| Type: | Package |
| Title: | Simulate and Conduct Dose-Escalation Phase I Studies |
| Version: | 1.0.3 |
| Date: | 2024-04-25 |
| Author: | Benjamin Esterni with contribution from Baboukar Mane |
| Maintainer: | Christophe Zemmour <christophe.zemmour@gmail.com> |
| Description: | Two Phase I designs are implemented in the package: the classical 3+3 and the Continual Reassessment Method (<doi:10.2307/2531628>). Simulations tools are also available to estimate the operating characteristics of the methods with several user-dependent options. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Packaged: | 2024-04-26 08:00:12 UTC; zemmourc |
| Repository: | CRAN |
| Date/Publication: | 2024-04-26 08:30:02 UTC |
UBCRM is a package containing functions to simulate and conduct dose escalation phase I studies
Description
Two designs are implemented in the package: the classical 3+3 and the Continual Reassessment Method. Simulations tools are also available to estimate the operating characteristics of the methods with several user-dependent options.
Author(s)
Benjamin Esterni, Baboukar Mane. Unite de Biostatistique et de Methodologie, Institut Paoli-Calmettes, Marseille, France.
References
O'Quigley J., Pepe M., Fisher L. (1990). Continual Reassessment Method: a practical design for Phase I clinical trials in cancer. Biometrics 46, 33-48. <https://doi.org/10.2307/2531628>
O'Quigley J., Shen LZ. (1996). Continual Reassessment Method: a likelihood approach. Biometrics 52, 673-684. <https://doi.org/10.2307/2532905>
Paoletti X., Kramar A. (2009). A comparison of model choices for the Continual Reassessment Method in phase I cancer trials. Statistics in Medecine 28, 3012-3028. <https://doi.org/10.1002/sim.3682>
Chamorey Emmanuel. (2009). Methodologie des essais de phase precoce en cancerologie: evolution des schemas et apport de la pharmacologie. These.
Garret-Mayer Elizabeth. (2006). The Continual Reassessment Method for dose-finding studies: a tutorial. Clinical Trials: 57-71. <https://doi.org/10.1191/1740774506cn134oa>
Examples
data<- CreData(4)
prior<-c(.05,.1,.15,.2)
# One study simulation
simCrm(prior=prior, firstdose = 2, truerate = prior, cohortsize = 3, target = 1/3,
nptmax = 18, nmaxmtd = 6, nmaxdose = 18, sd = 1.34, approach = "bayes", model = "power",
method = "fpost", nextlevel = "ntarget", upskipping = TRUE, downskipping = FALSE,
lastdose = NA, graphic = FALSE, seed = 20130110)
# N simulations with CRM
# Power model, no up skipping, start at dose 2
res1<- ssimCrm(prior=prior, 100, firstdose = 2, truerate = prior, cohortsize = 3,
target = 1/3, nptmax = 18, nmaxmtd = 6, nmaxdose = 18, sd = 1.34, approach = "bayes",
method = "fpost", model = "power", nextlevel = "ntarget", upskipping = TRUE,
downskipping = FALSE, r = 2, seed=20130110)
res1
# Simulations with 3+3 design
res2<- ssim3p3(truerate=prior, 100, r = 2, seed=20130110)
res2
Creates a CRM dataframe
Description
Creates a n-row summary dataframe indicating the number of treated patients and observed DLTs at each of the n dose-levels. This is the dataframe structure that will be needed in the different functions of the UBCRM package.
Usage
CreData(ndose = 3, dosenames = paste("dose", 1:ndose, sep = " "))
Arguments
ndose |
Number of dose levels. |
dosenames |
A ndose-length character vector of labels for the dose levels. |
Value
A ndose * 3 dataframe containing:
dose |
Integer value 1..ndose ordering the doses. |
npt |
Integer count of the treated patients at dose i. |
ndlt |
Integer count of the observed DLT at dose i. |
Author(s)
Benjamin Esterni, Baboukar Mane. Unite de Biostatistique et de Methodologie, Institut Paoli-Calmettes, Marseille, France.
