
# specify column number of outcome variable
cOutcome <- 2

# fit logistic regression models
# all steps needed to construct a logistic regression model are written in a function
# called 'ExampleModels', which is described on page 4-5
riskmodel1 <- ExampleModels()$riskModel1
riskmodel2 <- ExampleModels()$riskModel2

# obtain predicted risks
predRisk1 <- predRisk(riskmodel1)
predRisk2 <- predRisk(riskmodel2)

# specify label of x-axis 
xlabel <- "Prior risk"                   
# specify label of y-axis
ylabel <- "Posterior risk"               
# specify title for the plot
titleplot <- "Prior versus posterior risk" 
# specify range of the x-axis and y-axis 
rangeaxis <- c(0,1)                       
# labels given to the groups without and with the outcome of interest
labels<- c("without outcome", "with outcome")           

# produce prior risks and posterior risks plot
plotPriorPosteriorRisk(data=ExampleData, priorrisk=predRisk1,
posteriorrisk=predRisk2, cOutcome=cOutcome, xlabel=xlabel, ylabel=ylabel,   
rangeaxis=rangeaxis, plotAll=TRUE, plottitle=titleplot, labels=labels)

Function for a receiver operating characteristic curve (ROC) plot and area under the ROC curve (AUC) value.

Description

The function produces ROC curve and corresponding AUC value with 95% CI. The function can plot one or multiple ROC curves in a single plot.

Usage

plotROC(data, cOutcome, predrisk, labels, plottitle, xlabel, ylabel, 
fileplot, plottype)

Arguments

Details

The function requirs predicted risks or risk scores and the outcome of interest for all individuals. Predicted risks can be obtained using the functions fitLogRegModel and predRisk or be imported from other methods or packages.

Value

The function creates ROC plot and returns AUC value with 95% CI.

References

Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982;143:29-36.

Tobias Sing, Oliver Sander, Niko Beerenwinkel, Thomas Lengauer. ROCR: visualizing classifier performance in R. Bioinformatics 2005;21(20):3940-3941.

See Also

predRisk, plotRiskDistribution

Examples

# specify the arguments in the function to produce ROC plot
# specify dataset with outcome and predictor variables
data(ExampleData)
# specify column number of the outcome variable
cOutcome <- 2

# fit logistic regression models
# all steps needed to construct a logistic regression model are written in a function
# called 'ExampleModels', which is described on page 4-5
riskmodel1 <- ExampleModels()$riskModel1
riskmodel2 <- ExampleModels()$riskModel2

# obtain predicted risks
predRisk1 <- predRisk(riskmodel1)
predRisk2 <- predRisk(riskmodel2)

# specify label of the ROC curve
labels <- c("without genetic factors", "with genetic factors")

# produce ROC curve
plotROC(data=ExampleData, cOutcome=cOutcome, 
predrisk=cbind(predRisk1,predRisk2), labels=labels)

Function to plot histogram of risks separated for individuals with and without the outcome of interest.

Description

Function to plot histogram of risks separated for individuals with and without the outcome of interest.

Usage

plotRiskDistribution(data, cOutcome, risks, interval, rangexaxis, 
rangeyaxis, plottitle, xlabel, ylabel, labels, fileplot, plottype)

Arguments

Value

The function creates the histogram of risks separated for individuals with and without the outcome of interest.

See Also

plotROC, riskScore

Examples

# specify dataset with outcome and predictor variables
data(ExampleData)
# specify column number of the outcome variable
cOutcome <- 2

# fit a logistic regression model
# all steps needed to construct a logistic regression model are written in a function
# called 'ExampleModels', which is described on page 4-5
riskmodel <- ExampleModels()$riskModel2

# obtain predicted risks
predRisk <- predRisk(riskmodel)

# specify the size of each interval
interval <- .05
# specify label of x-axis
xlabel <- "Predicted risk"
# specify label of y-axis
ylabel <- "Percentage"
# specify range of x-axis
xrange <- c(0,1)
# specify range of y-axis
yrange <- c(0,40)
# specify title for the plot
maintitle <- "Distribution of predicted risks"
# specify labels
labels <- c("Without outcome", "With outcome")

# produce risk distribution plot
plotRiskDistribution(data=ExampleData, cOutcome=cOutcome,
risks=predRisk, interval=interval, plottitle=maintitle, rangexaxis=xrange,
rangeyaxis=yrange, xlabel=xlabel, ylabel=ylabel, labels=labels)

Function to plot predicted risks against risk scores.

