deltaAUC {clinfun}

Comparing the AUC from ROC curves from nested binary regression

Description

Conducts the test

Usage

  deltaAUC(y, x, z)

Arguments

y

binary response variable

x

matrix of set of covariates that is the basis of the existing (reduced) model

z

matrix of set of covariates that are added to to get the new (full) model

Details

The models are fit using maximum rank correlation (MRC) method which is an alternate approach to logistic regression. In MRC the area under the ROC curve (AUC) is maximized as opposed to the likelihood in logistic regression. Due to invariance of AUC to location and scale shifts one of the parameters (anchor variable) is set to 1.

The first variable (column) in x is used as the anchor variable.

The IPMN data set used as an example in the paper below is included. The columns are high risk lesion (V1), recent weight loss (V2), main duct involvement (V4), presence of a solid component in imaging (V3), and lesion size (V5).

Value

It returns a list with the following elements

par.full

the MRC estimate of parameters for the full model

par.red

the MRC estimate of parameters for the reduced model

results

matrix od results which gives the full reduced model AUCs along with the test statistic and p-value

References

Heller G., Seshan V.E., Moskowitz C.S. and Gonen M. (2016) Inference for the difference in the area under the ROC curve derived from nested binary regression models. Biostatistics 18, 260-274.

Examples

  data(ipmn)
  deltaAUC(ipmn$V1, cbind(ipmn$V4, ipmn$V3, ipmn$V5), ipmn$V2)