References
O'Quigley J., Pepe M., Fisher L. (1990). Continual Reassessment Method: a practical design for Phase I clinical trials in cancer. Biometrics 46, 33-48. <https://doi.org/10.2307/2531628>
O'Quigley J., Shen LZ. (1996). Continual Reassessment Method: a likelihood approach. Biometrics 52, 673-684. <https://doi.org/10.2307/2532905>
Paoletti X., Kramar A. (2009). A comparison of model choices for the Continual Reassessment Method in phase I cancer trials. Statistics in Medecine 28, 3012-3028. <https://doi.org/10.1002/sim.3682>
Chamorey Emmanuel. (2009). Methodologie des essais de phase precoce en cancerologie: evolution des schemas et apport de la pharmacologie. These.
Garret-Mayer Elizabeth. (2006). The Continual Reassessment Method for dose-finding studies: a tutorial. Clinical Trials: 57-71. <https://doi.org/10.1191/1740774506cn134oa>
See Also
Examples
data<- CreData(5,c("5 mg/m2","7 mg/m2","10 mg/m2","15 mg/m2","20 mg/m2"))
data
Dose-escalation with the Continual Reassessment Method
Description
The function gives the next level to include patients following a model-based design. Needs an updated input dataframe with the CreData() structure.
Usage
Crm(Dk, prior, target = 1/3, nptmax = 24, nmaxmtd = 6, nmaxdose = nptmax, sd = 1.34,
approach = "bayes", model = "power", method = "fpost", nextlevel = "ntarget",
upskipping = F, downskipping = F, lastdose = NA)
Arguments
Dk |
Study dataframe with CreData() structure. |
prior |
Numeric vector of prior DLTs probabilities. |
target |
Target used for the MTD determination. |
nptmax |
Maximum number of patients to include in the study. |
nmaxmtd |
Maximum number of patients to be treated at the designated MTD. Assign a high value (=nptmax) to avoid such a stopping rule. |
nmaxdose |
Maximum number of patients to be treated at the same dose. Assign a high value (=nptmax) to avoid such a stopping rule. |
sd |
Standard deviation used in case of a normal distribution assumption for the parameter. |
approach |
Character indicating the estimation method: "bayes" (default value) for CRM or "mle" for CRML. |
model |
Character indicating the dose-DLT relationship model: "power", "tangent" or "logistic". More informations in the details section. |
method |
Estimation method for the posterior probabilities. "fpost" (default) estimates the mean of the posterior distribution of the parameter alpha (hat_alpha=E[alpha]) and uses it in psy(hat_alpha,...). "ppostp" and "pposts" directly estimate the mean of the posterior DLT probability. "ppostp" uses prior as singletons whereas "pposts" calculates appropriates singletons (see ail, ait or aip functions). |
nextlevel |
Character option used for determining the next dose level. "ntarget" (default) if the next level is chosen as the closest level to the desired target (may be higher than target). "utarget" if the next level is the closest level with the restriction to be lower than the target value. |
upskipping |
Boolean option used for determining the next dose level. If TRUE no level skip in escalation will be allowed. If FALSE (default) the level skips will be permitted. |
downskipping |
Boolean option used for determining the next dose level. If TRUE no level skip in desescalation will be allowed. If FALSE (default) the level skips will be permitted. |
lastdose |
Integer representing the last experimented dose level. |
Details
Details of the 3 dose-DLT relationship proposed models: "power" for the power model psy(s,a)=s^exp(a), "tangent" for the hyperbolic tangent model psy(s,a)= ((tanh(s)+1)/2)**a, "logistic" for the logistic model psy(s,a) = exp(3+a*s)/(1+exp(3+a*s)). Note: power and tangent models are equivalent after an appropriate transformation on the parameter.
Value
nextdose |
An integer representing the next recommended dose to experiment. |
mtd |
If reached, an integer representing the MTD. |
prob |
Posterior DLTs probabilities. |
Author(s)
Benjamin Esterni, Baboukar Mane. Unite de Biostatistique et de Methodologie, Institut Paoli-Calmettes, Marseille, France.
References
O'Quigley J., Pepe M., Fisher L. (1990). Continual Reassessment Method: a practical design for Phase I clinical trials in cancer. Biometrics 46, 33-48. <https://doi.org/10.2307/2531628>
O'Quigley J., Shen LZ. (1996). Continual Reassessment Method: a likelihood approach. Biometrics 52, 673-684. <https://doi.org/10.2307/2532905>
Paoletti X., Kramar A. (2009). A comparison of model choices for the Continual Reassessment Method in phase I cancer trials. Statistics in Medecine 28, 3012-3028. <https://doi.org/10.1002/sim.3682>
Chamorey Emmanuel. (2009). Methodologie des essais de phase precoce en cancerologie: evolution des schemas et apport de la pharmacologie. These.
