| Title: | Simulate Survival Data for Randomized Clinical Trials |
|---|---|
| Description: | A system to simulate clinical trials with time to event endpoints. Event simulation is based on Cox models allowing for covariates in addition to the treatment or group factor. Specific drop-out rates (separate from administrative censoring) can be controlled in the simulation. Other features include stratified randomization, non-proportional hazards, different accrual patterns, and event projection (timing to reach the target event) based on interim data. |
| Authors: | Bo Zhang [cre, aut], Yi Zhong [aut, ctb], Ginny Peng [aut, ctb], Bin Yao [aut, ctb] |
| Maintainer: | Bo Zhang <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.2 |
| Built: | 2024-12-03 06:36:54 UTC |
| Source: | CRAN |
censor event time
censor_surv(eventtime,censortime, x)censor_surv(eventtime,censortime, x)
eventtime |
vector of event times |
censortime |
vector of censor time |
x |
covariates matrix |
Censor events given a fixed dropout rate
censordata(simdata,lambda,gamma, dropoutrate,ebx=1,gammac=1,groupfreq=1, censordist='exponential',timeinterval=NULL,HRPW=FALSE)censordata(simdata,lambda,gamma, dropoutrate,ebx=1,gammac=1,groupfreq=1, censordist='exponential',timeinterval=NULL,HRPW=FALSE)
simdata |
Simulated event dataset |
lambda |
lambda for event hazard function |
gamma |
gamma for event 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) |
ebx |
exp(beta*x), if there's no covariates, ebx=1. |
groupfreq |
frequence of each level of ebx value |
timeinterval |
time intervals for piecewise baseline hazard function |
gammac |
gamma for censor hazard function. Default is 1 (exponential) |
censordist |
censor hazard distribution. Default is exponential |
HRPW |
Indicator of piecewise hazard ratios. TURE for piecewise. FALSE for non piecewise |
Calculate the combination frequency of several columns in a data frame.
column_freq(x,namelist,keepID=FALSE)column_freq(x,namelist,keepID=FALSE)
x |
Input data as a data frame. |
namelist |
A list of column names that need to be counted. |
keepID |
If TRUE, the output will keep the unique ID for the column combination. |
simulate covariates matrix based on user provided factor information
cov_simu(sample_size = sample_size,factors=factors)cov_simu(sample_size = sample_size,factors=factors)
sample_size |
Total number of patients |
factors |
A list contains basic information about the covariate factors. Each element should have factor's name, number of levels and their frequency, as well as the hazard ratio to the reference group. Factors should be categorical data. |
f1<-list(name='Region', N_level=3, prevalence=c(0.1,0.2,0.7), HR=c(1,1,1), strata=TRUE) f2<-list(name='Gender', N_level=2, prevalence=c(0.5,0.5), HR=c(1,0.9), strata=TRUE) factors<-list(f1,f2) cov_simu(sample_size=300,factors=factors)f1<-list(name='Region', N_level=3, prevalence=c(0.1,0.2,0.7), HR=c(1,1,1), strata=TRUE) f2<-list(name='Gender', N_level=2, prevalence=c(0.5,0.5), HR=c(1,0.9), strata=TRUE) factors<-list(f1,f2) cov_simu(sample_size=300,factors=factors)
Convert Categorical Data to Dummy Variables
dummy_convert(dataset,column_names)dummy_convert(dataset,column_names)
dataset |
Data frame that contains the categorical columns |
column_names |
A list of column names that need to be converted to dummy variables. |
x<-data.frame(trt=as.factor(rbinom(100,1,0.5)), Gender=as.factor(rbinom(100,1,0.5))) dummy_convert(x,c("trt","Gender"))x<-data.frame(trt=as.factor(rbinom(100,1,0.5)), Gender=as.factor(rbinom(100,1,0.5))) dummy_convert(x,c("trt","Gender"))
calculate censor hazard function parameter for a given dropout rate.
