| Title: | Riskset ROC Curve Estimation from Censored Survival Data |
|---|---|
| Description: | Compute time-dependent Incident/dynamic accuracy measures (ROC curve, AUC, integrated AUC )from censored survival data under proportional or non-proportional hazard assumption of Heagerty & Zheng (Biometrics, Vol 61 No 1, 2005, PP 92-105). |
| Authors: | Patrick J. Heagerty <[email protected]>, packaging by Paramita Saha-Chaudhuri <[email protected]> |
| Maintainer: | Paramita Saha-Chaudhuri <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0.4.1 |
| Built: | 2024-11-20 04:49:53 UTC |
| Source: | https://github.com/cran/risksetROC |
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.
CoxWeights(marker, Stime, status, predict.time, entry)CoxWeights(marker, Stime, status, predict.time, entry)
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. |
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.
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 |
Patrick J. Heagerty
Heagerty, P.J., Zheng Y. (2005) Survival Model Predictive Accuracy and ROC curves Biometrics, 61, 92 – 105
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$linear.predictor AUC <- NULL out <- CoxWeights(marker=eta, Stime=survival.time, status=survival.status, predict.time=30) ## to see how well the marker predicts one-month survival AUC <- out$AUClibrary(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
This function integrates AUC using weights
as discussed in Heagerty and Zheng, 2005.
IntegrateAUC(AUC, utimes, St, tmax, weight="rescale")IntegrateAUC(AUC, utimes, St, tmax, weight="rescale")
AUC |
Area under ROC curve at utimes |
utimes |
Unique event times for subjects |
St |
Estimated survival probability at utimes |
tmax |
Maximum time length to be considered |
weight |
Either of "rescale" or "conditional" |
This function estimates time-dependent concordance measure
as discussed in the
paper from AUC and weights derived from
the survival time distribution. The concordance measure is estimated
under the assumption that smaller of the two event times happened
before time tmax. The resulting measure is an weighted sum of
estimated AUC at each unique failure time where weights are
proportional to , and T is failure time of interest.
If weight="rescale", then the weights are rescaled so that the sum of
the weights is one. If weight="conditional", it is assumed that both
the events happened before tmax.
Returns the following item:
iAUC |
Integrated AUC using w(t) as above as weights |
Patrick J. Heagerty
Heagerty, P.J., Zheng Y. (2005) Survival Model Predictive Accuracy and ROC curves Biometrics, 61, 92 – 105
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) ## first find the estimated survival probabilities at unique failure times surv.prob <- unique(survfit(Surv(survival.time,survival.status)~1)$surv) fit0 <- coxph( Surv(survival.time,survival.status) ~ score + cell.type + tx + age, na.action=na.omit ) eta <- fit0$linear.predictor model.score <- eta utimes <- unique( survival.time[ survival.status == 1 ] ) utimes <- utimes[ order(utimes) ] ## find AUC at unique failure times AUC <- rep( NA, length(utimes) ) for( j in 1:length(utimes) ) { out <- CoxWeights( eta, survival.time, survival.status,utimes[j]) AUC[j] <- out$AUC } ## integrated AUC to get concordance measure iAUC <- IntegrateAUC( AUC, utimes, surv.prob, tmax=365 )library(MASS) data(VA) survival.time <- VA$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$stime > 500 ] <- 500 library(survival) ## first find the estimated survival probabilities at unique failure times surv.prob <- unique(survfit(Surv(survival.time,survival.status)~1)$surv) fit0 <- coxph( Surv(survival.time,survival.status) ~ score + cell.type + tx + age, na.action=na.omit ) eta <- fit0$linear.predictor model.score <- eta utimes <- unique( survival.time[ survival.status == 1 ] ) utimes <- utimes[ order(utimes) ] ## find AUC at unique failure times AUC <- rep( NA, length(utimes) ) for( j in 1:length(utimes) ) { out <- CoxWeights( eta, survival.time, survival.status,utimes[j]) AUC[j] <- out$AUC } ## integrated AUC to get concordance measure iAUC <- IntegrateAUC( AUC, utimes, surv.prob, tmax=365 )
This function creates Kaplan-Meier plot.
