| Title: | Predicting and Simulating Clinical Trial with Time-to-Event Endpoint |
| Version: | 0.0.4 |
| Description: | Predict the course of clinical trial with a time-to-event endpoint for both two-arm and single-arm design. Each of the four primary study design parameters (the expected number of observed events, the number of subjects enrolled, the observation time, and the censoring parameter) can be derived analytically given the other three parameters. And the simulation datasets can be generated based on the design settings. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0), R.rsp |
| Config/testthat/edition: | 3 |
| VignetteBuilder: | R.rsp |
| NeedsCompilation: | no |
| Packaged: | 2024-11-22 14:58:20 UTC; YDing29 |
| Author: | Yang Ding [aut, cre] |
| Maintainer: | Yang Ding <tomding.biostat@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2024-11-25 11:40:18 UTC |
Calculate the censoring rate for a one-arm design
Description
Calculate the censoring rate for a one-arm design
Usage
CensRate.OneArm(
N = NULL,
d = NULL,
s = NULL,
m = NULL,
l = NULL,
alpha = NULL,
nu = NULL
)
Arguments
N |
Number of subjects plan to enrolled |
d |
expected number of events observed at time |
s |
enrollment period |
m |
maximum follow-up for a single subject |
l |
observation time |
alpha |
shape parameter of weibull survival distribution |
nu |
scale parameter of weibull survival distribution |
Value
This function returns a list containing all design parameters, including the calculated censoring rate gamma.
Examples
CensRate.OneArm(N=100,d=10,l=10,s=12,m=6,alpha=1,nu=20)
Calculate the censoring rate for a two-arm clinical trial
Description
Calculate the censoring rate for a two-arm clinical trial
Usage
CensTime.TwoArm(
N.0 = NULL,
N.1 = NULL,
d = NULL,
l = NULL,
alpha0.t = NULL,
nu0.t = NULL,
alpha1.t = NULL,
nu1.t = NULL,
s = NULL,
m = NULL,
design2 = NULL
)
Arguments
N.0 |
number of subjects plan to be enrolled in control arm |
N.1 |
number of subjects plan to be enrolled in experimental arm |
d |
expected number of events observed at time |
l |
observation time |
alpha0.t |
shape parameter of weibull survival distribution for control arm |
nu0.t |
scale parameter of weibull survival distribution for control arm |
alpha1.t |
shape parameters of weibull survival distribution for experimental arm |
nu1.t |
scale parameter of a weibull survival distribution for control arm |
s |
enrollment time |
m |
maximum follow-up time for a subject |
design2 |
a list containing all the above parameters for two-arm design |
Value
This function returns a list containing all design parameters, including the calculated censoring rate gamma.c
Examples
#calculate the censoring parameter
CensTime.TwoArm(N.0=100,N.1=100,d=10,l=3,alpha0.t=1,nu0.t=5,alpha1.t=2,nu1.t=4,s=5,m=4)
Calculate the expected number of events or number of subjects enrolled in a one-arm clinical trial
Description
Calculate the expected number of events or number of subjects enrolled in a one-arm clinical trial
Usage
NumEventsSub.OneArm(
N = NULL,
d = NULL,
l = NULL,
gamma = NULL,
s = NULL,
m = NULL,
alpha = NULL,
nu = NULL,
design1 = NULL
)
Arguments
N |
Number of subjects plan to enrolled |
d |
expected number of events observed at time |
l |
observation time |
gamma |
parameter of the exponential distribution of censoring time |
s |
enrollment period |
m |
maximum follow-up for a single subject |
alpha |
shape parameter of weibull survival distribution |
nu |
scale parameter of weibull survival distribution |
design1 |
a list containing all the above parameters for one-arm design |
Value
This function returns a list containing all design parameters as the same with input parameters of this function.
