| Type: | Package |
| Title: | Surrogate Survival ROC |
| Version: | 0.1.0 |
| Author: | Yunro Chung [cre] |
| Maintainer: | Yunro Chung <yunro.chung@asu.edu> |
| Description: | Nonparametric and semiparametric estimations of the time-dependent ROC curve for an incomplete failure time data with surrogate failure time endpoints. |
| Depends: | R (≥ 3.5.0), survival |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| LazyData: | true |
| NeedsCompilation: | no |
| Packaged: | 2018-08-22 22:28:21 UTC; roy |
| Repository: | CRAN |
| Date/Publication: | 2018-08-30 16:20:03 UTC |
Surrogate Survival ROC
Description
Nonparametric and semiparametric estimations of the time-dependent ROC curve for an incomplete failure time data with surrogate failure time endpoints.
Details
| Package: | isoph |
| Type: | Package |
| Version: | 0.1.0 |
| Date: | 2018-08-17 |
| License: | GPL (>= 2) |
Author(s)
Yunro Chung [cre]
Maintainer: Yunro Chung <yunro.chung@asu.edu>
References
Yunro Chung and Yingye Zheng, Improving efficiency of evaluating prognostic accuracy of biomarkers for incomplete failure-time data with surrogate outcome (in progress)
Surrogate Survival ROC
Description
Nonparametric and semiparametric estimations of the time-dependent ROC curve for an incomplete failure time data with surrogate failure time endpoints
Usage
surrosurvROC(DATA, method, pred.time, wt=NULL, span=NULL, b.rep=200)
Arguments
DATA |
data frame, consisting of Marker: Predictior or marekr value; Survival time; Status: Event indicator (1: event; 0: censoring); STime: Surroagte survival Time; SStatus: Surrogate event indicator (1: event; 0: censoring) |
method |
"KNN"" for nonparametric model using nearest neighborhood kernel; "COX"" for semiparametric proportional hazard model |
pred.time |
Prediction time of the ROC curve |
wt |
Weight, such as inverse probablity weighting |
span |
Smoothing bandwidth parameter for KNN |
b.rep |
Number of bootstrap |
Details
It provides a more efficient time-dependent ROC curve for an incomplete failure time data, when surrogate failure time endpoints are additionally observed for all subjects.
Author(s)
Yunro Chung [cre]
References
Yunro Chung and Yingye Zheng, Evaluating Prognostic Accuracy of Biomarkers for Incomplete and Right-Censored Data with Surrogate Outcome (in progress)
Examples
DATA=data.frame(
Time= c(1,2,5,3,9,NA,8,9,10,NA,NA,NA,6,4,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),
Status= c(1,1,0,0,1,NA,1,1,0, NA,NA,NA,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),
STime= c(3,2,4,2,8,5,8,7,11,1,8,9,3,5,2,5,10,3,5,8,5,2,4,6,7),
SStatus=c(1,0,1,0,1,1,1,0,0,1,1,1,1,0,1,1,0,0,1,0,1,0,1,0,0),
Marker= c(1,5,1,2,3,1,2,3,4,5,9,8,5,7,3,4,2,5,3,4,7,5,9,3,8)
)
#COX at year 3
RES1=surrosurvROC(DATA, method="COX", pred.time=3)
print(RES1)
#KNN at year 3
nobs=sum(!is.na(DATA$Time))
span=0.25*nobs^(-0.20)
RES2=surrosurvROC(DATA, method="KNN",pred.time=3,span=span)
print(RES2)