ORmultivariate: Function to obtain multivariate odds ratios from a logistic...
In PredictABEL: Assessment of risk prediction models

Description Usage Arguments Details Value References See Also Examples

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.

1	ORmultivariate(riskModel, filename)

`riskModel`	Name of logistic regression model that can be fitted using the function `fitLogRegModel`.
`filename`	Name of the output file in which the multivariate ORs will be saved. If no directory is specified, the file is saved in the working directory as a txt file. When `filename` is not specified, the output is not saved.

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.

The function returns:

`Predictors Summary`	OR with 95% CI and corresponding p-values for each predictor in the model
`Brier Score`	Brier score
`Nagelkerke Index`	Nagelkerke's R^2 value

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.

fitLogRegModel, ORunivariate

# 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, filename="multiOR.txt")