Examples
# Calculate the expected number of events in a one-arm clinical trial
NumEventsSub.OneArm(N=100,d=NULL,l=3,gamma=0.1,s=12,m=6,alpha=1,nu=20)
Calculate the expected number of events or number of subjects enrolled in a two-arm clinical trial
Description
Calculate the expected number of events or number of subjects enrolled in a two-arm clinical trial
Usage
NumEventsSub.TwoArm(
N.0 = NULL,
N.1 = NULL,
ratio = NULL,
d = NULL,
l = NULL,
gamma.c = NULL,
alpha0.t = NULL,
nu0.t = NULL,
alpha1.t = NULL,
nu1.t = NULL,
s = NULL,
m = NULL,
design2 = NULL
)
Arguments
N.0 |
number of subjects plan to be enrolled in control arm |
N.1 |
number of subjects plan to be enrolled in experimental arm |
ratio |
randomization ratio between two arms: |
d |
expected number of events observed at time |
l |
observation time |
gamma.c |
parameter of the exponential distribution of censoring time |
alpha0.t |
shape parameter of weibull survival distribution for control arm |
nu0.t |
scale parameter of weibull survival distribution for control arm |
alpha1.t |
shape parameters of weibull survival distribution for experimental arm |
nu1.t |
scale parameter of a weibull survival distribution for control arm |
s |
enrollment time |
m |
maximum follow-up time for a subject |
design2 |
a list containing all the above parameters for two-arm design |
Value
This function returns a list containing all design parameters as the same with input parameters of this function.
Examples
# calculate the expected number of events
NumEventsSub.TwoArm(N.0=100,N.1=100,l=6,gamma.c=1,alpha0.t = 1,nu0.t=5,alpha1.t=2,nu1.t=4,s=5,m=4)
# calculate the expeTrcted number of events using a list as input
design2 <- list(N.0=100,N.1=100,l=6,gamma.c=1,alpha0.t = 1,nu0.t=5,alpha1.t=2,nu1.t=4,s=5,m=4)
NumEventsSub.TwoArm(design2=design2)
# calculate the number of subject enrolled
NumEventsSub.TwoArm(ratio=1,d=24,l=6,gamma.c=1,alpha0.t = 1,nu0.t=5,alpha1.t=2,nu1.t=4,s=5,m=4)
Calculate the observation time for a one-arm clinical trial
Description
Calculate the observation time for a one-arm clinical trial
Usage
ObsTime.OneArm(
N = NULL,
d = NULL,
s = NULL,
m = NULL,
alpha = NULL,
nu = NULL,
gamma = NULL
)
Arguments
N |
Number of subjects plan to enrolled |
d |
expected number of events observed at time |
s |
enrollment period |
m |
maximum follow-up for a single subject |
alpha |
shape parameter of weibull survival distribution |
nu |
scale parameter of weibull survival distribution |
gamma |
parameter of the exponential distribution of censoring time |
Value
This function returns a list containing all design parameters, including the calculated observation time l.