Garret-Mayer Elizabeth. (2006). The Continual Reassessment Method for dose-finding studies: a tutorial. Clinical Trials: 57-71. <https://doi.org/10.1191/1740774506cn134oa>
See Also
Examples
data<- CreData(5)
data<- updata(data,1,3,0)
data<- updata(data,2,3,1)
data<- updata(data,2,3,1)
data
Crm(data,prior=c(0.1,0.15,0.25,0.35,0.45),target=0.3,nextlevel="ntarget",nptmax=24,nmaxmtd=6)
data<- updata(data,3,3,2)
data
Crm(data,prior=c(0.1,0.15,0.25,0.35,0.45),target=0.3,nextlevel="ntarget",nptmax=24,nmaxmtd=6)
Likelihood functions
Description
Lp is the likelihood function for the power model psy(s,a)=s^exp(a). Lt is the likelihood function for the hyperbolic tangent model psy(s,a)= ((tanh(s)+1)/2)**a. Ll is the likelihood function for the logistic model psy(s,a) = exp(3+a*s)/(1+exp(3+a*s)).
Usage
Lp(a, data, sgl)
Lt(a, data, sgl)
Ll(a, data, sgl)
Arguments
a |
Parameter. |
data |
CRM dataframe with a CreData() structure. |
sgl |
Dose level singleton. |
Value
Numeric value of the computed likelihood.
Author(s)
Benjamin Esterni, Baboukar Mane. Unite de Biostatistique et de Methodologie, Institut Paoli-Calmettes, Marseille, France.
References
O'Quigley J., Pepe M., Fisher L. (1990). Continual Reassessment Method: a practical design for Phase I clinical trials in cancer. Biometrics 46, 33-48. <https://doi.org/10.2307/2531628>
O'Quigley J., Shen LZ. (1996). Continual Reassessment Method: a likelihood approach. Biometrics 52, 673-684. <https://doi.org/10.2307/2532905>
Paoletti X., Kramar A. (2009). A comparison of model choices for the Continual Reassessment Method in phase I cancer trials. Statistics in Medecine 28, 3012-3028. <https://doi.org/10.1002/sim.3682>
Chamorey Emmanuel. (2009). Methodologie des essais de phase precoce en cancerologie: evolution des schemas et apport de la pharmacologie. These.
Garret-Mayer Elizabeth. (2006). The Continual Reassessment Method for dose-finding studies: a tutorial. Clinical Trials: 57-71. <https://doi.org/10.1191/1740774506cn134oa>
Functions to calculate the appropriate dose level singletons
Description
Pool of functions to calculate dose level singletons values: aip, ail2 and ait2 calculate sgl in order that E[psy] = prior, ail1 and ait1 calculate sgl in order that psy(sgl,1) = prior.
Usage
aip(p_prior, sd = 1.34)
ait1(p_prior, a=1)
ail1(p_prior, a=1)
ait2(p_prior)
ail2(p_prior)
Arguments
p_prior |
Prior toxicity probability. |
sd |
Standard deviation in case of normal distribution for the parameter. |
a |
Rate in case of exponential distribution for the parameter. |
Value
Numeric length(p-prior)-vector.
Author(s)
Benjamin Esterni, Baboukar Mane. Unite de Biostatistique et de Methodologie, Institut Paoli-Calmettes, Marseille, France.
References
O'Quigley J., Pepe M., Fisher L. (1990). Continual Reassessment Method: a practical design for Phase I clinical trials in cancer. Biometrics 46, 33-48. <https://doi.org/10.2307/2531628>
O'Quigley J., Shen LZ. (1996). Continual Reassessment Method: a likelihood approach. Biometrics 52, 673-684. <https://doi.org/10.2307/2532905>
Paoletti X., Kramar A. (2009). A comparison of model choices for the Continual Reassessment Method in phase I cancer trials. Statistics in Medecine 28, 3012-3028. <https://doi.org/10.1002/sim.3682>
Chamorey Emmanuel. (2009). Methodologie des essais de phase precoce en cancerologie: evolution des schemas et apport de la pharmacologie. These.
Garret-Mayer Elizabeth. (2006). The Continual Reassessment Method for dose-finding studies: a tutorial. Clinical Trials: 57-71. <https://doi.org/10.1191/1740774506cn134oa>
Density functions
Description
Density functions for the model parameter. fp(a,sd) is the normal density: 1/(sd*sqrt(2*pi))*exp(-(a^2)/(2*sd^2)). ft and fl are the exponential density (with a fixed rate = 1): exp(-a).