Description

This function is used to make a plot of predicted risks against risk scores.

Usage

plotRiskscorePredrisk(data, riskScore, predRisk, plottitle, xlabel, 
ylabel, rangexaxis, rangeyaxis, filename, fileplot, plottype)

Arguments

Details

The function creates a plot of predicted risks against risk scores. Predicted risks can be obtained using the functions fitLogRegModel and predRisk or be imported from other methods or packages. The function riskScore can be used to compute unweighted or weighted risk scores.

Value

The function creates a plot of predicted risks against risk scores.

See Also

riskScore, predRisk

Examples

# specify dataset with outcome and predictor variables
data(ExampleData)

# fit a logistic regression  model
# all steps needed to construct a logistic regression model are written in a function
# called 'ExampleModels', which is described on page 4-5
riskmodel <- ExampleModels()$riskModel2

# obtain predicted risks
predRisk <- predRisk(riskmodel)

# specify column numbers of genetic predictors
cGenPred <- c(11:16)

# function to compute unweighted genetic risk scores
riskScore <- riskScore(weights=riskmodel, data=ExampleData, 
cGenPreds=cGenPred, Type="unweighted")

# specify range of x-axis
rangexaxis <- c(0,12)   
# specify range of y-axis
rangeyaxis <- c(0,1)     
# specify label of x-axis
xlabel <- "Risk score"     
# specify label of y-axis
ylabel <- "Predicted risk" 
# specify title for the plot
plottitle <- "Risk score versus predicted risk"

# produce risk score-predicted risk plot
plotRiskscorePredrisk(data=ExampleData, riskScore=riskScore, predRisk=predRisk, 
plottitle=plottitle, xlabel=xlabel, ylabel=ylabel, rangexaxis=rangexaxis, 
rangeyaxis=rangeyaxis)

Function to compute predicted risks for all individuals in the dataset.

Description

Function to compute predicted risks for all individuals in the (new)dataset.

Usage

predRisk(riskModel, data, cID, filename)

Arguments

Details

The function computes predicted risks from a specified logistic regression model. The function fitLogRegModel can be used to construct such a model.

Value

The function returns a vector of predicted risks.

See Also

fitLogRegModel, plotCalibration, plotROC, plotPriorPosteriorRisk

Examples

# specify dataset with outcome and predictor variables
data(ExampleData)
# specify column number of the outcome variable
cOutcome <- 2
# specify column number of ID variable
cID <- 1
# specify column numbers of non-genetic predictors
cNonGenPred <- c(3:10)
# specify column numbers of non-genetic predictors that are categorical
cNonGenPredCat <- c(6:8)
# specify column numbers of genetic predictors
cGenPred <- c(11,13:16)
# specify column numbers of genetic predictors that are categorical
cGenPredCat <- c(0)

# fit logistic regression model
riskmodel <- fitLogRegModel(data=ExampleData, cOutcome=cOutcome,
cNonGenPreds=cNonGenPred, cNonGenPredsCat=cNonGenPredCat,
cGenPreds=cGenPred, cGenPredsCat=cGenPredCat)

# obtain predicted risks 
predRisk <- predRisk(riskModel=riskmodel)

Function for reclassification table and statistics.

Description

The function creates a reclassification table and provides statistics.

