Package 'survivalSL'

Description

A data frame with 1912 French kidney transplant recipients from the DIVAT cohort.

Usage

data(dataDIVAT2)

Format

A data frame with the 4 following variables:

age

This numeric vector provides the age of the recipient at the transplantation (in years).

hla

This numeric vector provides the indicator of transplantations with at least 4 HLA incompatibilities between the donor and the recipient (1 for high level and 0 otherwise).

retransplant

This numeric vector provides the indicator of re-transplantation (1 for more than one transplantation and 0 for first kidney transplantation).

ecd

The Expended Criteria Donor (1 for transplantations from ECD and 0 otherwise). ECD are defined by widely accepted criteria, which includes donors older than 60 years of age or 50-59 years of age with two of the following characteristics: history of hypertension, cerebrovascular accident as the cause of death or terminal serum creatinine higher than 1.5 mg/dL.

times

This numeric vector is the follow up times of each patient.

failures

This numeric vector is the event indicator (0=right censored, 1=event). An event is considered when return in dialysis or patient death with functioning graft is observed.

Source

URL: www.divat.fr

References

Le Borgne F, Giraudeau B, Querard AH, Giral M and Foucher Y. Comparisons of the performances of different statistical tests for time-to-event analysis with confounding factors: practical illustrations in kidney transplantation. Statistics in medicine. 30;35(7):1103-16, 2016. <doi:10.1002/ sim.6777>

Examples

data(dataDIVAT2)

# Compute the non-adjusted Hazard Ratio related to the ECD versus SCD
cox.ecd<-coxph(Surv(times, failures) ~ ecd, data=dataDIVAT2)
summary(cox.ecd) # Hazard Ratio = 1.97

Description

A data frame with 4267 French kidney transplant recipients.

Usage

data(dataDIVAT3)

Format

A data frame with 4267 observations for the 8 following variables.

ageR

This numeric vector represents the age of the recipient (in years)

sexeR

This numeric vector represents the gender of the recipient (1=men, 0=female)

year.tx

This numeric vector represents the year of the transplantation

ante.diab

This numeric vector represents the diabetes statute (1=yes, 0=no)

pra

This numeric vector represents the pre-graft immunization using the panel reactive antibody (1=detectable, 0=undetectable)

ageD

This numeric vector represents the age of the donor (in years)

death.time

This numeric vector represents the follow up time in days (until death or censoring)

death

This numeric vector represents the death indicator at the follow-up end (1=death, 0=alive)

Source

URL: www.divat.fr

References

Le Borgne et al. Standardized and weighted time-dependent ROC curves to evaluate the intrinsic prognostic capacities of a marker by taking into account confounding factors. Manuscript submitted. Stat Methods Med Res. 27(11):3397-3410, 2018. <doi: 10.1177/ 0962280217702416.>

Examples

data(dataDIVAT3)

### a short summary of the recipient age at the transplantation
summary(dataDIVAT3$ageR)

### Kaplan and Meier estimation of the recipient survival
plot(survfit(Surv(death.time/365.25, death) ~ 1, data = dataDIVAT3),
 xlab="Post transplantation time (in years)", ylab="Patient survival",
 mark.time=FALSE)

Description

A data frame with 1300 simulated French patients with multiple sclerosis from the OFSEP cohort. The baseline is 1 year after the initiation of the first-line treatment.

Usage

data(dataOFSEP)

Format

A data frame with 1300 observations for the 3 following variables:

time

This numeric vector represents the follow up time in years (until disease progression or censoring)

event

This numeric vector represents the disease progression indicator at the follow-up end (1=progression, 0=censoring)

age

This numeric vector represents the patient age (in years) at baseline.

duration

This numeric vector represents the disease duration (in days) at baseline.

period

This numeric vector represents the calendar period: 1 in-between 2014 and 2018, and 0 otherwise.

gender

This numeric vector represents the gender: 1 for women.

relapse

This numeric vector represents the diagnosis of at least one relapse since the treatment initiation : 1 if at leat one event, and 0 otherwise.

edss

This vector of character string represents the EDSS level: "miss" for missing, "low" for EDSS between 0 to 2, and "high" otherwise.

t1

This vector of character string represents the new gadolinium-enhancing T1 lesion: "missing", "0" or "1+" for at least 1 lesion.

t2

This vector of character string represents the new T2 lesions: "no" or "yes".

rio

This numeric vector represents the modified Rio score.