KM.plot(Stime, survival, max.T=NULL, lty=NULL, all=TRUE, ...)KM.plot(Stime, survival, max.T=NULL, lty=NULL, all=TRUE, ...)
Stime |
unique ordered event times |
survival |
estimates of survival probabilities at Stime |
max.T |
maximum time to be considered for plotting, default is NULL which plots survival till max(Stime)+1 units |
lty |
line type |
all |
TRUE or FALSE, default is TRUE |
... |
additional plot arguments |
This function creates Kaplan-Meier plot. If all=TRUE, then this creates a new plot. If all=FALSE, it adds line to an existing plot and hence must be called after a plot() or similar call.
Patrick J. Heagerty
Heagerty, P.J., Zheng Y. (2005) Survival Model Predictive Accuracy and ROC curves Biometrics, 61, 92 – 105
data(pbc) ## considering only randomized patients pbc1 <- pbc[1:312,] ## create new censoring variable combine 0,1 as 0, 2 as 1 survival.status <- ifelse( pbc1$status==2, 1, 0) survival.time <- pbc1$fudays kout <- weightedKM(Stime=survival.time, status=survival.status) KM.plot(kout$time,kout$survival)data(pbc) ## considering only randomized patients pbc1 <- pbc[1:312,] ## create new censoring variable combine 0,1 as 0, 2 as 1 survival.status <- ifelse( pbc1$status==2, 1, 0) survival.time <- pbc1$fudays kout <- weightedKM(Stime=survival.time, status=survival.status) KM.plot(kout$time,kout$survival)
This function estimates the time-varying parameter estimate
of non-proportional
hazard model using local-linear Cox regression as discussed in
Heagerty and Zheng, 2005.
llCoxReg(Stime, entry=NULL, status, marker, span=0.40, p=1, window="asymmetric")llCoxReg(Stime, entry=NULL, status, marker, span=0.40, p=1, window="asymmetric")
Stime |
For right censored data, this is the follow up time. For left truncated data, this is the ending time for the interval. |
entry |
For left truncated data, this is the entry time of the interval. The default is set to NULL for right censored data. |
status |
Survival status. |
marker |
Marker value. |
span |
bandwidth parameter that controls the size of a local neighborhood. |
p |
1 if only the time-varying coefficient is of interest and 2 if the derivative of time-varying coefficient is also of interest, default is 1 |
window |
Either of "asymmetric" or "symmetric", default is asymmetric. |
This function calculates the parameter estimate
of non-proportional hazard model using local-linear Cox regression as
discussed in Heagerty and Zheng, 2005. This estimation is based on a
time-dependent Cox model (Cai and Sun, 2003). For p=1, the
return item beta has two columns, the first column is the
time-varying parameter estimate, while the second column is the
derivative. However, if the derivative of the time-varying parameter
is of interest, then we suggest to use p=2. In this case,
beta has four columns, the first two columns are the same when
p=1, while the last two columns estimates the coefficients of
squared marker value and its derivative.