Usage

reclassification(data, cOutcome, predrisk1, predrisk2, cutoff)

Arguments

Details

The function creates a reclassification table and computes the categorical and continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI). A reclassification table indicates the number of individuals who move to another risk category or remain in the same risk category as a result of updating the risk model. Categorical NRI equal to x% means that compared with individuals without outcome, individuals with outcome were almost x% more likely to move up a category than down. The function also computes continuous NRI, which does not require any discrete risk categories and relies on the proportions of individuals with outcome correctly assigned a higher probability and individuals without outcome correctly assigned a lower probability by an updated model compared with the initial model. IDI equal to x% means that the difference in average predicted risks between the individuals with and without the outcome increased by x% in the updated model. The function requires predicted risks estimated by using two separate risk models. Predicted risks can be obtained using the functions fitLogRegModel and predRisk or be imported from other methods or packages. p-values in NRI and IDI were rounded upto five decimal places.

Value

The function returns the reclassification table, separately for individuals with and without the outcome of interest and the following measures:

References

Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 2007;115(7):928-935.

Pencina MJ, D'Agostino RB Sr, D'Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008;27(2):157-172; discussion 207-212.

See Also

plotDiscriminationBox, predRisk

Examples

# specify dataset with outcome and predictor variables 
data(ExampleData)
# specify column number of the outcome variable
cOutcome <- 2

# fit logistic regression models
# all steps needed to construct a logistic regression model are written in a function
# called 'ExampleModels', which is described on page 4-5
riskmodel1 <- ExampleModels()$riskModel1
riskmodel2 <- ExampleModels()$riskModel2

# obtain predicted risks
predRisk1 <- predRisk(riskmodel1)
predRisk2 <- predRisk(riskmodel2)
# specify cutoff values for risk categories
cutoff <- c(0,.10,.30,1)    

# compute reclassification measures
reclassification(data=ExampleData, cOutcome=cOutcome, 
predrisk1=predRisk1, predrisk2=predRisk2, cutoff)

Function to compute genetic risk scores.

Description

The function computes unweighted or weighted genetic risk scores. The relative effects (or weights) of genetic variants can either come from beta coefficients of a risk model or from a vector of beta coefficients imported into R, e.g., when beta cofficients are obtained from meta-analysis.

Usage

riskScore(weights, data, cGenPreds, Type)

Arguments

Details

The function calculates unweighted or weighted genetic risk scores. The unweighted genetic risk score is a simple risk allele count assuming that all alleles have the same effect. For this calculation, it is required that the genetic variables are coded as the number of risk alleles. Beta coefficients are used to determine which allele is the risk allele. When the sign of the beta coefficient is negative, the allele coding is reversed. The weighted risk score is a sum of the number of risk alleles multiplied by their beta coefficients.

The beta coefficients can come from two different sources, either beta coefficients of a risk model or a vector of beta coefficients imported into R, e.g., when beta cofficients are obtained from meta-analysis. This vector of beta coefficients should be a named vector containing the same names as mentioned in genetic variants. A logistic regression model can be constructed using fitLogRegModel from this package.

Value

The function returns a vector of risk scores.

Note

When a vector of beta coefficients is imported, it should be checked whether the DNA strands and the coding of the risk alleles are the same as in the study data. The functions are available in the package GenABEL to accurately compute risk scores when the DNA strands are different or the risk alleles are coded differently in the study data and the data used in meta-analysis.

See Also

plotRiskDistribution, plotRiskscorePredrisk

Examples

# specify dataset with outcome and predictor variables
data(ExampleData) 
# specify column numbers of genetic predictors
cGenPred <- c(11:16)

# fit a logistic regression model
# all steps needed to construct a logistic regression model are written in a function
# called 'ExampleModels', which is described on page 4-5
riskmodel <- ExampleModels()$riskModel2

# compute unweighted risk scores 
riskScore <- riskScore(weights=riskmodel, data=ExampleData, 
cGenPreds=cGenPred, Type="unweighted")

Function to construct a simulated dataset containing individual genotype data, genetic risks and disease status for a hypothetical population.

Description

Construct a dataset that contains individual genotype data, genetic risk, and disease status for a hypothetical population. The dataset is constructed using simulation in such a way that the frequencies and odds ratios (ORs) of the genetic variants and the population disease risk computed from this dataset are the same as specified by the input parameters.