Examples

data(dataOFSEP)

### Kaplan and Meier estimation of the disease progression free survival
plot(survfit(Surv(time, event) ~ 1, data = dataOFSEP),
     ylab="Disease progression free survival",
     xlab="Time after the first anniversary of the first-line treatment in years")

Description

Fit an AFT parametric model with a gamma distribution.

Usage

LIB_AFTgamma(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL, data)

Arguments

Details

The model is obtained by using the dist="gamma" in the flexsurvreg package.

Value

References

Jackson, C. (2016). flexsurv: A Platform for Parametric Survival Modeling in R. Journal of Statistical Software, 70(8), 1-33. doi:10.18637/jss.v070.i08

Examples

data(dataDIVAT2)

# The estimation of the model from the first 200 lignes
model <- LIB_AFTgamma(times="times", failures="failures", data=dataDIVAT2[1:200,],
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"))

# The predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

Description

Fit an AFT parametric model with a generalized gamma distribution.

Usage

LIB_AFTggamma(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL, data)

Arguments

Details

The model is obtained by using the dist="gengamma" in the flexsurvreg package.

Value

References

Jackson, C. (2016). flexsurv: A Platform for Parametric Survival Modeling in R. Journal of Statistical Software, 70(8), 1-33. doi:10.18637/jss.v070.i08

Examples

data(dataDIVAT2)

# The estimation of the model from the first 200 lignes
model <- LIB_AFTggamma(times="times", failures="failures", data=dataDIVAT2[1:200,],
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"))

# The predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

Description

Fit an AFT parametric model with a log logistic distribution.

Usage

LIB_AFTllogis(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL, data)

Arguments

Details

The model is obtained by using the dist="llogis" in the flexsurvreg package.

Value

References

Jackson, C. (2016). flexsurv: A Platform for Parametric Survival Modeling in R. Journal of Statistical Software, 70(8), 1-33. doi:10.18637/jss.v070.i08

Examples

data(dataDIVAT2)

# The estimation of the model from the first 200 lignes
model <- LIB_AFTllogis(times="times", failures="failures", data=dataDIVAT2[1:200,],
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"))

# The predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

Description

Fit an AFT parametric model with a Weibull distribution.

Usage

LIB_AFTweibull(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL, data)

Arguments

Details

The model is obtained by using the dist="weibull" in the flexsurvreg package.

Value

References

Jackson, C. (2016). flexsurv: A Platform for Parametric Survival Modeling in R. Journal of Statistical Software, 70(8), 1-33. doi:10.18637/jss.v070.i08

Examples

data(dataDIVAT2)

# The estimation of the model from the first 200 lignes
model <- LIB_AFTweibull(times="times", failures="failures", data=dataDIVAT2[1:200,],
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"))

# The predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

Description

Fit a Cox regression for a selection of covariate.

Usage

LIB_COXaic(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL,
data, final.model)

Arguments

Value

References

Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5), 1-13, https://www.jstatsoft.org/v39/i05/

Examples

data(dataDIVAT2)

# The estimation of the model
model <- LIB_COXaic(times="times", failures="failures", data=dataDIVAT2,
  final.model=c("age"),  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"))

# The predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)", ylab="Predicted survival",
     col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

Description

Fit a Cox regression for all covariates to be used in the super learner.

Usage

LIB_COXall(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL, data)

Arguments

Details

The Cox regression is obtained by using the survival package.

Value

References

Terry M. Therneau (2021). A Package for Survival Analysis in R. R package version 3.2-13, https://CRAN.R-project.org/package=survival.

Examples

data(dataDIVAT2)

# The estimation of the model
model <- LIB_COXall(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"))

# The predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

Description

Fit an elastic net Cox regression for fixed values of the regularization parameters.

Usage

LIB_COXen(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL,
data, alpha, lambda)

Arguments

Details

The elastic net Cox regression is obtained by using the glmnet package.

Value

References

Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5), 1-13, https://www.jstatsoft.org/v39/i05/

Examples

data(dataDIVAT2)

# The estimation of the model
model <- LIB_COXen(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"), lambda=.1, alpha=.1)

# The predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

Description

Fit a Lasso Cox regression for a fixed value of the regularization parameter.

Usage

LIB_COXlasso(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL,
data, lambda)

Arguments

Details

The Lasso Cox regression is obtained by using the glmnet package.