Returns a list of following items:
time |
unique failure times |
beta |
estimate of time-varying parameter |
Patrick J. Heagerty
Heagerty, P.J., Zheng Y. (2005) Survival Model Predictive Accuracy and ROC curves Biometrics, 61, 92 – 105
data(pbc) ## considering only randomized patients pbc1 <- pbc[1:312,] ## create new censoring variable combine 0,1 as 0, 2 as 1 survival.status <- ifelse( pbc1$status==2, 1, 0) survival.time <- pbc1$fudays pbc1$status1 <- survival.status fit <- coxph( Surv(fudays,status1) ~ log(bili) + log(protime) + edema + albumin + age, data=pbc1 ) eta5 <- fit$linear.predictors x <- eta5 nobs <- length(survival.time[survival.status==1]) span <- 1.0*(nobs^(-0.2)) ## Not run: bfnx1 <- llCoxReg(Stime=survival.time, status=survival.status, marker=x, span=span, p=1) plot(bfnx1$time, bfnx1$beta[,1], type="l", xlab="Time", ylab="beta(t)") ## End(Not run)data(pbc) ## considering only randomized patients pbc1 <- pbc[1:312,] ## create new censoring variable combine 0,1 as 0, 2 as 1 survival.status <- ifelse( pbc1$status==2, 1, 0) survival.time <- pbc1$fudays pbc1$status1 <- survival.status fit <- coxph( Surv(fudays,status1) ~ log(bili) + log(protime) + edema + albumin + age, data=pbc1 ) eta5 <- fit$linear.predictors x <- eta5 nobs <- length(survival.time[survival.status==1]) span <- 1.0*(nobs^(-0.2)) ## Not run: bfnx1 <- llCoxReg(Stime=survival.time, status=survival.status, marker=x, span=span, p=1) plot(bfnx1$time, bfnx1$beta[,1], type="l", xlab="Time", ylab="beta(t)") ## End(Not run)
This is Mayo PBC data as obtained from the website: http://lib.stat.cmu.edu/datasets/pbc
A data frame with 418 observations and 20 variables: id (patient id), fudays (follow-up days, number of days between registration and the earlier of death, transplantation, or study analysis time in July, 1986), status (survival status), drug (1 = D-penicillamine, 2 = placebo) age (age in days), sex (0 = male, 1 = female), ascites (presence of asictes: 0=no 1=yes), hepatom (presence of hepatomegaly: 0=no 1=yes), spiders (presence of spiders: 0=no 1=yes), edema (presence of edema: 0=no edema and no diuretic therapy for edema; .5 = edema present without diuretics, or edema resolved by diuretics; 1 = edema despite diuretic therapy), bili (serum bilirubin in mg/dl), chol (serum cholesterol in mg/dl), albumin (albumin in gm/dl), copper (urine copper in ug/day), alkphos (alkaline phosphatase in U/liter), sgot (SGOT in U/ml), trig (triglicerides in mg/dl), platelet (platelets per cubic ml / 1000), protime (prothrombin time in seconds), stage (histologic stage of disease)
Patrick J. Heagerty
Heagerty, P.J., Zheng Y. (2005) Survival Model Predictive Accuracy and ROC curves Biometrics, 61, 92 – 105
library(MASS) data(VA) ## need to order the data in ascending order of survival time new.VA=VA[order(VA$stime),] risket.VA=riskset(new.VA)library(MASS) data(VA) ## need to order the data in ascending order of survival time new.VA=VA[order(VA$stime),] risket.VA=riskset(new.VA)
This function creates risk set at each unique failure time from a survival data set.
riskset(dat, entry=FALSE)riskset(dat, entry=FALSE)
dat |
survival dataset with at least three variables: survival.times, survival.status and marker, in that order. The survival data set may have additional variables. In case of interval censored data, the first four columns are: entry time, exit time, status at exit and marker |
entry |
default is FALSE indicating right censored data. TRUE if left truncated data |
This function creates risk set at each unique failure time from a survival data set and is needed for llCoxReg(). The function can handle both right censored and interval censored data.
Returns a new data set with columns as follows: start, finish, newStatus and other variables from the original dataset except survival time and status. The first two columns correspond to the start and end of time intervals considered and the newStatus corresponds to the survival status of the patient corresponding to this interval, i.e. the status is 1 if the patient had event during this interval (start, finish] and 0 otherwise. Note that the survival time need to be in ascending order.