Usage
fp(a, sd)
ft(a)
fl(a)
Arguments
a |
Parameter. |
sd |
Standard deviation. |
Value
Numeric value of the computed density.
Author(s)
Benjamin Esterni, Baboukar Mane. Unite de Biostatistique et de Methodologie, Institut Paoli-Calmettes, Marseille, France.
References
O'Quigley J., Pepe M., Fisher L. (1990). Continual Reassessment Method: a practical design for Phase I clinical trials in cancer. Biometrics 46, 33-48. <https://doi.org/10.2307/2531628>
O'Quigley J., Shen LZ. (1996). Continual Reassessment Method: a likelihood approach. Biometrics 52, 673-684. <https://doi.org/10.2307/2532905>
Paoletti X., Kramar A. (2009). A comparison of model choices for the Continual Reassessment Method in phase I cancer trials. Statistics in Medecine 28, 3012-3028. <https://doi.org/10.1002/sim.3682>
Chamorey Emmanuel. (2009). Methodologie des essais de phase precoce en cancerologie: evolution des schemas et apport de la pharmacologie. These.
Garret-Mayer Elizabeth. (2006). The Continual Reassessment Method for dose-finding studies: a tutorial. Clinical Trials: 57-71. <https://doi.org/10.1191/1740774506cn134oa>
Dose-Toxicity modelisation functions
Description
psip corresponds to the power model psy(s,a)=s^exp(a). psit corresponds to the hyperbolic tangent model psy(s,a)= ((tanh(s)+1)/2)**a. psil corresponds to the logistic model psy(s,a) = exp(3+a*s)/(1+exp(3+a*s)).
Usage
psip(sgl,a)
psit(sgl,a)
psil(sgl,a)
Arguments
sgl |
Dose level singleton. |
a |
Parameter. |
Value
Numeric value of the computed function.
Author(s)
Benjamin Esterni, Baboukar Mane. Unite de Biostatistique et de Methodologie, Institut Paoli-Calmettes, Marseille, France.
References
O'Quigley J., Pepe M., Fisher L. (1990). Continual Reassessment Method: a practical design for Phase I clinical trials in cancer. Biometrics 46, 33-48. <https://doi.org/10.2307/2531628>
O'Quigley J., Shen LZ. (1996). Continual Reassessment Method: a likelihood approach. Biometrics 52, 673-684. <https://doi.org/10.2307/2532905>
Paoletti X., Kramar A. (2009). A comparison of model choices for the Continual Reassessment Method in phase I cancer trials. Statistics in Medecine 28, 3012-3028. <https://doi.org/10.1002/sim.3682>
Chamorey Emmanuel. (2009). Methodologie des essais de phase precoce en cancerologie: evolution des schemas et apport de la pharmacologie. These.
Garret-Mayer Elizabeth. (2006). The Continual Reassessment Method for dose-finding studies: a tutorial. Clinical Trials: 57-71. <https://doi.org/10.1191/1740774506cn134oa>
Simulation of one dose-escalation study with the classical 3+3 design
Description
Given a true rates vector of DLT probabilities, the function simulate a 3+3 dose-escalation design.
Usage
sim3p3(truerate, seed = NULL)
Arguments
truerate |
A nlevel-length vector of true rates for the DLTs. |
seed |
If not empty, the seed to use for random generation. |
Value
data |
Study data. |
mtd |
If reached, an integer representing the MTD level. |
lastdose |
An integer representing the last experimented dose. |
Author(s)
Benjamin Esterni, Baboukar Mane. Unite de Biostatistique et de Methodologie, Institut Paoli-Calmettes, Marseille, France.
References
O'Quigley J., Pepe M., Fisher L. (1990). Continual Reassessment Method: a practical design for Phase I clinical trials in cancer. Biometrics 46, 33-48. <https://doi.org/10.2307/2531628>
O'Quigley J., Shen LZ. (1996). Continual Reassessment Method: a likelihood approach. Biometrics 52, 673-684. <https://doi.org/10.2307/2532905>
Paoletti X., Kramar A. (2009). A comparison of model choices for the Continual Reassessment Method in phase I cancer trials. Statistics in Medecine 28, 3012-3028. <https://doi.org/10.1002/sim.3682>
Chamorey Emmanuel. (2009). Methodologie des essais de phase precoce en cancerologie: evolution des schemas et apport de la pharmacologie. These.