Value

References

Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5), 1-13, https://www.jstatsoft.org/v39/i05/

Examples

data(dataDIVAT2)

# The estimation of the model
model <- LIB_COXlasso(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"), lambda=1)

# The predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

Description

Fit a ridge Cox regression for a fixed value of the regularization parameter.

Usage

LIB_COXridge(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL,
data, lambda)

Arguments

Details

The ridge Cox regression is obtained by using the glmnet package.

Value

References

Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5), 1-13, https://www.jstatsoft.org/v39/i05/

Examples

data(dataDIVAT2)

# The estimation of the model
model <- LIB_COXridge(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"), lambda=1)

# The predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)", ylab="Predicted survival",
     col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

Description

Fit a PH model with an Exponential distribution.

Usage

LIB_PHexponential(times, failures, group=NULL, cov.quanti=NULL,
cov.quali=NULL, data)

Arguments

Details

The model is obtained by using the dist="exp" in the flexsurvreg package.

Value

References

Jackson, C. (2016). flexsurv: A Platform for Parametric Survival Modeling in R. Journal of Statistical Software, 70(8), 1-33. doi:10.18637/jss.v070.i08

Examples

data(dataDIVAT2)

# The estimation of the model from the first 200 lignes
model <- LIB_PHexponential(times="times", failures="failures", data=dataDIVAT2[1:200,],
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"))

# The predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

Description

Fit a PH parametric model with a Gompertz distribution.

Usage

LIB_PHgompertz(times, failures, group=NULL, cov.quanti=NULL,
cov.quali=NULL, data)

Arguments

Details

The model is obtained by using the dist="gompertz" in the flexsurvreg package.

Value

References

Jackson, C. (2016). flexsurv: A Platform for Parametric Survival Modeling in R. Journal of Statistical Software, 70(8), 1-33. doi:10.18637/jss.v070.i08

Examples

data(dataDIVAT2)

# The estimation of the model from the first 200 lignes
model <- LIB_PHgompertz(times="times", failures="failures", data=dataDIVAT2[1:200,],
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"))

# The predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

Description

Fit an PH model with a survival function is modelled as a natural cubic spline function.

Usage

LIB_PHspline(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL,
data, k)

Arguments

Details

The model is obtained by using the scale="hazard" in the flexsurvreg package.

Value

References

Jackson, C. (2016). flexsurv: A Platform for Parametric Survival Modeling in R. Journal of Statistical Software, 70(8), 1-33. doi:10.18637/jss.v070.i08

Examples

data(dataDIVAT2)

# The estimation of the model from the first 200 lignes with two knots
model <- LIB_PHspline(times="times", failures="failures", data=dataDIVAT2[1:200,],
        cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"), k=2)

# The predicted survival of the first subject of the training sample
  plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
       ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

Description

Fit a neural network based on the partial logistic regression.

Usage

LIB_PLANN(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL,
data, inter, size, decay, maxit, MaxNWts)

Arguments

Details

This function is based is based on the survivalPLANN from the related package.

Value

References

Biganzoli E, Boracchi P, Mariani L, and et al. Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach. Stat Med, 17:1169-86, 1998.

Examples

data(dataDIVAT2)

# The neural network based from the first 300 individuals of the data base

model <- LIB_PLANN(times="times", failures="failures", data=dataDIVAT2[1:300,],
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"),
  inter=0.5, size=32, decay=0.01, maxit=100, MaxNWts=10000)

# The predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

Description

Fit survival random forest tree for given values of the regularization parameters.

Usage

LIB_RSF(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL,
data, nodesize, mtry, ntree)

Arguments

Details

The survival random forest tree is obtained by using the randomForestSRC package.

Value

References

Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5), 1-13, https://www.jstatsoft.org/v39/i05/

Examples

data(dataDIVAT2)

# The estimation of the model
model <- LIB_RSF(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"), nodesize=10,
  mtry=2, ntree=100)

# The predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

Description

Fit a 1-layer neural network based on the partial likelihood from a Cox proportional hazards model.

Usage

LIB_SNN(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL,
data, n.nodes, decay, batch.size, epochs)

Arguments

Details

This function is based is based on the deepsurv from the survivalmodels package. You need to call Python using reticulate. In order to use it, the required Python packages must be installed with reticulate::py_install. Therefore, before running the present LIB_SNN function, you must install and call for the reticulate and survivalmodels packages, and install pycox by using the following command: install_pycox(pip = TRUE, install_torch = FALSE). The survivalSL package functions without these supplementary installations if this learner is not included in the library.