Patrick J. Heagerty
Heagerty, P.J., Zheng Y. (2005) Survival Model Predictive Accuracy and ROC curves Biometrics, 61, 92 – 105
library(MASS) data(VA) ## need to order the data in ascending order of survival time new.VA=VA[order(VA$stime),] risket.VA=riskset(new.VA)library(MASS) data(VA) ## need to order the data in ascending order of survival time new.VA=VA[order(VA$stime),] risket.VA=riskset(new.VA)
This function creates risksetAUC from a survival data set
risksetAUC(Stime, entry=NULL, status, marker, method="Cox", span=NULL, order=1, window="asymmetric", tmax, weight="rescale", plot=TRUE, type="l", xlab="Time", ylab="AUC", ...)risksetAUC(Stime, entry=NULL, status, marker, method="Cox", span=NULL, order=1, window="asymmetric", tmax, weight="rescale", plot=TRUE, type="l", xlab="Time", ylab="AUC", ...)
Stime |
For right censored data, this is the follow up time. For left truncated data, this is the ending time for the interval. |
entry |
For left truncated data, this is the entry time of the interval. The default is set to NULL for right censored data. |
status |
survival status, 1 if had an event and 0 otherwise |
marker |
marker |
method |
either of "Cox", "LocalCox" and "Schoenfeld", default is "Cox" |
span |
bandwidth parameter that controls the size of a local neighborhood, needed for method="LocalCox" or method="Schoenfeld" |
order |
0 or 1, locally mean if 0 and local linear if 1, needed for method="Schoenfeld", default is 1 |
window |
either of "asymmetric" or "symmetric", default is asymmetric, needed for method="LocalCox" |
tmax |
maximum time to be considered for calculation of AUC |
weight |
either of "rescale" or "conditional". If weight="rescale", then weights are rescaled so that the sum is unity. If weight="conditional" both the event times are assumed to be less than tmax |
plot |
TRUE or FALSE, default is TRUE |
type |
default is "l", can be either of "p" for points, "l" for line, "b" for both |
xlab |
label for x-axis |
ylab |
label for y-axis |
... |
additional plot arguments |
This function creates and plots AUC based on incident/dynamic definition of Heagerty, et. al. based on a survival data and marker values. If proportional hazard is assumed then method="Cox" can be used. In case of non-proportional hazard, either of "LocalCox" or "Schoenfeld" can be used. These two methods differ in how the smoothing is done. If plot="TRUE" then the AUC curve is plotted against time (till tmax+1). Additional plot arguments can be supplied.
Returns a list of the following items:
utimes |
ordered unique failure times |
St |
estimated survival probability at utimes |
AUC |
Area under ROC curve at utimes |
Cindex |
Cindex |
Paramita Saha
Heagerty, P.J., Zheng Y. (2005) Survival Model Predictive Accuracy and ROC curves Biometrics, 61, 92 – 105
IntegrateAUC(), weightedKM(), llCoxReg(), SchoenSmooth(), CoxWeights()
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[survival.time>500 ] <- 0 survival.time[survival.time>500 ] <- 500 fit0 <- coxph( Surv(survival.time,survival.status) ~ score + cell.type + tx + age, na.action=na.omit ) eta <- fit0$linear.predictor tmax=365 AUC.CC=risksetAUC(Stime=survival.time, status=survival.status, marker=eta, method="Cox", tmax=tmax);library(MASS) data(VA) survival.time=VA$stime survival.status=VA$status score <- VA$Karn cell.type <- factor(VA$cell) tx <- as.integer( VA$treat==1 ) age <- VA$age survival.status[survival.time>500 ] <- 0 survival.time[survival.time>500 ] <- 500 fit0 <- coxph( Surv(survival.time,survival.status) ~ score + cell.type + tx + age, na.action=na.omit ) eta <- fit0$linear.predictor tmax=365 AUC.CC=risksetAUC(Stime=survival.time, status=survival.status, marker=eta, method="Cox", tmax=tmax);
This function creates risksetROC from a survival data set
risksetROC(Stime, entry=NULL, status, marker, predict.time, method="Cox", span=NULL, order=1, window="asymmetric", prop=0.5, plot=TRUE, type="l", xlab="FP", ylab="TP", ...)risksetROC(Stime, entry=NULL, status, marker, predict.time, method="Cox", span=NULL, order=1, window="asymmetric", prop=0.5, plot=TRUE, type="l", xlab="FP", ylab="TP", ...)
