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 |

`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 |

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 |

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
# 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)
``` |

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