pbf2: prob being in response function

In PBRF: The Probability of Being in Response Function and Its Variance Estimates

Description

Calculate the probability of being in response function (PBRF) and the variances

Usage

  pbf2(y1,y2,d1,d2,times=y2[order(y2)])

Arguments

y1	a numeric vector of event times denoting the minimum of event times T_1, T_2 and censoring time C, where T_2 corresponds to the event time, T_1 corresponds to the response time.
y2	a numeric vector of event times denoting the minimum of event time T_2 and censoring time C. Clearly, y2 is not smaller than y1.
d1	a numeric vector of event indicators with 1 denoting the response is observed and 0 denoting otherwise.
d2	a numeric vector of event indicators with 1 denoting the event is observed and 0 denoting otherwise.
times	a numeric vector of timepoints at which we want to estimate the PBRF

Details

There three methods to estimate PBRF: the subtraction, the division and the Semi-Markov methods are presented in Tsai et al. (2017). There are two sub-methods for division and the Semi-Markov methods when the censoring distrbution is estimated in two different ways (looking at y1=min(T_1,T_2,C) and at y2=min(T_2,C)). So there are 5 methods in total reported. Method 1: division and based on y1; Method 2: division and based on y2; Method 3: Semi-Markov and based on y1; Method 4: Semi-Markov and based on y2; Method 5: subtraction. The methods based on y2 perform better than the corresponding ones based on y1.

Value

pbrf	The estimates at each timepoints (row) and by methods 1-5 (column)
vpbrf	The variance estimates at each timepoints (row) and by methods 1-5 (column)

Author(s)

Xiaodong Luo

References

Tsai W.Y., Luo X., Crowley J. (2017) The Probability of Being in Response Function and Its Applications. In: Matsui S., Crowley J. (eds) Frontiers of Biostatistical Methods and Applications in Clinical Oncology. Springer, Singapore. <doi: 10.1007/978-981-10-0126-0_10>.

Examples

  n<-300
  rho<-0.5
  lambda10<-0.1;lambda20<-0.08;lambdac0<-0.09
  lam1<-rep(0,n);lam2<-rep(0,n);lamc<-rep(0,n)
  z<-rep(0,n)
  z[1:(n/2)]<-1
  lam1<-lambda10
  lam2<-lambda20
  lamc<-lambdac0
  tem<-matrix(0,ncol=3,nrow=n)
  
  y2y<-matrix(0,nrow=n,ncol=3)
  y2y[,1]<-rnorm(n);y2y[,3]<-rnorm(n)
  y2y[,2]<-rho*y2y[,1]+sqrt(1-rho^2)*y2y[,3]
  tem[,1]<--log(1-pnorm(y2y[,1]))/lam1
  tem[,2]<--log(1-pnorm(y2y[,2]))/lam2
  tem[,3]<--log(1-runif(n))/lamc
  
  y1<-apply(tem,1,min)
  y2<-apply(tem[,2:3],1,min)
  d1<-as.numeric(tem[,1]<=y1)
  d2<-as.numeric(tem[,2]<=y2)
  btemp<-pbf2(y1,y2,d1,d2,times=c(1,3,5))
  btemp

PBRF documentation built on May 2, 2019, 1:26 p.m.