OneArmTTE (version 1.0)

OneArmTTEDesign: 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
)

Value

No visible return values.

Arguments

n

Number of subjects.

eventRates.ctrl

Event rates of historical control.

eventRates

Event rates of subjects in the trial.

enrollRates

Enrollment rates of subjects in the trial.

dropoutRates

Dropout rates of the subjects in the trial.

cutTime

Analysis time after last patient in; not used if Event=TRUE.

landmark

The landmark of interest to evaluate the survival rate for Landmark Kaplan-Meier method.

Event

Indicator of whether the analysis is driven by number of events; default is FALSE.

n.event

Number of events at analysis; not used if Event=FALSE.

RWdata

The real world data to be used as external control; A tibble/data.frame containing time and censor, where censor=1 indicates event; default is NULL.

RWSurvCal

Indicator of whether to calculate historical cumulative hazard and survival rate at landmark from real world data as the null case; default is FALSE.

conf.type

Type of confidence interval in the survival model; One of "none", "plain" (the default), "log", "log-log", "logit" or "arcsin".

alpha

Type I error rate level.

nsim

Number of simulations; default is 10000.

seed

Seed for simulation.

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.

Examples

# \donttest{
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)
# }