CoxWeights: Incident/Dynamic (I/D) ROC curve, AUC and integrated AUC...

In risksetROC: Riskset ROC curve estimation from censored survival data

Description

This function estimates of TP and FP based on a Cox model as discussed in Heagerty and Zheng, 2005, for incident/dynamic ROC curve. TP is estimated as Equation (1) and FP is estimated as Equation (2) of the paper.

Usage

CoxWeights(marker, Stime, status, predict.time, entry)

Arguments

marker	estimated linear predictor from a set of covariates. Note that this linear predictor can be obtained from any model.
Stime	For right censored data, this is the follow up time. For left truncated data, this is the ending time for the interval.
status	Indicator of status, 1 if death or event, 0 otherwise.
predict.time	Time point of the ROC curve.
entry	For left truncated data, this is the entry time of the interval. The default is set to NULL for right censored data.

Details

Suppose we have censored survival data (right censored or both left-truncated and right censored data) along with a marker value and we want to see how well the marker predicts the survival time for the subjects in the dataset using Incident/dynamic definition of ROC curve. In particular, suppose we have survival times in days and we want to see how well the marker predicts the one-year survival (predict.time=365 days). This function CoxWeights(), returns the unique marker values, TP (True Positive), FP (False Positive) and AUC (Area under (ROC) curve) corresponding to the time point of interest (predict.time). Note that the linear predictor marker can be obtained from any model, specifically, the survival model may be based on either a PH or a time-varying Cox model.

Value

Returns a list of the following items:

eta	unique marker values for calculation of TP and FP
TP	True Positive values corresponding to unique marker values
FP	False Positive values corresponding to unique marker values
AUC	Area Under (ROC) Curve at time predict.time

Author(s)

Patrick J. Heagerty

References

Heagerty, P.J., Zheng Y. (2005) Survival Model Predictive Accuracy and ROC curves Biometrics, 61, 92 – 105

Examples

library(MASS)
data(VA)
survival.time <- VA$stime
survival.status <- VA$status
score <- VA$Karn
cell.type <- factor(VA$cell )
tx <- as.integer( VA$treat==1 )
age <- VA$age
survival.status[VA$stime > 500 ] <- 0
survival.time[VA$stime > 500 ] <- 500
library(survival)
fit0 <- coxph( Surv(survival.time,survival.status)
        ~ score + cell.type + tx + age, na.action=na.omit )
summary(fit0)
eta <- fit0$linear.predictor
AUC <- NULL
out <- CoxWeights(marker=eta, Stime=survival.time, status=survival.status,
predict.time=30)
## to see how well the marker predicts one-month survival
AUC <- out$AUC

risksetROC documentation built on May 30, 2017, 6:31 a.m.