LambdaCensor(lambda=lambda,gamma=gamma,theta=theta,ebx=1,gammac=1, groupfreq=1,censordist='exponential',timeinterval=NULL)LambdaCensor(lambda=lambda,gamma=gamma,theta=theta,ebx=1,gammac=1, groupfreq=1,censordist='exponential',timeinterval=NULL)
lambda |
lambda for event hazard function |
gamma |
gamma for event hazard function |
gammac |
gamma for censor hazard function. This is required if the censoring hazard function is weibull |
theta |
Dropout rate |
ebx |
exp(beta*x), if there's no covariates, ebx=1. |
groupfreq |
frequence of each level of ebx value |
censordist |
censor hazard function distribution |
timeinterval |
time intervals for piecewise baseline hazard function |
Wan F. (2017) Simulating survival data with predefined censoring rates for proportional hazards models. Statist. Med. 2017; 36(5): 838-854
Martinez EZ, Achcar JA, de Oliveira Peres MV, de Queiroz JAM (2016) A brief note on the simulation of survival data with a desired percentage of right-censored data. Journal of Data Science . 2016, Vol. 14 Issue 4, p701-712. 12p
lambdac<-LambdaCensor(lambda=0.03,gamma=1,theta=0.2)lambdac<-LambdaCensor(lambda=0.03,gamma=1,theta=0.2)
calculate censor hazard function parameter for a given dropout rate. (for piecewise hazard ratios)
LambdaCensor_betapw(lambda=lambda,gamma=gamma,theta=theta,ebx=1, gammac=1,groupfreq=1,censordist='exponential',timeinterval=NULL)LambdaCensor_betapw(lambda=lambda,gamma=gamma,theta=theta,ebx=1, gammac=1,groupfreq=1,censordist='exponential',timeinterval=NULL)
lambda |
lambda for event hazard function |
gamma |
gamma for event hazard function |
gammac |
gamma for censor hazard function. This is required if the censoring hazard function is weibull |
theta |
Dropout rate |
ebx |
exp(beta*x), if there's no covariates, ebx=1. |
groupfreq |
frequence of each level of ebx value |
censordist |
censor hazard function distribution |
timeinterval |
time intervals for piecewise baseline hazard function |
Wan F. (2017) Simulating survival data with predefined censoring rates for proportional hazards models. Statist. Med. 2017; 36(5): 838-854
Martinez EZ, Achcar JA, de Oliveira Peres MV, de Queiroz JAM (2016) A brief note on the simulation of survival data with a desired percentage of right-censored data. Journal of Data Science . 2016, Vol. 14 Issue 4, p701-712. 12p
simulate accrual time where the accrual rate is linearly increased
linear_accrual(np,rampupt,acceleration)linear_accrual(np,rampupt,acceleration)
np |
Total number of patients |
rampupt |
The length of the ramp up period. |
acceleration |
The acceleration of the accrual rate (increase of each time unit). |
linear_accrual(np=200,rampupt=10,acceleration=5)linear_accrual(np=200,rampupt=10,acceleration=5)
objective function for the finding of censor hazard function parameter.
objfunction(x,lambda,gamma,gammac,theta,ebx,groupfreq,censordist,timeinterval)objfunction(x,lambda,gamma,gammac,theta,ebx,groupfreq,censordist,timeinterval)
x |
Censor hazard function parameter. For exponential or weibull censor hazard, x is lambda, for uniform hazard, x is the maximum time of the censoring window. |
lambda |
lamda for event hazard function |
gamma |
gamma for event hazard function |
gammac |
gamma for censor hazard function. THis is required if the censoring hazard function is weibull |
theta |
Dropout rate |
ebx |
exp(beta*x), if there's no covariates, ebx=1. |
groupfreq |
frequence of each level of ebx value |
censordist |
censor hazard function distribution |
timeinterval |
time intervals for piecewise baseline hazard function |
objective function for the finding of censor hazard function parameter.
objfunction_betapw(x,lambda,gamma,gammac,theta,ebx,groupfreq,censordist,timeinterval)objfunction_betapw(x,lambda,gamma,gammac,theta,ebx,groupfreq,censordist,timeinterval)
x |
Censor hazard function parameter. For exponential or weibull censor hazard, x is lambda, for uniform hazard, x is the maximum time of the censoring window. |
lambda |
lamda for event hazard function |
gamma |
gamma for event hazard function |
gammac |
gamma for censor hazard function. THis is required if the censoring hazard function is weibull |
theta |
Dropout rate |
ebx |
exp(beta*x), if there's no covariates, ebx=1. |
groupfreq |
frequence of each level of ebx value |
censordist |
censor hazard function distribution |
timeinterval |
time intervals for piecewise baseline hazard function |
Trial projection given current snapshot dataset.
