PBIR (version 0.1-0)

PBIR2: Estimate and compare PBIR curves from two groups over a time window

Description

Estimate and compare PBIR curves from two groups over a time window

Usage

PBIR2(
  t2PROGRESSION,
  STATUS_PROGRESSION,
  t2RESPONSE,
  STATUS_RESPONSE,
  TRT,
  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

TRT

treatment indicator: 1 for treatment arm; 0 for control arm

time

user-selected time points at which PBIRs are to be compared; the default value is "NULL" and PBIRs at all observed time points are compared

alpha

coverage level of the point-wise confidence interval for the difference in the PBIR, the default value is 0.95

Value

a data matrix containing "time", "estimated differences in PBIR (treatment-control)", "standard errors of estimated PBIR differences", "confidence intervals of the PBIR difference"

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=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 difference in PBIR
# the analysis is truncated at time 8, which is slightly smaller than the largest follow-up time

fit=PBIR2(t2PROGRESSION=t2progression,
          STATUS_PROGRESSION=delta_progression,
          t2RESPONSE=t2response,
          STATUS_RESPONSE=delta_response,
          TRT=trt)


# Plot the estimated differnece in PBIR

tt=fit$time
diff=fit$diff
low=fit$ci.low
up=fit$ci.up

tt=c(0, tt)
diff=c(0, diff)
low=c(0, low)
up=c(0, up)
B=length(tt)

tt=rep(tt, rep(2, B))[-1]
diff=rep(diff, rep(2, B))[-(2*B)]
low=rep(low, rep(2, B))[-(2*B)]
up=rep(up, rep(2, B))[-(2*B)]

plot(range(c(fit$time, 0)), range(c(low, up)),
     xlab="time", ylab="difference in PBIR",
     lwd=2, type="n")
lines(tt, diff, lwd=2, col=3)
lines(tt, low,  col=2)
lines(tt, up, col=2)
lines(range(fit$time), rep(0, 2), col=4, lty=4)
# }