Stime |
For right censored data, this is the follow up time. For left truncated data, this is the ending time for the interval. |
entry |
For left truncated data, this is the entry time of the interval. The default is set to NULL for right censored data. |
status |
survival status, 1 if had an event and 0 otherwise |
marker |
marker |
predict.time |
time point of interest |
method |
either of "Cox", "LocalCox" and "Schoenfeld", default is "Cox" |
span |
bandwidth parameter that controls the size of a local neighborhood, needed for method="LocalCox" or method="Schoenfeld" |
order |
0 or 1, locally mean if 0 and local linear if 1, needed for method="Schoenfeld", default is 1 |
window |
either of "asymmetric" or "symmetric", default is asymmetric, needed for method="LocalCox" |
prop |
what proportion of the time-interval to consider when doing a local Cox fitting at predict.time, needed for method="LocalCox", default is 0.5. |
plot |
TRUE or FALSE, default is TRUE |
type |
default is "l", can be either of "p" for points, "l" for line, "b" for both |
xlab |
label for x-axis |
ylab |
label for y-axis |
... |
additional plot arguments |
This function creates and plots ROC based on incident/dynamic definition of Heagerty, et. al. based on a survival data and marker values. If proportional hazard is assumed then method="Cox" can be used. In case of non-proportional hazard, either of "LocalCox" or "Schoenfeld" can be used. These two methods differ in how the smoothing is done. If plot="TRUE" then the ROC curve is plotted with the diagonal line. Additional plot arguments can be supplied.
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 |
Paramita Saha
Heagerty, P.J., Zheng Y. (2005) Survival Model Predictive Accuracy and ROC curves Biometrics, 61, 92 – 105
llCoxReg(), SchoenSmooth(), CoxWeights()
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[survival.time>500 ] <- 0 survival.time[survival.time>500 ] <- 500 fit0 <- coxph( Surv(survival.time,survival.status) ~ score + cell.type + tx + age, na.action=na.omit ) eta <- fit0$linear.predictor ROC.CC30=risksetROC(Stime=survival.time, status=survival.status, marker=eta, predict.time=30, method="Cox", main="ROC Curve", lty=2, col="red")library(MASS) data(VA) survival.time=VA$stime survival.status=VA$status score <- VA$Karn cell.type <- factor(VA$cell) tx <- as.integer( VA$treat==1 ) age <- VA$age survival.status[survival.time>500 ] <- 0 survival.time[survival.time>500 ] <- 500 fit0 <- coxph( Surv(survival.time,survival.status) ~ score + cell.type + tx + age, na.action=na.omit ) eta <- fit0$linear.predictor ROC.CC30=risksetROC(Stime=survival.time, status=survival.status, marker=eta, predict.time=30, method="Cox", main="ROC Curve", lty=2, col="red")
This function smooths the Schoenfeld residuals using Epanechnikov's optimal kernel.
SchoenSmooth(fit, Stime, status, span=0.40, order=0, entry=NULL)SchoenSmooth(fit, Stime, status, span=0.40, order=0, entry=NULL)
fit |
the result of fitting a Cox regression model, using the coxph function |
Stime |
Survival times in case of right censored data and exit time for left truncated data |
status |
Survival status |
span |
bandwidth parameter that controls the size of a local neighborhood |
order |
0 or 1, locally mean if 0 and local linear if 1 |
entry |
entry time when left censored data is considered, default is NULL for only right censored data |
This function smooths the Schoenfeld residuals to get an estimate of time-varying effect of the marker using Epanechnikov's optimal kernel using either local mean or local linear smoother.