projection(snapshot_data, enroll_continue=FALSE, samplesize=0, rand_ratio=c(1,1), blocksize=1,accrual_interval=NULL, accrual_rate=NULL, lambda=NULL, trtHR=NULL, dropoutrate=NULL,eventtarget=0,maxlpfollowup=NULL)projection(snapshot_data, enroll_continue=FALSE, samplesize=0, rand_ratio=c(1,1), blocksize=1,accrual_interval=NULL, accrual_rate=NULL, lambda=NULL, trtHR=NULL, dropoutrate=NULL,eventtarget=0,maxlpfollowup=NULL)
snapshot_data |
Snapshot dataset in data frame. Data frame must include column "time","status" and "accrual". Optional column "onstudy" is indicator of patients are still on study. |
rand_ratio |
randomization ratio: control vs treatment |
enroll_continue |
Indicate whether trial is still enrolling new patients. Default is FALSE. if TRUE, user needs provide "samplesize", "rand_ratio","blocksize", "accrual_interval","accrual_rate". |
samplesize |
Total sample size of the trial. only needed if "enroll_continue=TRUE" |
blocksize |
Randomization blocksize, only needed if "enroll_continue=TRUE" |
accrual_interval |
accrual time windows. only needed if "enroll_continue=TRUE" |
accrual_rate |
accrual rate for each time window. only needed if "enroll_continue=TRUE" |
lambda |
Baseline hazard function parameter for exponential distribution |
trtHR |
hazard ratio between treatment and control |
dropoutrate |
Dropout rate |
eventtarget |
Total target event number |
maxlpfollowup |
Maximum followup time for last enrolled patient |
Project final event numbers or trial stop time based on interim analysis data
projection_simulation(snapshot_data, rand_ratio=c(1,1), enroll_continue=FALSE, samplesize=0, blocksize=1,accrual_interval=NULL, accrual_rate=NULL, lambda=NULL, trtHR=NULL, dropoutrate=NULL,eventtarget=NULL,maxlpfollowup=NULL,N_simulation=1)projection_simulation(snapshot_data, rand_ratio=c(1,1), enroll_continue=FALSE, samplesize=0, blocksize=1,accrual_interval=NULL, accrual_rate=NULL, lambda=NULL, trtHR=NULL, dropoutrate=NULL,eventtarget=NULL,maxlpfollowup=NULL,N_simulation=1)
snapshot_data |
Snapshot dataset or interim analysis dataset |
rand_ratio |
Randomization ratio between control and treatment |
blocksize |
The value of this parameter is used to define the size of the randomization blocks. The actual blocksize is number of treatment levels mutiplied by this parameter. Please refer to "blockrand" package for detailed usage. |
enroll_continue |
True if trial is still in the enrollment period |
samplesize |
if enroll_continue=TRUE, please provide the total sample size of the trial. |
trtHR |
Hazard ratio between treatment groups (treatment vs control) |
accrual_interval |
Time windows for accrual |
accrual_rate |
accrual rate for each accrual time window |
lambda |
lambda for event hazard function (exponential) |
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) |
eventtarget |
Number of target events |
maxlpfollowup |
maximum follow up time for the last enrolled patient |
N_simulation |
number of simulations to run |
Generate block randomized treatment label based on covariates matrix
randomize_trt(cov_mat=cov_mat,blocksize=blocksize,trtHR=trtHR,rand_ratio=c(1,1))randomize_trt(cov_mat=cov_mat,blocksize=blocksize,trtHR=trtHR,rand_ratio=c(1,1))
cov_mat |
Covariates matrix. |
blocksize |
Randomization block size |
trtHR |
Hazard ratio between treatment arms. |
rand_ratio |
Randomization ratio between control and treatment |
Generate block randomized treatment label based on covariates matrix for two arm trial.
randomize_trt2(cov_mat=cov_mat,blocksize=blocksize,rand_ratio=c(1,1))randomize_trt2(cov_mat=cov_mat,blocksize=blocksize,rand_ratio=c(1,1))
cov_mat |
Covariates matrix. |
blocksize |
Randomization block size |
rand_ratio |
Randomization ratio between control and treatment |
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.
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)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)
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 |
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 |
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)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)
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.
