ProjectionCPE {SurvEval}

Projecton-based Approach to Estimate the CPE from Nested Survival Models.

Description

Using a projection-based approach, the concordance probability estimate (CPE) for nested Cox proportional hazards regression models is computed. Under this approach, the proportional hazards assumption is satisfied for the model containing both the standard covariates and the new covariates under investigation.

Usage

ProjectionCPE(Time, Event,StandardMarkers,NewMarkers, tau, Block=TRUE)
 

Arguments

Time

Survival Time

Event

Event Status

StandardMarkers

A vector or matrix of standard covariates. Unique column names should be assigned to the matrix before running this function.

NewMarkers

A vector or matrix of new covariates.Unique column names should be assigned to the matrix before running this function.

tau

The follow-up duration for a given study. This should be no larger than the maximum observed failure time (uncensored).

Block

Due to the complexity of the calculation, this is an approximation that calculates the CPE using random blocks of 50 patients. Recommended for data sets larger than 150 individuals.

Details

This function implements the method described in Heller and Devlin (under review).

Value

an object with the following elements

projCPE

Projected CPE statistic using the standard markers.

References

Heller, Glenn, and Sean M. Devlin. Measuring the Impact of New Risk Factors Within Survival Models. Under Review.

Examples

SM0 = rnorm(300, 0,1)
SM1 = rnorm(300, 0,1)
NM0 = rnorm(300, 0,1)
NM1 = rnorm(300, 0,1)
  
S1 = exp(-1*(0.408*SM0 +0.15*SM1 + 0.684*NM0+0.15*NM1))*rweibull(300,scale = 1 , shape=1) 
C1 = runif(300, 0,1.58)
  
Time = pmin(S1, C1)
Event = 1*(S1 < C1)
 
StandardMarkers = cbind(SM0,SM1)
NewMarkers = cbind(NM0 ,NM1 )
tau = max(Time[Event==1])

ProjectionCPE(Time, Event,StandardMarkers,NewMarkers,  tau)