| Title: | 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. |
| Authors: | Yunro Chung [cre] |
| Maintainer: | Yunro Chung <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.0 |
| Built: | 2025-02-22 03:23:46 UTC |
| Source: | https://github.com/cran/surrosurvROC |
Nonparametric and semiparametric estimations of the time-dependent ROC curve for an incomplete failure time data with surrogate failure time endpoints.
| Package: | isoph |
| Type: | Package |
| Version: | 0.1.0 |
| Date: | 2018-08-17 |
| License: | GPL (>= 2) |
Yunro Chung [cre]
Maintainer: Yunro Chung <[email protected]>
Yunro Chung and Yingye Zheng, Improving efficiency of evaluating prognostic accuracy of biomarkers for incomplete failure-time data with surrogate outcome (in progress)
Nonparametric and semiparametric estimations of the time-dependent ROC curve for an incomplete failure time data with surrogate failure time endpoints
surrosurvROC(DATA, method, pred.time, wt=NULL, span=NULL, b.rep=200)surrosurvROC(DATA, method, pred.time, wt=NULL, span=NULL, b.rep=200)
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 |
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.
Yunro Chung [cre]
Yunro Chung and Yingye Zheng, Evaluating Prognostic Accuracy of Biomarkers for Incomplete and Right-Censored Data with Surrogate Outcome (in progress)
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$Time)) span=0.25*nobs^(-0.20) RES2=surrosurvROC(DATA, method="KNN",pred.time=3,span=span) print(RES2)DATA=data.frame( Time= c(1,2,5,3,9,NA,8,9,10,NA,NA,NA,6,4,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA), Status= c(1,1,0,0,1,NA,1,1,0, NA,NA,NA,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA), STime= c(3,2,4,2,8,5,8,7,11,1,8,9,3,5,2,5,10,3,5,8,5,2,4,6,7), SStatus=c(1,0,1,0,1,1,1,0,0,1,1,1,1,0,1,1,0,0,1,0,1,0,1,0,0), Marker= c(1,5,1,2,3,1,2,3,4,5,9,8,5,7,3,4,2,5,3,4,7,5,9,3,8) ) #COX at year 3 RES1=surrosurvROC(DATA, method="COX", pred.time=3) print(RES1) #KNN at year 3 nobs=sum(!is.na(DATA$Time)) span=0.25*nobs^(-0.20) RES2=surrosurvROC(DATA, method="KNN",pred.time=3,span=span) print(RES2)