OneArmTTE (version 1.0)

OneArmTTEAnalysis: 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
)

Value

No visible return values.

Arguments

data

Trial data. A tibble/data.frame containing time and censor, where censor=1 indicates event.

eventRates.ctrl

Event rates of historical control.

landmark

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

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; 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.

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).

Examples

# \donttest{
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)
# }