Value

References

Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018). DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Medical Research Methodology, 18(1), 24. https://doi.org/10.1186/s12874-018-0482-1

Description

Compute several metrics to evaluate the prognostic capacities with time-to-event data.

Usage

metrics(times, failures, data, prediction.matrix, prediction.times, metric,
pro.time=NULL, ROC.precision=seq(.01, .99, by=.01))

Arguments

Details

The following metrics can be used: "bs" for the Brier score at the prognostic time pro.time, "ci" for the concordance index at the prognostic time pro.time, "loglik" for the log-likelihood, "ibs" for the integrated Brier score up to the last observed time of event, "ibll" for the integrated binomial log-likelihood up to the last observed time of event, "bll" for the binomial log-likelihood, "ribs" for the restricted integrated Brier score up to the prognostic time pro.time, "ribll" for the restricted integrated binomial log-likelihood up to the last observed time of event, "bll" for the binomial log-likelihood, "auc" for the area under the time-dependent ROC curve up to the prognostic time pro.time.

Value

A numeric value with the metric estimation.

Examples

data(dataDIVAT2)

# The estimation of the model
model <- LIB_COXridge(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"), lambda=1)

# The apparent AUC at 10-year post-transplantation
metrics(times="times", failures="failures", data=dataDIVAT2,
  prediction.matrix=model$predictions, prediction.times=model$times,
  metric="auc", pro.time=10)

# The integrated Brier score up to 10 years post-transplanation
metrics(times="times", failures="failures", data=dataDIVAT2,
  prediction.matrix=model$predictions, prediction.times=model$times,
  metric="ribs", pro.time=10)

Description

A calibration plot of an object of the class libsl (library of survival super learner).

Usage

## S3 method for class 'libsl'
plot(x, n.groups=5, pro.time=NULL,
newdata=NULL, times=NULL, failures=NULL, ...)

Arguments

Details

The plot represents the observed survival and the related 95% confidence intervals, which are respectively estimated by the Kaplan and Meier estimator and the Greenwood formula, against the mean of the predictive values for individuals stratified into groups of the same size according to the percentiles. The identity line is usually included for reference.

Value

No return value for this S3 method.

See Also

plot.default

Examples

data(dataDIVAT2)

# The estimation of the model from the first 200 lignes
model <- LIB_COXall(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"))

# The calibration plot from the validation sample of 150 patients
plot(model, n.groups=5, pro.time=12, col=3,
     xlab="Predicted 12-year survival", ylab="Observed 12-year survival",
     newdata=dataDIVAT2[151:300,], times="times", failures="failures")

Description

A plot of ROC curves is produced.

Usage

## S3 method for class 'rocrisca'
plot(x, ..., information=TRUE)

Arguments

Value

No return value for this S3 method.

See Also

plot.default

Examples

data(dataDIVAT3)

# A subgroup analysis to reduce the time needed for this example

dataDIVAT3 <- dataDIVAT3[1:400,]

# The time-dependent ROC curve to evaluate the
# capacities of the recipient age for the prognosis of post-kidney
# transplant mortality up to 2000 days.

# Compute the raw sensitivity and specificity
roc1 <- roc(times="death.time", failures="death", variable="ageR",
confounders=~1, data=dataDIVAT3, pro.time=2000,
precision=seq(0.1,0.9, by=0.2))

plot(roc1, type="b", col=1, pch=2, lty=2, xlab="1-specificity", ylab="sensibility")

Description

A calibration plot of a Super Learner obtained by the function survivalSL.

Usage

## S3 method for class 'sltime'
plot(x, method, n.groups, pro.time, newdata,
times, failures, ...)

Arguments

Details

The plot represents the observed survival and the related 95% confidence intervals, which are respectively estimated by the Kaplan and Meier estimator and the Greenwood formula, against the mean of the predictive values for individuals stratified into groups of the same size according to the percentiles. The identity line is usually included for reference.

Value

No return value for this S3 method.

See Also

plot.default

Examples

data(dataDIVAT2)

#The outcome model base on a Super Learner from the first 150 individuals of the data base
sl1 <- survivalSL( methods=c("LIB_AFTgamma", "LIB_PHgompertz"),  metric="ci",
  data=dataDIVAT2[1:150,],  times="times", failures="failures", group="ecd",
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant"), cv=3)

# The calibration plot from the validation sample of 150 patients
plot(sl1, method="sl", n.groups=5, pro.time=12, col=2,
     xlab="Predicted 12-year survival", ylab="Observed 12-year survival",
     newdata=dataDIVAT2[151:300,], times="times", failures="failures")

Description

Predict the survival based on a model or algorithm from an object of the class libsl.