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)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)
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. |
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 |
# 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# 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
Block randomization
self_blockrand(N=N,trt_levels=trt_levels,blocksize=blocksize,rand_ratio=rand_ratio)self_blockrand(N=N,trt_levels=trt_levels,blocksize=blocksize,rand_ratio=rand_ratio)
N |
Total number of patients |
trt_levels |
treatment levels in vector. for example, c(0,1) for control vs treatment trial. |
blocksize |
Randomization block size |
rand_ratio |
Randomization ratio between control and treatment |
self_blockrand(N=100,trt_levels=c(0,1),blocksize=2,rand_ratio=c(1,1))self_blockrand(N=100,trt_levels=c(0,1),blocksize=2,rand_ratio=c(1,1))
Simulate accrual time
step_accrual(np,tt,arate)step_accrual(np,tt,arate)
np |
Total number of patients |
tt |
Time windows |
arate |
accrual rate for each time windows |
timeinterval<-c(0,5,10) accrualrate<-c(10,15,20) N<-200 accrual<-step_accrual(N,timeinterval,accrualrate)timeinterval<-c(0,5,10) accrualrate<-c(10,15,20) N<-200 accrual<-step_accrual(N,timeinterval,accrualrate)
Simulate survival data given design matrix and covariates (betas).
surv_data_simulation(lambda,gamma,x,betas=NULL, dropoutrate=0, gammac=1,censordist='exponential',timeinterval=NULL,trt_timeinterval=NULL)surv_data_simulation(lambda,gamma,x,betas=NULL, dropoutrate=0, gammac=1,censordist='exponential',timeinterval=NULL,trt_timeinterval=NULL)
lambda |
lambda for event hazard function |
gamma |
gamma for event hazard function |
x |
design matrix |
betas |
coefficients for the covariates. The length of betas should be the same of number of columns of x |
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. Default is exponential |
timeinterval |
time intervals if the baseline hazard function is piecewise. |
trt_timeinterval |
Time windows for piecewise hazard ratios |
N<-400 x<-data.frame(arm=rbinom(N,1,0.5), factor1=rbinom(N,1,0.7), factor2=rbinom(N,1,0.8)) betas<-c(arm=-0.35667,factor1=0.3,factor2=-0.1) data<-surv_data_simulation(lambda=0.2,gamma=2,x=x,betas=betas,dropoutrate=0.2)N<-400 x<-data.frame(arm=rbinom(N,1,0.5), factor1=rbinom(N,1,0.7), factor2=rbinom(N,1,0.8)) betas<-c(arm=-0.35667,factor1=0.3,factor2=-0.1) data<-surv_data_simulation(lambda=0.2,gamma=2,x=x,betas=betas,dropoutrate=0.2)
Simulate clinical trial by adding accrual time to the simulated survival data. And cut the trial at the target event time or at the maximum follow up time
trial_data_simulation(simdata,accrual,eventtarget=NULL,maxlpfollowup=NULL)trial_data_simulation(simdata,accrual,eventtarget=NULL,maxlpfollowup=NULL)
simdata |
A data frame contains simulated survival time and censor status. |
accrual |
Simulated accrual time. |
eventtarget |
Target number of events of the trial |
maxlpfollowup |
The maximum follow up time for the last enrolled patient. |
Simulate event time with weibull hazard function
weibullsim(N=NULL,lambda,gamma,x=NULL,betas=NULL)weibullsim(N=NULL,lambda,gamma,x=NULL,betas=NULL)
N |
Total number of patients |
lambda |
lambda for event hazard function |
gamma |
gamma for event hazard function |
x |
design matrix |
betas |
covariates for design matrix |
data<-weibullsim(N=300,lambda=0.03,gamma=0.9)data<-weibullsim(N=300,lambda=0.03,gamma=0.9)
Simulate event time with weibull hazard function for piecewise hazard ratios
weibullsim_betapw(lambda,gamma,t,x,betas)weibullsim_betapw(lambda,gamma,t,x,betas)
lambda |
lambda for event hazard function |
gamma |
gamma for event hazard function |
x |
design matrix |
betas |
covariates for design matrix |
t |
TIme windows for piecewise hazard ratios |
Simulate event time with weibull hazard function given the piecewise parameters.
weibullsim_pw(lambda,gamma,t,x,betas=NULL)weibullsim_pw(lambda,gamma,t,x,betas=NULL)
lambda |
lambda for event hazard function. It will be a vector for piecewise hazard function. |
gamma |
gamma for event hazard function. It will be a vector for piecewise hazard function. |
x |
design matrix |
betas |
covariates for desing matrix |
t |
time intervals for piecewise weibull parameters. |