tuneCOXaic {survivalSL}

Tune a Cox Model with a Forward Selection Based on the AIC

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

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).

model.min

An optional argument with the minimal set of covariates.

model.max

An optional argument with the maximal set of covariates.

Details

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

Value

optimal

The names of covariate to adjuste the fit.

results

The result of the stepAIC process.

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))