Usage

## S3 method for class 'libsl'
predict(object, newdata, newtimes, ...)

Arguments

Details

The model object is obtained from the flexsurvreg package.

Value

Examples

data(dataDIVAT2)

# The estimation of the model from the first 200 lines
model <- LIB_PHgompertz(times="times", failures="failures", data=dataDIVAT2[1:200,],
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"))

# Predicted survival for 2 new subjects
pred <- predict(model,
  newdata=data.frame(age=c(52,52), hla=c(0,1), retransplant=c(1,1), ecd=c(0,1)))

plot(y=pred$predictions[1,], x=pred$times, xlab="Time (years)", ylab="Predicted survival",
     col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

lines(y=pred$predictions[2,], x=pred$times, col=2, type="l", lty=1, lwd=2)

legend("bottomright", col=c(1,2), lty=1, lwd=2, c("Subject #1", "Subject #2"))

Description

Predict the survival of new observations based on an SL by using the survivalSL function.

Usage

## S3 method for class 'sltime'
predict(object, newdata, newtimes, ...)

Arguments

Value

See Also

survivalSL.

Examples

data(dataDIVAT2)

# The training of the super learner from the first 150 individuals of the data base
sl1 <- survivalSL(method=c("LIB_COXridge", "LIB_AFTggamma"),  metric="ci",
  data=dataDIVAT2[1:150,],  times="times", failures="failures", pro.time = 12,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"), cv=3)

# Individual prediction for 2 new subjects
pred <- predict(sl1,
  newdata=data.frame(age=c(52,52), hla=c(0,1), retransplant=c(1,1), ecd=c(0,1)))

plot(y=pred$predictions$sl[1,], x=pred$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

lines(y=pred$predictions$sl[2,], x=pred$times, col=2, type="l", lty=1, lwd=2)

legend("bottomright", col=c(1,2), lty=1, lwd=2, c("Subject #1", "Subject #2"))

Description

Print the model or algorithm.

Usage

## S3 method for class 'libsl'
print(x, ...)

Arguments

Value

No return value for this S3 method.

See Also

LIB_AFTgamma, LIB_AFTggamma, LIB_AFTllogis, LIB_AFTweibull, LIB_PHexponential, LIB_PHgompertz.

Examples

data(dataDIVAT2)

model <- LIB_AFTgamma(times="times", failures="failures",  data=dataDIVAT2[1:100,],
        cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"))

print(model)

Description

Print the contribution of learners included in the super learner.

Usage

## S3 method for class 'sltime'
print(x,  digits=7, ...)

Arguments

Value

No return value for this S3 method.

Examples

data(dataDIVAT2)

sl1 <- survivalSL(method=c("LIB_COXridge", "LIB_AFTggamma"),  metric="ci",
  data=dataDIVAT2[1:150,],  times="times", failures="failures", pro.time = 12,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"), cv=3)

print(sl1, digits=4)

Description

This function allows for the estimation of time-dependent ROC curve by considering possible confounding factors. This method is implemented by standardizing and weighting based on an IPW estimator.

Usage

roc(times, failures, variable, confounders, data,
 pro.time, precision=seq(.01, .99, by=.01))

Arguments

Details

This function computes confounder-adjusted time-dependent ROC curve with right-censored data. We adapted the naive IPCW estimator as explained by Blanche, Dartigues and Jacqmin-Gadda (2013) by considering the probability of experiencing the event of interest before the fixed prognostic time, given the possible confounding factors.

Value

References

Blanche et al. (2013) Review and comparison of roc curve estimators for a time-dependent outcome with marker-dependent censoring. Biometrical Journal, 55, 687-704. <doi:10.1002/ bimj.201200045>

Le Borgne et al. Standardized and weighted time-dependent ROC curves to evaluate the intrinsic prognostic capacities of a marker by taking into account confounding factors. Stat Methods Med Res. 27(11):3397-3410, 2018. <doi: 10.1177/ 0962280217702416>.

