Perform analysis on the data of one-arm clinical trial with time-to-event endpoint

Description

This function can get analysis results on the input trial data using several approaches for one-arm trial design with time-to-event endpoint. Default approaches include one-sample log-rank test, Landmark Kaplen-Meier method and binary method which regards the survival of each subject at a landmark is a binary variable. In addition, if RWdata is not NULL, the RWdata input will be used as an external control and cox model will be used to evaluate the treatment effect of input trial data (experimental arm) compared with the external control.

Usage

OneArmTTEAnalysis(
  data,
  eventRates.ctrl,
  landmark,
  RWdata = NULL,
  RWSurvCal = FALSE,
  conf.type = "plain",
  alpha = 0.05
)

Arguments

Details

This function outputs a list of analysis results of each design, including: 1) p-value of one-sample log-rank test, 2) historical survival rate at landmark, 3) survival rate estimate with confidence interval of landmark kaplan-meier method, 4) survival rate estimate with confidence interval of binary method, 5) p-value of binary method, 6) hazard ratio estimate with confidence interval compared with real world data (if available), 7) p-value of log-rank test compared with real world data (if available).

Value

No visible return values.

Examples

library(survival)
data(example_data)
# Piecewise exponential of historical control
median.ctrl <- c(14.3, 1.5, 4.9)
eventRates.ctrl <- tibble::tibble(duration=c(4,2,100),rate=log(2)/median.ctrl)
OneArmTTEAnalysis(example_data, eventRates.ctrl, landmark=6)

Get operating characteristics of one-arm clinical trial design with time-to-event endpoint

Description

Using simulation, this function can get operating characterisitics of several approaches for one-arm trial design with time-to-event endpoint. Default approaches include one-sample log-rank test, Landmark Kaplen-Meier method and binary method which regards the survival of each subject at a landmark is a binary variable. In addition, if RWdata is not NULL, the RWdata input will be used as an external control and cox model will be used to evaluate the treatment effect of simulated data (experimental arm) compared with the external control. The output includes probability of rejecting null hypothesis of each design, average number of events at analysis, and average analysis time after last patient in. When eventRates is same as eventRates.ctrl, the probability of rejecting null hypothesis is type I error; When eventRates is the alternative hypothesis from desirable treatment effect, the probability of rejecting null hypothesis is power.

Usage

OneArmTTEDesign(
  n,
  eventRates.ctrl,
  eventRates,
  enrollRates,
  dropoutRates,
  cutTime,
  landmark,
  Event = FALSE,
  n.event,
  RWdata = NULL,
  RWSurvCal = FALSE,
  conf.type = "plain",
  alpha = 0.05,
  nsim = 10000,
  seed = 43
)

Arguments

Details

The function output a list of the operating characteristics including: 1) probability of rejecting null hypothesis of each design, 2) average number of events at analysis, 3) average analysis time after last patient in.

Value

No visible return values.

Examples

library(survival)
# Piecewise exponential of historical control
median.ctrl <- c(14.3, 1.5, 4.9)
eventRates.ctrl <- tibble::tibble(duration=c(4,2,100),rate=log(2)/median.ctrl)
# Piecewise exponential assumption of treatment:
# Hazard ratio = 1 for time 0-3 and Hazard ratio = 0.47 after
eventRates.trt = tibble::tibble(duration=c(3,1,2,100),rate=log(2)/c(14.3, median.ctrl/0.47))
# Constant enrollment rates and dropout rates
enrollRates = tibble::tibble(duration=106, rate=14/3)
dropoutRates = tibble::tibble(duration=106, rate=0.2/12)
OneArmTTEDesign(n=40, eventRates.ctrl, eventRates.trt, enrollRates, dropoutRates, cutTime=3,
                landmark=6, Event=FALSE, conf.type = 'plain', alpha=0.05, nsim=100, seed=43)

Get Cumulative Hazard at a Landmark Timepoint

Description

Get Cumulative Hazard at a Landmark Timepoint

Usage

cumhazard(
  eventRates = tibble::tibble(duration = c(3, 100), rate = c(log(2)/5, log(2)/5 * 0.5)),
  landmark
)

Arguments

Value

A numeric which is the cumulative hazard at a landmark timepoint.

Examples

# Piecewise exponential event rates of 0.5 for time 0-3, 0.4 for time 3-6, and 0.5 after
cumhaz <- cumhazard(eventRates=tibble::tibble(duration = c(3,3,100),rate = c(0.5, 0.4, 0.3)),
                    landmark=12)

An Example Trial Dataset

Description

A dataset containing the time-to-event information. The variables are as follows

Usage

data(example_data)

Format

A data frame with 3 variables and 200 observations

Details

• time. event or censoring time

• censor. censoring indicator; 1=event

• Trt. treatment arm name

Generate Piecewise Exponential Enrollment

Description

Generate Piecewise Exponential Enrollment

Usage

rpwenroll(n = NULL, enrollRates = tibble(duration = c(1, 2), rate = c(2, 5)))

Arguments

Value

A vector of random enrollment times following piecewise exponential distribution.

Examples

# piecewise uniform (piecewise exponential inter-arrival times) for 10k patients enrollment
# enrollment rates of 5 for time 0-100, 15 for 100-300, and 30 thereafter
x <- rpwenroll(n=10000, enrollRates=tibble::tibble(rate = c(5, 15, 30), duration = c(100,200,100)))
plot(x,1:10000,
     main="Piecewise uniform enrollment simulation",xlab="Time",
     ylab="Enrollment")
# exponential enrollment
x <- rpwenroll(10000, enrollRates=tibble::tibble(rate = .03, duration = 1))
plot(x,1:10000,main="Simulated exponential inter-arrival times",
     xlab="Time",ylab="Enrollment")

The Piecewise Exponential Distribution

Description

The piecewise exponential distribution allows a simple method to specify a distribtuion where the hazard rate changes over time. It is likely to be useful for conditions where failure rates change, but also for simulations where there may be a delayed treatment effect or a treatment effect that that is otherwise changing (e.g., decreasing) over time. rpwexp() is to support simulation of both the Lachin and Foulkes (1986) sample size method for (fixed trial duration) as well as the Kim and Tsiatis(1990) method (fixed enrollment rates and either fixed enrollment duration or fixed minimum follow-up).

Usage

rpwexp(n = 100, failRates = tibble(duration = c(1, 1), rate = c(10, 20)))

Arguments

Details

Using the cumulative=TRUE option, enrollment times that piecewise constant over time can be generated.

Value

A vector of random event times following piecewise exponential distribution.

Examples

# get 10k piecewise exponential failure times
# failure rates are 1 for time 0-.5, 3 for time .5 - 1 and 10 for >1.
# intervals specifies duration of each failure rate interval
# with the final interval running to infinity
x <- rpwexp(10000, failRates=tibble::tibble(rate = c(1, 3, 10), duration = c(.5,.5,1)))
plot(sort(x),(10000:1)/10001,log="y", main="PW Exponential simulated survival curve",
xlab="Time",ylab="P{Survival}")
# exponential failure times
x <- rpwexp(10000, failRates=tibble::tibble(rate = 5, duration=1))

plot(sort(x),(10000:1)/10001,log="y", main="Exponential simulated survival curve",
     xlab="Time",ylab="P{Survival}")