PBIR (version 0.1-0)

CRR: Estimate cumulative response rates (CRR) and test their equality between two groups

Description

Estimate cumulative response rates (CRR) and test their equality between two groups

Usage

CRR(
  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

binary indicator for treatment assignment: 1 for treatment arm and 0 for control arm

time

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

alpha

coverage level of the point-wise confidence interval for the cumulative response rate; the default value is 0.95

Value

A list with following elements

• result0: a data matrix containing "time", "CRR estimates (group 0)", "standard error of CRR estimates (group 0)", "confidence interval of CRR (group 0)"

• result1: a data matrix containing "time", "CRR estimates (group 1)", "standard error of CRR estimates (group 1)", "confidence interval of CRR (group 1)"

• pvalue: the p-value from two group comparison

References

Gray, RJ. (1988) A class of K-sample tests for comparing the cumulative incidence of a competing risk, ANNALS OF STATISTICS, 16:1141-1154.

Aalen, O. (1978) Nonparametric estimation of partial transition probabilities in multiple decrement models, ANNALS OF STATISTICS, 6:534-545.

Examples

# NOT RUN {
library(cmprsk)
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

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

fit

# Plot the estimated PBIR by group

tt1=c(0, fit$result1$time)
CRR1=c(0, fit$result1$CRR)
B1=length(tt1)
tt1=rep(tt1, rep(2, B1))[-1]
CRR1=rep(CRR1, rep(2, B1))[-(2*B1)]
tt0=c(0, fit$result0$time)
CRR0=c(0, fit$result0$CRR)
B0=length(tt0)
tt0=rep(tt0, rep(2, B0))[-1]
CRR0=rep(CRR0, rep(2, B0))[-(2*B0)]
plot(range(c(fit$result1$time, fit$result0$time)),
     range(c(fit$result1$CRR, fit$result0$CRR)),
     xlab="time",  ylab="CRR",
     main="black: group 0; red: group 1", type="n")
lines(tt0, CRR0, col=1)
lines(tt1, CRR1, col=2)
# }