Examples

# import and attach the data example
data(dataDIVAT3)

# A subgroup analysis to reduce the time needed for this example

dataDIVAT3 <- dataDIVAT3[1:400,]

# The standardized and weighted time-dependent ROC curve to evaluate the
# capacities of the recipient age for the prognosis of post kidney
# transplant mortality up to 2000 days by taking into account the
# donor age and the recipient gender.

# 1. Standardize the marker according to the covariates among the controls
lm1 <- lm(ageR ~ ageD + sexeR, data=dataDIVAT3[dataDIVAT3$death.time >= 2500,])
dataDIVAT3$ageR_std <- (dataDIVAT3$ageR - (lm1$coef[1] + lm1$coef[2] * dataDIVAT3$ageD +
 lm1$coef[3] * dataDIVAT3$sexeR)) / sd(lm1$residuals)

# 2. Compute the sensitivity and specificity from the proposed IPW estimators
roc2 <- roc(times="death.time", failures="death", variable="ageR_std",
confounders=~bs(ageD, df=3) + sexeR, data=dataDIVAT3, pro.time=2000,
precision=seq(0.1,0.9, by=0.2))

# The corresponding ROC graph
plot(roc2, col=2, pch=2, lty=1, type="b", xlab="1-specificity", ylab="sensibility")

# The corresponding AUC
roc2$auc

Description

Return predictive performances of a model or algorithm obtained by a library of the class libsl.

Usage

## S3 method for class 'libsl'
summary(object, newdata=NULL, ROC.precision=seq(.01,.99,.01), digits=7, ...)

Arguments

Details

The following metrics are returned: "brier" for the Brier score at the prognostic time pro.time, "ibs" for the Integrated Brier score up to the last observed time of event, "ibll" for the Integrated Binomial Log-likelihood up to the last observed time of event, "bll" for the binomial Log-likelihood, "ribs" for the restricted Integrated Brier score up to the prognostic time pro.time, "ribll" for the restricted Integrated Binomial Log-likelihood Log-likelihood up to the last observed time of event, "bll" for the binomial Log-likelihood, "auc" for the area under the time-dependent ROC curve up to the prognostic time pro.time.

Value

No return value for this S3 method.

See Also

LIB_AFTgamma, LIB_AFTggamma, LIB_AFTllogis, LIB_AFTweibull, LIB_PHexponential, LIB_PHgompertz.

Examples

data(dataDIVAT2)

# The training of the Weibull model with the first 400 patients
model <- LIB_PHgompertz(times="times", failures="failures", data=dataDIVAT2[1:400,],
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"))

# The prognostic capacities from the same training sample
# (up to 4 years forseveral indicators)
summary(model, pro.time=4)

# The prognostic capacities from a validation of the next 150 patients
# (up to 4 years for several indicators)
 summary(model, pro.time=4, newdata=dataDIVAT2[401:550,], times="times",
 failures="failures")

Description

Return goodness-of-fit indicators of a Super Learner obtained by the function survivalSL.

Usage

## S3 method for class 'sltime'
summary(object,  method="sl", newdata=NULL,
ROC.precision=seq(.01,.99,.01), digits=7, ...)

Arguments

Details

The following metrics are returned: "ci" for the concordance index at the prognostic time pro.time, "bs" for the Brier score at the prognostic time pro.time, "ibs" for the integrated Brier score up to the last observed time of event, "ibll" for the integrated binomial log-likelihood up to the last observed time of event, "bll" for the binomial Log-likelihood, "ribs" for the restricted Integrated Brier score up to the prognostic time pro.time, "ribll" for the restricted integrated binomial log-likelihood up to the last observed time of event, "bll" for the binomial log-likelihood, and "auc" for the area under the time-dependent ROC curve up to the prognostic time pro.time.

Value

No return value for this S3 method.

See Also

survivalSL.

Examples

data(dataDIVAT2)

dataDIVAT2$train <- 1*rbinom(n=dim(dataDIVAT2)[1], size = 1, prob=1/2)

# The training of the super learner with 2 algorithms from the
   # first 100 patients of the training sample
sl1 <- survivalSL(method=c("LIB_AFTgamma", "LIB_PHgompertz"),  metric="auc",
  data=dataDIVAT2[dataDIVAT2$train==1,][1:100,],  times="times", failures="failures",
  pro.time = 12,  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"),
  cv=3)

# The prognostic capacities from the same training sample
summary(sl1)

Description

This function allows to compute a Super Learner (SL) to predict survival outcomes.