Garret-Mayer Elizabeth. (2006). The Continual Reassessment Method for dose-finding studies: a tutorial. Clinical Trials: 57-71. <https://doi.org/10.1191/1740774506cn134oa>
See Also
Examples
# A 3-dose study with 10%, 20% and 30% of true rates for toxicity
sim3p3(c(0.1,0.2,0.3))
Simulation of one dose-escalation study with the Continual Reassessment Method
Description
Given prior and true rates vectors of DLT probabilities, the function simulates a CRM dose-escalation design.
Usage
simCrm(prior, firstdose = NA, truerate = prior, cohortsize = 3, target = 1/3,nptmax = 24,
nmaxmtd = nptmax, nmaxdose = nptmax, sd = 1.34, approach = "bayes", model = "power",
method = "fpost", nextlevel = "ntarget", upskipping = F, downskipping = F, lastdose = NA,
graphic = F, seed = NULL)
Arguments
prior |
Numeric vector of prior DLT probabilities. |
firstdose |
Integer representing the dose at which the first cohort will be treated. |
truerate |
A nlevel-length vector of true rates for the DLTs. |
cohortsize |
Size of the cohort. Default value = 3. |
target |
Target used for the MTD determination. |
nptmax |
Maximum number of patients to include in the study. |
nmaxmtd |
Maximum number of patients to be treated at the designated MTD. Assign a high value (=nptmax) to avoid such a stopping rule. |
nmaxdose |
Maximum number of patients to be treated at the same dose. Assign a high value (=nptmax) to avoid such a stopping rule. |
sd |
Standard deviation used in case of a normal distribution assumption for the parameter. |
approach |
Character indicating the estimation method: "bayes" (default value) for CRM or "mle" for CRML. |
model |
Character indicating the dose-DLT relationship model: "power", "tangent" or "logistic". |
method |
Estimation method for the posterior probabilities. "fpost" (default) estimates the mean of the posterior distribution of the paramater alpha (hat_alpha=E[alpha]) and uses it in psy(hat_alpha,...). "ppostp" and "pposts" directly estimate the mean of the posterior DLT probability. "ppostp" uses prior as singletons whereas "pposts" calculates appropriate singletons (see ail, ait or aip functions). |
nextlevel |
Character option used for determining the next dose level. "ntarget" (default) if the next level is chosen as the closest level to the desired target (may be higher than target). "utarget" if the next level is the closest level with the restriction to be lower than the target value. |
upskipping |
Boolean option used for determining the next dose level. If TRUE no level skip in escalation will be allowed. If FALSE (default) the level skips will be permitted. |
downskipping |
Boolean option used for determining the next dose level. If TRUE no level skip in desescalation will be allowed. If FALSE (default) the level skips will be permitted. |
lastdose |
Integer representing the last experimented dose level. |
graphic |
Boolean option for graphic generation. |
seed |
If not empty, the seed to use for random generation. |
Value
data |
Study data. |
dose |
Integer vector representing for each cohort the experimented dose levels. |
nDLT |
Integer vector representing for each cohort the number of observed DLTs. |
mtd |
If reached, an integer representing the MTD level. |
lastdose |
An integer representing the last experimented dose. |
prob |
Posterior DLT probabilities. |
Author(s)
Benjamin Esterni, Baboukar Mane. Unite de Biostatistique et de Methodologie, Institut Paoli-Calmettes, Marseille, France.
References
O'Quigley J., Pepe M., Fisher L. (1990). Continual Reassessment Method: a practical design for Phase I clinical trials in cancer. Biometrics 46, 33-48. <https://doi.org/10.2307/2531628>
O'Quigley J., Shen LZ. (1996). Continual Reassessment Method: a likelihood approach. Biometrics 52, 673-684. <https://doi.org/10.2307/2532905>
Paoletti X., Kramar A. (2009). A comparison of model choices for the Continual Reassessment Method in phase I cancer trials. Statistics in Medecine 28, 3012-3028. <https://doi.org/10.1002/sim.3682>
Chamorey Emmanuel. (2009). Methodologie des essais de phase precoce en cancerologie: evolution des schemas et apport de la pharmacologie. These.