Examples
ObsTime.OneArm(N=100,d=10,gamma=0.1,s=12,m=6,alpha=1,nu=20)
Calculate the observation time for a two-arm clinical trial
Description
predicting two-arm clinical trial
Usage
ObsTime.TwoArm(
N.0 = NULL,
N.1 = NULL,
ratio = NULL,
d = NULL,
gamma.c = NULL,
alpha0.t = NULL,
nu0.t,
alpha1.t,
nu1.t,
s,
m,
design2 = NULL
)
Arguments
N.0 |
number of subjects plan to be enrolled in control arm |
N.1 |
number of subjects plan to be enrolled in experimental arm |
ratio |
randomization ratio between two arms: |
d |
expected number of events observed at time |
gamma.c |
parameter of the exponential distribution of censoring time |
alpha0.t |
shape parameter of weibull survival distribution for control arm |
nu0.t |
scale parameter of weibull survival distribution for control arm |
alpha1.t |
shape parameters of weibull survival distribution for experimental arm |
nu1.t |
scale parameter of a weibull survival distribution for control arm |
s |
enrollment time |
m |
maximum follow-up time for a subject |
design2 |
a list containing all the above parameters for two-arm design |
Value
This function returns a list containing all design parameters, including the calculated observation time l
Examples
# calculate the observation time
ObsTime.TwoArm(N.0=100,N.1=100,d=10,gamma.c=1,alpha0.t = 1,nu0.t=5,alpha1.t=2,nu1.t=4,s=5,m=4)
Simulating survival dataset for a one-arm design
Description
Simulating survival dataset for a one-arm design
Usage
SimData.OneArm(
N = NULL,
d = NULL,
l = NULL,
gamma = NULL,
s = NULL,
m = NULL,
alpha = NULL,
nu = NULL,
design1,
seed,
nsim
)
Arguments
N |
Number of subjects plan to enrolled |
d |
expected number of events observed at time |
l |
observation time |
gamma |
parameter of the exponential distribution of censoring time |
s |
enrollment period |
m |
maximum follow-up for a single subject |
alpha |
shape parameter of weibull survival distribution |
nu |
scale parameter of weibull survival distribution |
design1 |
a list containing all the above parameters for one-arm design |
seed |
random seed number |
nsim |
number of simulations |
Value
This function will return the simulated datasets and the according design settings
Examples
design1 <- TrialPred.OneArm(N=100,d=NULL,l=3,gamma=0.1
,s=12,m=6,alpha=1,nu=20)
# Simulate 100 datasets under design1
SimData.OneArm(design1=design1,seed=1234,nsim=100)
Simulating survival dataset for a two-arm design
Description
Simulating survival dataset for a two-arm design
Usage
SimData.TwoArm(
N.0 = NULL,
N.1 = NULL,
ratio = NULL,
d = NULL,
l = NULL,
gamma.c = NULL,
s = NULL,
m = NULL,
alpha0.t = NULL,
nu0.t = NULL,
HR = NULL,
alpha1.t = NULL,
nu1.t = NULL,
design2 = NULL,
seed = NULL,
nsim = NULL
)
Arguments
N.0 |
number of subjects plan to be enrolled in control arm |
N.1 |
number of subjects plan to be enrolled in experimental arm |
ratio |
randomization ratio between two arms: |
d |
expected number of events observed at time |
l |
observation time |
gamma.c |
parameter of the exponential distribution of censoring time |
s |
enrollment time |
m |
maximum follow-up time for a subject |
alpha0.t |
shape parameter of weibull survival distribution for control arm |
nu0.t |
scale parameter of weibull survival distribution for control arm |
HR |
hazard ratio of experimental group over control group |
alpha1.t |
shape parameters of weibull survival distribution for experimental arm |
nu1.t |
scale parameter of a weibull survival distribution for control arm |
design2 |
a list containing all the above parameters for two-arm design |
seed |
random seed |
nsim |
number of simulations |
Value
This function will return the simulated datasets and the according design settings
Examples
design2 <- NumEventsSub.TwoArm(N.0=100,N.1=100,l=6,gamma.c=1
,alpha0.t = 1,nu0.t=5,alpha1.t=2,nu1.t=4,s=5,m=4)
SimData.TwoArm(design2=design2,seed=1234,nsim=100)
Function for predicting one-arm clinical trial
Description
Function for predicting one-arm clinical trial
Usage
TrialPred.OneArm(
N = NULL,
d = NULL,
l = NULL,
gamma = NULL,
s = NULL,
m = NULL,
alpha = NULL,
nu = NULL,
design1 = NULL
)
Arguments
N |
Number of subjects plan to enrolled |
d |
expected number of events observed at time |
l |
observation time |
gamma |
parameter of the exponential distribution of censoring time |
s |
enrollment period |
m |
maximum follow-up for a single subject |
alpha |
shape parameter of weibull survival distribution |
nu |
scale parameter of weibull survival distribution |
design1 |
a list containing all the above parameters for one-arm design |
Value
This function returns a list containing all design parameters as the same with input parameters of this function. If any one of the parameters d, N, l or gamma is missing, it can be calculated based on the other parameters.