Usage

survivalSL(methods, metric="ci",  data, times, failures, group=NULL,
cov.quanti=NULL, cov.quali=NULL, cv=10, param.tune=NULL, pro.time=NULL,
optim.local.min=FALSE, ROC.precision=seq(.01,.99,.01),
param.weights.fix=NULL, param.weights.init=NULL,
keep.predictions=TRUE, progress=TRUE)

Arguments

Details

Each object of the list declared in param.tune must have the same name than the names of the methods included in the SL. If param.tune = NULL, the tunning parameters of each algorithm are estimated by cv-fold cross-validation. Otherwise, the user can propose a tunning grid for each method, as explained in the following table. The following metrics can be used: "ci" for the concordance index at the prognostic time pro.time, "bs" for the Brier score at the prognostic time pro.time, "loglik" for the log-likelihood, "ibs" for the integrated Brier score up to the last observed time of event, "ibll" for the Integrated binomial log-likelihood up to the last observed time of event, "bll" for the binomial log-likelihood, "ribs" for the restricted integrated Brier score up to the prognostic time pro.time, "ribll" for the restricted integrated binomial log-likelihood up to the last observed time of event, "bll" for the binomial log-likelihood, and "auc" for the area under the time-dependent ROC curve up to the prognostic time pro.time.

The following learners are available:

The following loss functions for the estimation of the super learner weigths are available (metric):

• Area under the ROC curve ("auc")

• Concordance index ("ci")

• Brier score ("bs")

• Binomial log-likelihood ("bll")

• Integrated Brier score ("ibs")

• Integrated binomial log-likelihood ("ibll")

• Restricted integrated Brier score ("ribs")

• Restricted integrated binomial log-Likelihood ("ribll")

Value

References

Polley E and van der Laanet M. Super Learner In Prediction. http://biostats.bepress.com. 2010.

Examples

data(dataDIVAT2)

# The Super Learner based from the first 250 individuals of the data base
sl1 <- survivalSL(methods=c("LIB_AFTgamma", "LIB_PHgompertz"),  metric="ci",
  data=dataDIVAT2[1:250,],  times="times", failures="failures", group="ecd",
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant"), cv=5)

# Individual prediction
pred <- predict(sl1, newdata=data.frame(age=c(52,52), hla=c(0,1),
retransplant=c(1,1), ecd=c(0,1)))

plot(y=pred$predictions$sl[1,], x=pred$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

lines(y=pred$predictions$sl[2,], x=pred$times, col=2, type="l", lty=1, lwd=2)

legend("topright", col=c(1,2), lty=1, lwd=2, c("Subject #1", "Subject #2"))

Description

This function finds the model which minimize the AIC of a Cox PH model.

Usage

tuneCOXaic(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL,
data, model.min=NULL, model.max=NULL)

Arguments

Details

The function runs the stepAIC function of the MASS package for covariates' selection.

Value

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

Examples

data(dataDIVAT2)

tune.model <- tuneCOXaic(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"))

tune.model$optimal$final.model # the covariate in the model with the best AIC

# The estimation of the training model with the corresponding lambda value
model <- LIB_COXaic(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"),
  final.model=tune.model$optimal$final.model)

# The resulted predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

Description

This function finds the optimal lambda and alpha parameters for an elastic net Cox regression.

Usage

tuneCOXen(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL,
data, cv=10, parallel=FALSE, alpha, lambda)

Arguments

Details

The function runs the cv.glmnet function of the glmnet package.

Value

References

Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5), 1-13, https://www.jstatsoft.org/v39/i05/

Examples

data(dataDIVAT2)

tune.model <- tuneCOXen(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"), cv=5,
  alpha=seq(.1, 1, by=.1), lambda=seq(.1, 1, by=.1))

tune.model$optimal$lambda # the estimated lambda value

# The estimation of the training modelwith the corresponding lambda value
model <- LIB_COXen(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"),
  alpha=tune.model$optimal$alpha,
  lambda=tune.model$optimal$lambda)

# The resulted predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

Description

This function finds the optimal lambda parameter for a Lasso Cox regression.

Usage

tuneCOXlasso(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL,
data, cv=10, parallel=FALSE, lambda)

Arguments

Details

The function runs the cv.glmnet function of the glmnet package.

