tunePHspline {survivalSL}

Tune a Survival Regression using the Royston/Parmar Spline Model

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

times

The name of the variable related the numeric vector with the follow-up times.

failures

The name of the variable related the numeric vector with the event indicators (0=right censored, 1=event).

group

The name of the variable related to the exposure/treatment. This variable shall have only two modalities encoded 0 for the untreated/unexposed patients and 1 for the treated/exposed ones. The default value is NULL: no specific exposure/treatment is considered. When a specific exposure/treatment is considered, it will be forced in the algorithm or related interactions will be tested when possible.

cov.quanti

The name(s) of the variable(s) related to the possible quantitative covariates. These variables must be numeric.

cov.quali

The name(s) of the variable(s) related to the possible qualitative covariates. These variables must be numeric with two levels: 0 and 1. A complete disjunctive form must be used for covariates with more levels.

data

A data frame for training the model in which to look for the variables related to the status of the follow-up time (times), the event (failures), the optional treatment/exposure (group) and the covariables included in the previous model (cov.quanti and cov.quali).

cv

The value of the number of folds. The default value is 10.

k

The number of knots optimized over.

Details

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

Value

optimal

The value of k that gives the maximum mean cross-validated C-index.

results

The data frame with the mean cross-validated C-index according to k.

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

tune.model <- tunePHspline(times="times", failures="failures", data=dataDIVAT2[1:200,],
    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

# The estimation of the training model with the corresponding lambda value

 model <- LIB_PHspline(times="times", failures="failures", data=dataDIVAT2[1:200,],
   cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"),
   k=tune.model$optimal$k)

# 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))