Returns a list of following items:
time |
failure times |
beta |
estimate of time-varying parameter |
Patrick J. Heagerty
Heagerty, P.J., Zheng Y. (2005) Survival Model Predictive Accuracy and ROC curves Biometrics, 61, 92 – 105
data(pbc) ## considering only randomized patients pbc1 <- pbc[1:312,] ## create new censoring variable combine 0,1 as 0, 2 as 1 survival.status <- ifelse( pbc1$status==2, 1, 0) survival.time <- pbc1$fudays pbc1$status1 <- survival.status fit <- coxph( Surv(fudays,status1) ~ log(bili) + log(protime) + edema + albumin + age, data=pbc1 ) eta5 <- fit$linear.predictors x <- eta5 nobs <- length(survival.time[survival.status==1]) span <- 1.5*(nobs^(-0.2)) fitCox5 <- coxph( Surv(survival.time,survival.status) ~ x ) bfnx1.1 <- SchoenSmooth( fit=fitCox5, Stime=survival.time, status=survival.status, span=span, order=1) bfnx1.0 <- SchoenSmooth( fit=fitCox5, Stime=survival.time, status=survival.status, span=span, order=0) plot(bfnx1.1$time, bfnx1.1$beta, type="l", xlab="Time", ylab="beta(t)") lines(bfnx1.0$time, bfnx1.0$beta, lty=3)data(pbc) ## considering only randomized patients pbc1 <- pbc[1:312,] ## create new censoring variable combine 0,1 as 0, 2 as 1 survival.status <- ifelse( pbc1$status==2, 1, 0) survival.time <- pbc1$fudays pbc1$status1 <- survival.status fit <- coxph( Surv(fudays,status1) ~ log(bili) + log(protime) + edema + albumin + age, data=pbc1 ) eta5 <- fit$linear.predictors x <- eta5 nobs <- length(survival.time[survival.status==1]) span <- 1.5*(nobs^(-0.2)) fitCox5 <- coxph( Surv(survival.time,survival.status) ~ x ) bfnx1.1 <- SchoenSmooth( fit=fitCox5, Stime=survival.time, status=survival.status, span=span, order=1) bfnx1.0 <- SchoenSmooth( fit=fitCox5, Stime=survival.time, status=survival.status, span=span, order=0) plot(bfnx1.1$time, bfnx1.1$beta, type="l", xlab="Time", ylab="beta(t)") lines(bfnx1.0$time, bfnx1.0$beta, lty=3)
This function estimates S(t) where sampling weights are permitted.
weightedKM(Stime, status, wt=NULL, entry=NULL)weightedKM(Stime, status, wt=NULL, entry=NULL)
Stime |
Survival times when right censored data is considered. In case of interval censored data this is the end point for the time interval. |
status |
Survival status |
wt |
weight, default is unweighted |
entry |
entry times in case of interval censored data, default is NULL when right censored data is considered |
This function obtains survival function estimate where sampling weights are permitted.
Returns a list of following items:
time |
ordered unique failure times |
survival |
survival estimate at the unique failure times |
Patrick J. Heagerty
Heagerty, P.J., Zheng Y. (2005) Survival Model Predictive Accuracy and ROC curves Biometrics, 61, 92 – 105
data(pbc) ## considering only randomized patients pbc1 <- pbc[1:312,] ## create new censoring variable combine 0,1 as 0, 2 as 1 survival.status <- ifelse( pbc1$status==2, 1, 0) survival.time <- pbc1$fudays kout <- weightedKM(Stime=survival.time, status=survival.status) KM.plot(kout$time,kout$survival)data(pbc) ## considering only randomized patients pbc1 <- pbc[1:312,] ## create new censoring variable combine 0,1 as 0, 2 as 1 survival.status <- ifelse( pbc1$status==2, 1, 0) survival.time <- pbc1$fudays kout <- weightedKM(Stime=survival.time, status=survival.status) KM.plot(kout$time,kout$survival)