Value

References

Simon et al. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5), 1-13, https://www.jstatsoft.org/v39/i05/

Examples

data(dataDIVAT2)

tune.model <- tuneCOXlasso(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"),
  cv=5, lambda=seq(0, 10, by=.1))

tune.model$optimal$lambda # the estimated lambda value

# The estimation of the training modelwith the corresponding lambda value
model <- LIB_COXlasso(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"),
  lambda=tune.model$optimal$lambda)

# The resulted predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

Description

This function finds the optimal lambda parameter for a ridge Cox regression.

Usage

tuneCOXridge(times, failures, group=NULL, cov.quanti=NULL,
cov.quali=NULL, data, cv=10, parallel=FALSE, lambda)

Arguments

Details

The function runs the cv.glmnet function of the glmnet package.

Value

References

Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5), 1-13, https://www.jstatsoft.org/v39/i05/

Examples

data(dataDIVAT2)

tune.model <- tuneCOXridge(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"),
  cv=5, lambda=seq(0, 10, by=.1))

tune.model$optimal$lambda # the estimated lambda value

# The estimation of the training modelwith the corresponding lambda value
model <- LIB_COXridge(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"),
  lambda=tune.model$optimal$lambda)

# The resulted predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

Description

This function finds the optimal number of knots of the spline function.

Usage

tunePHspline(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL,
data, cv=10, k)

Arguments

Details

The function runs the flexsurvspline function of the flexsurv package. The metric used in the cross-validation is the C-index.

Value

References

Royston, P. and Parmar, M. (2002). Flexible parametric proportional-hazards and proportional odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Statistics in Medicine 21(1):2175-2197. doi: 10.1002/sim.1203

Examples

data(dataDIVAT2)

# The estimation of the hyperparameters on the first 150 patients

tune.model <- tunePHspline(times="times", failures="failures", data=dataDIVAT2[1:150,],
    cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"),
    cv=3, k=1:2)

# the estimated nodesize value

 tune.model$optimal
 tune.model$results

Description

This function finds the optimal inter, size, decay, maxit, and MaxNWts parameters for the survival neural network by using cross-validation and the concordance index.

Usage

tunePLANN(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL,
data, cv=10, inter, size, decay, maxit, MaxNWts)

Arguments

Details

This function is based is based on the survivalPLANN package.

Value

References

Biganzoli E, Boracchi P, Mariani L, and et al. Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach. Stat Med, 17:1169-86, 1998.

Examples

data(dataDIVAT2)

# The hyper-parameter grid needs to be more precise and the maximum number
# of iterations > 1000. We have reduced the arguments to respect examples requiring
# less than 5 seconds for packages on the CRAN.

tune.model <- tunePLANN(times="times", failures="failures", data=dataDIVAT2[1:300,],
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"), cv=3,
  inter=1, size=c(16, 32), decay=0.01, maxit=50, MaxNWts=10000)

tune.model$optimal # the optimal hyperparameters

tune.model$results # the C-index for the tested grid

Description

This function finds the optimal nodesize, mtry, and ntree parameters for a survival random forest tree.

Usage

tuneRSF(times, failures, group=NULL, cov.quanti=NULL,
cov.quali=NULL, data, nodesize, mtry, ntree)

Arguments

Details

The function runs the tune.rfsrc function of the randomForestSRC package.

Value

References

Ishwaran H. and Kogalur U.B. (2007). Random survival forests for R, Rnews, 7(2):25-31.

Examples

data(dataDIVAT2)

tune.model <- tuneRSF(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"),
  nodesize=c(100, 250, 500), mtry=1, ntree=100)

tune.model$optimal # the estimated nodesize value

# The estimation of the training modelwith the corresponding lambda value
model <- LIB_RSF(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"),
  nodesize=tune.model$optimal$nodesize, mtry=1, ntree=100)

# The resulted predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

Description

This function finds the optimal n.nodes, decay, batch.size, and epochs parameters for a survival neural network.

Usage

tuneSNN(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL,
data, cv=10, n.nodes, decay, batch.size, epochs)

Arguments

Details

This function is based is based on the deepsurv from the survivalmodels package. You need to call Python using reticulate. In order to use it, the required Python packages must be installed with reticulate::py_install. Therefore, before running the present LIB_SNN function, you must install and call for the reticulate and survivalmodels packages, and install pycox by using the following command: install_pycox(pip = TRUE, install_torch = FALSE). The survivalSL package functions without these supplementary installations if this learner is not included in the library.

Value

References

Katzman et al. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Medical Research Methodology, 18(1), 24. 1018.

https://doi.org/10.1186/s12874-018-0482-1