run_simulation_simsurv {TwoArmSurvSim}

Run Clinical Trial Simulations Based on survival data generated by simsurv package

Description

Runs single or mutiple clinical trial (Time to event endpoint) simulations based survival time generated by simsurv package. Trial data summary will be provied for each simulation. Cox model will be fitted afther trial simulation. If stratification factors were provided, stratified cox model results will also be provided. If "N_simulation" is set to 1, one simulation dataset will be generated.

Usage

run_simulation_simsurv(samplesize, rand_ratio=c(1,1), blocksize, factors=NULL, 
accrual_interval=NULL,accrual_rate=NULL,  eventtarget=NULL,maxlpfollowup=NULL, 
N_simulation=1,alpha=0.05,simsurv1=NULL, simsurv2=NULL)

  

Arguments

samplesize

Total number of patients in the simulated clinical trial

rand_ratio

Randomization ratio between control and treatment

blocksize

The value of this parameter is used to define the size of the randomizaiton blocks. The actual blocksize is number of treatment levels mutipled by this parameter. Please refer to "blockrand" package for detailed usage.

factors

stratification factors. Default is NULL

accrual_interval

Time windows for accrual

accrual_rate

accrual rate for each accrual time window

eventtarget

Number of target events

maxlpfollowup

maximum follow up time for the last enrolled patient

N_simulation

number of simulations to run

alpha

Two sided alpha for testing power calculation

simsurv1

simsurv command to generate survival time. Design matrix should set to "x". Please refer to examples.

simsurv2

simsurv command to gendrate dropout time.

Value

TrilInfo

Summary of the simulated trial data

ModelResult

Cox model results comparing treatment vs control

StraModelResult

Stratified Cox model results comparing treatment vs control

Data

simulated dataset only if "N_simulateion" is set to 1

Examples

# Example 1, compare simsurv and TwoArmSurvSim, trtHR=0.7 eventtarget=247, power should be 0.8 

f1<-list(name='Gender', N_level=2, prevalence=c(0.5,0.5), HR=c(1,0.9), strata=TRUE)
factors=list(f1)


samplesize = 400
blocksize = 2
accrual_interval = c(0,5,10)
accrual_rate = c(5,10,20)
eventtarget = 247
N_simulation = 1


# Simsurv

simsurv1 <- "simsurv(lambdas = 0.03, gammas = 1, 
betas = c(trt = log(0.7),Gender.1=log(0.9)),x = x)"
simsurv2 <-NULL

out<-run_simulation_simsurv(samplesize=samplesize,blocksize=blocksize,factors=factors,
accrual_interval=accrual_interval,accrual_rate=accrual_rate, eventtarget=eventtarget,
N_simulation=N_simulation,simsurv1=simsurv1,simsurv2=simsurv2)

# example 2,  Time dependent treatment effect. 
# h(t)=h0(t)*exp(beta0*x+beta1*x*log(t)).  beta0=log(0.7), beta1=0.15

simsurv1 <- "simsurv( lambdas = 0.1, gammas = 1.5,betas = c(trt = log(0.7)),
x = x, tde = c(trt = 0.15),tdefunction = \"log\")"
simsurv2 <-NULL