TwoArmSurvSim (version 0.2)

run_simulation: Run Clinical Trial Simulations Based on User Defined Trial Settings

Description

Runs single or mutiple clinical trial (Time to event endpoint) simulations based on the clinical trial settings. 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(samplesize, rand_ratio=c(1,1), blocksize, factors=NULL,trtHR=trtHR, 
trt_timeinterval=NULL, accrual_interval=NULL, accrual_rate=NULL, rampuptime=NULL,
acceleration=NULL, lambda, gamma, timeinterval=NULL, dropoutrate=0,gammac=1,
censordist='exponential', eventtarget=NULL,maxlpfollowup=NULL, N_simulation=1,
alpha=0.05)

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

trtHR

Hazard ratio between treatment groups (treatment vs control)

trt_timeinterval

Time windows for trtHR when trtHR is piecewise. Always start with time 0. Example: c(0,10,30)

accrual_interval

Time windows for accrual

accrual_rate

accrual rate for each accrual time window

rampuptime

rampup time for linear increased accrual

acceleration

acceleration rate for linear increased accrual

lambda

lambda for event hazard function

gamma

gamma for event hazard function

timeinterval

time intervals for piecewise baseline hazard function

dropoutrate

Patient dropout rate with range [0,1). If dropoutrate contains only one number. The program will control the dropout rate at population level(treatment + control). If dropoutrate contains two numbers (ie. c(0.2,0.1)), the program will control the dropout rate of control and treatment arm seperately, with the first dropout rate number for control and the second number for treatment. Default value is "0" (no dropout)

gammac

gamma for censor hazard function. Default is 1 (exponential)

censordist

censor hazard distribution. Can be "weibull", "exponential" or "uniform". Default is exponential

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

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

# NOT RUN {

f1<-list(name='Region', N_level=3, prevalence=c(0.1,0.2,0.7), HR=c(1,0.7,0.9), strata=TRUE)
f2<-list(name='Gender', N_level=2, prevalence=c(0.5,0.5), HR=c(1,0.9), strata=TRUE)
f3<-list(name='Stage', N_level=4, prevalence=c(0.2,0.25,0.3,0.25), HR=c(1,1.05,1.3,1.5),
strata=TRUE)

factors<-list(f1,f2,f3)

samplesize<-400
blocksize<-2
accrual_interval<-c(0,5,10)
accrual_rate<-c(5,10,20)
trtHR<-0.7
lambda<-0.03
gamma<-1.2
dropoutrate<-0.2
eventtarget<-240
N_simulation<-10


out<-run_simulation(samplesize=samplesize,blocksize=blocksize,factors=factors,
accrual_interval=accrual_interval,accrual_rate=accrual_rate, trtHR=trtHR, lambda=lambda,
gamma=gamma,dropoutrate=dropoutrate,eventtarget=eventtarget,N_simulation=N_simulation)


# }