FitDat {OneArm2stage}

Fit Historical Survival Data Assuming the Failure Time Follows a Weibull Distribution

Description

The function fits parametric models for the time-to-event data with the underlying distribution of the failure time assumed to be Weibull.

Usage

FitDat(data)

Arguments

data

a historical survival data sample, has to contain two variables 'Time' and 'Cens':
Time, the time under observation during trial for each patient.
Cens, the status indicator of patients (event = 1, censored = 0).

Value

fit.Weibull the fitted model assuming a Weibull distribution.
AIC the AIC value from the fitted model.
parameter.estimates the estimated parameters from the fitted model.

References

Wang, M., Rule, S., Zinzani, P. L., Goy, A., Casasnovas, O., Smith, S. D.,..., Robak, T. (2018). Acalabrutinib in relapsed or refractory mantle cell lymphoma (ACE-LY-004): a single-arm, multicentre, phase 2 trial. The Lancet, 391(10121), 659–667. https://doi.org/10.1016/s0140-6736(17)33108-2

Examples

library(IPDfromKM)
# a sample dataset that we already extracted from Wang et al, 2018.
df<- read.csv(system.file("extdata", "df.csv", package = "OneArm2stage"))

# risk time points
trisk <- c(0,2,4,6,8,10,12,14,16,18,20,22,24)

# number of patients at risk at each risk time point
nrisk.radio <- c(124,120,115,110,107,104,103,95,46,18,11,8,0)

# Preprocess the raw coordinates into an proper format for reconstruct IPD
pre_radio <- preprocess(dat=df, trisk=trisk,
                     nrisk=nrisk.radio,totalpts=NULL,maxy=100)

#Reconstruct IPD
est_radio <- getIPD(prep=pre_radio,armID=0,tot.events=NULL)

# shift the IPD data into the proper format for 'FitDat()'
ipd <- est_radio$IPD
dat3 <- as.data.frame(cbind(rep(0, nrow(ipd)),ipd$time, ipd$status))
colnames(dat3) <- c("Entry", "Time", "Cens")

# use FitDat function to fit the historical dat
modelSelect <- FitDat(dat3)
modelSelect$AIC
# Weibull
# 301.7776

# check the estimated parameters from the modeling results
modelSelect$parameter.estimates
# $Weibull
# shape     scale
# 0.1133671 3.9939753