Examples
# Calculate the expected number of events in a one-arm clinical trial
TrialPred.OneArm(N=100,d=NULL,l=3,gamma=0.1,s=12,m=6,alpha=1,nu=20)
#Calculate the expected number of events using a list as input
design1 <- list(N=100,d=NULL,l=3,gamma=0.1,s=12,m=6,alpha=1,nu=20)
TrialPred.OneArm(design1=design1)
#Calculate the number of subjects enrolled
TrialPred.OneArm(N=NULL,d=8,l=15,gamma=0.1,s=12,m=6,alpha=1,nu=20)
#Calculate the observation time
TrialPred.OneArm(N=100,d=10,l=NULL,gamma=0.1,s=12,m=6,alpha=1,nu=20)
#Calculate the censoring parameter gamma
TrialPred.OneArm(N=100,d=10,l=10,gamma=NULL,s=12,m=6,alpha=1,nu=20)
Function for predicting two-arm clinical trial
Description
predicting two-arm clinical trial
Usage
TrialPred.TwoArm(
N.0 = NULL,
N.1 = NULL,
ratio = NULL,
d = NULL,
l = NULL,
gamma.c = NULL,
alpha0.t = NULL,
nu0.t = NULL,
HR = NULL,
alpha1.t = NULL,
nu1.t = NULL,
s = NULL,
m = NULL,
design2 = NULL
)
Arguments
N.0 |
number of subjects plan to be enrolled in control arm |
N.1 |
number of subjects plan to be enrolled in experimental arm |
ratio |
randomization ratio between two arms: |
d |
expected number of events observed at time |
l |
observation time |
gamma.c |
parameter of the exponential distribution of censoring time |
alpha0.t |
shape parameter of weibull survival distribution for control arm |
nu0.t |
scale parameter of weibull survival distribution for control arm |
HR |
hazard ratio of experimental group over control group |
alpha1.t |
shape parameters of weibull survival distribution for experimental arm |
nu1.t |
scale parameter of a weibull survival distribution for control arm |
s |
enrollment time |
m |
maximum follow-up time for a subject |
design2 |
a list containing all the above parameters for two-arm design |
Value
This function returns a list containing all design parameters as the same with input parameters of this function. If any one of the parameters d, N.0(or N.1), l or gamma.c is missing, it can be calculated based on the other parameters.
Examples
# calculate the expected number of events
TrialPred.TwoArm(N.0=100,N.1=100,d=NULL,l=6,gamma.c=1
,alpha0.t = 1,nu0.t=5,alpha1.t=2,nu1.t=4,s=5,m=4)
# calculate the expected number of events using a list as input
design2 <- list(N.0=100,N.1=100,d=NULL,l=6,gamma.c=1
,alpha0.t = 1,nu0.t=5,alpha1.t=2,nu1.t=4,s=5,m=4)
TrialPred.TwoArm(design2=design2)
# calculate the number of subject enrolled
TrialPred.TwoArm(N.0=NULL,N.1=NULL,ratio=1,d=24,l=6,gamma.c=1
,alpha0.t = 1,nu0.t=5,alpha1.t=2,nu1.t=4,s=5,m=4)
# calculate the observation time
TrialPred.TwoArm(N.0=100,N.1=100,d=10,l=NULL,gamma.c=1
,alpha0.t = 1,nu0.t=5,alpha1.t=2,nu1.t=4,s=5,m=4)
# calculate the censoring parameter
TrialPred.TwoArm(N.0=100,N.1=100,d=10,l=3,gamma.c=NULL
,alpha0.t=1,nu0.t=5,alpha1.t=2,nu1.t=4,s=5,m=4)