APBinary: Estimating the AP and the AUC for Binary Outcome Data.

In APtools: Average Positive Predictive Values (AP) for Binary Outcomes and Censored Event Times

Description

This function calculates the estimates of the AP and AUC for binary outcomes as well as their confidence intervals using the perturbation or the nonparametric bootstrap resampling method.

Usage

  APBinary(status, marker, cut.values = NULL,
    method = "none", alpha = 0.95, B = 1000, weight = NULL)

Arguments

status	Binary indicator, 1 indicates case / the class of prediction interest and 0 otherwise.
marker	Numeric risk score. Data can be continuous or ordinal.
cut.values	risk score values to use as a cut-off for calculation of positive predictive values (PPV) and true positive fractions (TPF). The default value is NULL.
method	Method to obtain confidence intervals. The default is method = "none", in which case only point estimates will be given without confidence intervals. If method= "perturbation", then perturbation based CI will be calculated. If method = "bootstrap", then nonparametric bootstrap based CI will be calculated.
alpha	Confidence level. The default level is 0.95.
B	Number of resampling to obtain confidence interval. The default value is 1000.
weight	Optional. The default weight is 1, same object length as the "status" and "marker" object. Users can use their own weights, and the length of weight is required to be the same as the length of status.

Value

an object of class "APBinary" which is a list with components:

ap_summary	Summary of the AP, including the proportion of cases, a point estimate of AP, and their corresponding confidence intervals.
auc_summary	Summary of the AUC, including a point estimate of AUC with a confidence interval.
PPV	Available object, positive predictive values at the unique risk score in the data.
TPF	Available object, true positive fractions at the unique risk score in the data.

References

Yuan, Y., Su, W., and Zhu, M. (2015). Threshold-free measures for assessing the performance of medical screening tests. Frontiers in Public Health, 3.57.

Bingying Li (2015) Threshold-free Measure for Assessing the Performance of Risk Prediction with Censored Data, MSc. thesis, Simon Fraser University, Canada

Examples

status=c(rep(1,10),rep(0,1),rep(1,18),rep(0,11),rep(1,25),
	rep(0,44),rep(1,85),rep(0,176))
marker=c(rep(7,11),rep(6,29),rep(5,69),rep(4,261))
cut.values=sort(unique(marker)[-1])
out1 <- APBinary(status,marker,cut.values)
out1
out2 <- APBinary(status,marker,method="perturbation",
	alpha=0.90,B=1500)
out2

APtools documentation built on May 1, 2019, 9:10 p.m.