PBIR (version 0.1-0)

PBIR1: Estimate the PBIR curve over a time window

Description

Estimate the PBIR curve over a time window

Usage

PBIR1(
  t2PROGRESSION,
  STATUS_PROGRESSION,
  t2RESPONSE,
  STATUS_RESPONSE,
  time = NULL,
  alpha = 0.95
)

Arguments

t2PROGRESSION

time to progression/death or censoring

STATUS_PROGRESSION

binary indicator for progression status: 1 for progression/death; 0 for censoring

t2RESPONSE

time to response or censoring

STATUS_RESPONSE

binary indicator for response status: 1 for response; 0 for censoring

time

user-selected time points at which the PBIR is estimated; the default value is "NULL" and the PBIR will be estimated at all observed time points

alpha

coverage level of the point-wise confidence interval for PBIR curve; the default value is 0.95

Value

a data matrix containing "time", "PBIR estimates", "standard errors of PBIR estimates", "confidence intervals of the PBIR"

References

Huang, B., Tian, L., Talukder, E., Rothenberg, M., Kim, DY., and Wei, LJ. (2018) Evaluating Treatment Effect Based on Duration of Response for a Comparative Oncology Study. JAMA Oncol, doi: 10.1001/jamaoncol.2018.0275

Huang, B., Tian, L., McCaw, Z., Luo, Talukder, E., X., Rothenberg, M., Xie, W., Choueiri, T., Kim, DY., & Wei, LJ. (2020). Analysis of Response Data for Assessing Treatment Effects in Comparative Clinical Studies. Ann Intern Med, doi: 10.7326/M20-0104.

Examples

# NOT RUN {
library(survival)
n=100
set.seed(10)

# Generate the data

trt=rbinom(n, 1, 0.5)
error=rnorm(n)
tr=exp(rnorm(n)+error-trt*0.5+0.5)
tp=exp(rnorm(n)+error+trt*0.25)
tr[tp<tr]=Inf
tc=runif(n, 3, 8.5)
t2response=pmin(tr, tc)
delta_response=1*(tr<tc)
t2progression=pmin(tp, tc)
delta_progression=1*(tp<tc)

# Estimate the PBIR in two groups

fit1=PBIR1(t2PROGRESSION=t2progression[trt==1],
           STATUS_PROGRESSION=delta_progression[trt==1],
           t2RESPONSE=t2response[trt==1],
           STATUS_RESPONSE=delta_response[trt==1])

fit0=PBIR1(t2PROGRESSION=t2progression[trt==0],
           STATUS_PROGRESSION=delta_progression[trt==0],
           t2RESPONSE=t2response[trt==0],
           STATUS_RESPONSE=delta_response[trt==0])


# Plot the estimated PBIR by group

tt1=c(0, fit1$time)
PBIR1=c(0, fit1$PBIR)
B1=length(tt1)
tt1=rep(tt1, rep(2, B1))[-1]
PBIR1=rep(PBIR1, rep(2, B1))[-(2*B1)]
tt0=c(0, fit0$time)
PBIR0=c(0, fit0$PBIR)
B0=length(tt0)
tt0=rep(tt0, rep(2, B0))[-1]
PBIR0=rep(PBIR0, rep(2, B0))[-(2*B0)]
plot(range(c(fit1$time, fit0$time)), range(c(fit1$PBIR, fit0$PBIR)),
     xlab="time",  ylab="PBIR",
     main="black: group 0; red: group 1", type="n")
lines(tt0, PBIR0, col=1)
lines(tt1, PBIR1, col=2)
# }