Usage

simulatedDataset(ORfreq, poprisk, popsize, filename)

Arguments

Details

The function will execute when the matrix with odds ratios and frequencies, population disease risk and the number of individuals are specified.

The simulation method is described in detail in the references.

The method assumes that (i) the combined effect of the genetic variants on disease risk follows a multiplicative (log additive) risk model; (ii) genetic variants inherit independently, that is no linkage disequilibrium between the variants; (iii) genetic variants have independent effects on the disease risk, which indicates no interaction among variants; and (iv) all genotypes and allele proportions are in Hardy-Weinberg equilibrium. Assumption (ii) and (iv) are used to generate the genotype data, and assumption (ii) and (iii) are used to calculate disease risk.

Simulating the dataset involves three steps: (1) modelling genotype data, (2) modelling disease risks, and (3) modelling disease status. Brief descriptions of these steps are as follows:

(1) Modelling genotype data: For each variant the genotype frequencies are either specified or calculated from the allele frequencies using Hardy-Weinberg equilibrium. Then, the genotypes for each genetic variant are randomly distributed without replacement over all individuals.

(2) Modelling disease risks: For the calculation of the individual disease risk, Bayes' theorem is used, which states that the posterior odds of disease are obtained by multiplying the prior odds by the likelihood ratio (LR) of the individual genotype data. The prior odds are calculated from the population disease risk or disease prevalence (prior odds= prior risk/ (1- prior risk)) and the posterior odds are converted back into disease risk (disease risk= posterior odds/ (1+ posterior odds)). Under the no linkage disequilibrium (LD) assumption, the LR of a genetic profile is obtained by multiplying the LRs of the single genotypes that are included in the risk model. The LR of a single genotype is calculated using frequencies and ORs of genetic variants and population disease risk. See references for more details.

(3) Modelling disease status: To model disease status, we used a procedure that compares the estimated disease risk of each subject to a randomly drawn value between 0 and 1 from a uniform distribution. A subject is assigned to the group who will develop the disease when the disease risk is higher than the random value and to the group who will not develop the disease when the risk is lower than the random value.

This procedure ensures that for each genomic profile, the percentage of people who will develop the disease equals the population disease risk associated with that profile, when the subgroup of individuals with that profile is sufficiently large.

Value

The function returns:

References

Janssens AC, Aulchenko YS, Elefante S, Borsboom GJ, Steyerberg EW, van Duijn CM. Predictive testing for complex diseases using multiple genes: fact or fiction? Genet Med. 2006;8:395-400.

Kundu S, Karssen LC, Janssens AC: Analytical and simulation methods for estimating the potential predictive ability of genetic profiling: a comparison of methods and results. Eur J Hum Genet. 2012 May 30.

van Zitteren M, van der Net JB, Kundu S, Freedman AN, van Duijn CM, Janssens AC. Genome-based prediction of breast cancer risk in the general population: a modeling study based on meta-analyses of genetic associations. Cancer Epidemiol Biomarkers Prev. 2011;20:9-22.

van der Net JB, Janssens AC, Sijbrands EJ, Steyerberg EW. Value of genetic profiling for the prediction of coronary heart disease. Am Heart J. 2009;158:105-10.

Janssens AC, Moonesinghe R, Yang Q, Steyerberg EW, van Duijn CM, Khoury MJ. The impact of genotype frequencies on the clinical validity of genomic profiling for predicting common chronic diseases. Genet Med. 2007;9:528-35.

Examples

# specify the matrix containing the ORs and frequencies of genetic variants 
# In this example we used per allele effects of the risk variants
ORfreq<-cbind(c(1.35,1.20,1.24,1.16), rep(1,4), c(.41,.29,.28,.51),rep(1,4))

# specify the population disease risk
popRisk <- 0.3
# specify size of hypothetical population
popSize <- 10000

# Obtain the simulated dataset
Data <- simulatedDataset(ORfreq=ORfreq, poprisk=popRisk, popsize=popSize)

# Obtain the AUC and produce ROC curve
plotROC(data=Data, cOutcome=4, predrisk=Data[,3])