Garret-Mayer Elizabeth. (2006). The Continual Reassessment Method for dose-finding studies: a tutorial. Clinical Trials: 57-71. <https://doi.org/10.1191/1740774506cn134oa>
See Also
Examples
simCrm(c(0.1,0.2,0.3,0.35,0.45),firstdose=1,target=0.33)
Simulation of n dose-escalation study with the 3+3 design
Description
The ssim3p3 function simulates n dose-escalation study with the 3+3 design and provides summarized results.
Usage
ssim3p3(truerate, n, r = 2, seed = NULL)
Arguments
truerate |
A nlevel-length vector of true rates for the DLTs. |
n |
Number of studies to simulate. |
r |
Integer, number of digits for percentages in output. |
seed |
If not empty, the seed to use for random generation. |
Value
data |
Summarized result in a "np1" view. |
norecommendation |
Percentage of studies with no recommendation for the MTD (in case of the first level is considered as toxic). |
mean.npt |
Mean number of enrolled patients. |
mean.ndlt |
Mean number of observed DLTs. |
mean.lastdose |
Mean last experimented dose level. |
Author(s)
Benjamin Esterni, Baboukar Mane. Unite de Biostatistique et de Methodologie, Institut Paoli-Calmettes, Marseille, France.
References
O'Quigley J., Pepe M., Fisher L. (1990). Continual Reassessment Method: a practical design for Phase I clinical trials in cancer. Biometrics 46, 33-48. <https://doi.org/10.2307/2531628>
O'Quigley J., Shen LZ. (1996). Continual Reassessment Method: a likelihood approach. Biometrics 52, 673-684. <https://doi.org/10.2307/2532905>
Paoletti X., Kramar A. (2009). A comparison of model choices for the Continual Reassessment Method in phase I cancer trials. Statistics in Medecine 28, 3012-3028. <https://doi.org/10.1002/sim.3682>
Chamorey Emmanuel. (2009). Methodologie des essais de phase precoce en cancerologie: evolution des schemas et apport de la pharmacologie. These.
Garret-Mayer Elizabeth. (2006). The Continual Reassessment Method for dose-finding studies: a tutorial. Clinical Trials: 57-71. <https://doi.org/10.1191/1740774506cn134oa>
See Also
Examples
ssim3p3(c(0.1,0.2,0.25,0.35),100)
Simulation of n dose-escalation study with the Continual Reassessment Method
Description
The ssimCrm function simulates n dose-escalation study with the CRM and provides summarized results.
Usage
ssimCrm(prior, n, firstdose = NA, truerate = prior, cohortsize = 3, target = 1/3,
nptmax = 24, nmaxmtd = nptmax, nmaxdose = nptmax, sd = 1.34, approach = "bayes",
method = "fpost", model = "power", nextlevel = "ntarget", upskipping = F,
downskipping = F, r = 2, seed = NULL)
Arguments
prior |
Numeric vector of prior DLT probabilities. |
n |
Number of studies to simulate. |
firstdose |
Integer representing the dose at which the first cohort will be treated. |
truerate |
A nlevel-length vector of true rates for the DLTs. |
cohortsize |
Size of the cohort. Default value = 3. |
target |
Target used for the MTD determination. |
nptmax |
Maximum number of patients to include in the study. |
nmaxmtd |
Maximum number of patients to be treated at the designated MTD. Assign a high value (=nptmax) to avoid such a stopping rule. |
nmaxdose |
Maximum number of patients to be treated at the same dose. Assign a high value (=nptmax) to avoid such a stopping rule. |
sd |
Standard deviation used in case of a normal distribution assumption for the parameter. |
approach |
Character indicating the estimation method: "bayes" (default value) for CRM or "mle" for CRML. |
model |
Character indicating the dose-DLT relationship model: "power", "tangent" or "logistic". |
method |
Estimation method for the posterior probabilities. "fpost" (default) estimates the mean of the posterior distribution of the paramater alpha (hat_alpha=E[alpha]) and uses it in psy(hat_alpha,...). "ppostp" and "pposts" directly estimate the mean of the posterior DLT probability. "ppostp" uses prior as singletons whereas "pposts" calculates appropriate singletons (see ail, ait or aip functions). |
nextlevel |
Character option used for determining the next dose level. "ntarget" (default) if the next level is chosen as the closest level to the desired target (may be higher than target). "utarget" if the next level is the closest level with the restriction to be lower than the target value. |
upskipping |
Boolean option used for determining the next dose level. If TRUE no level skip in escalation will be allowed. If FALSE (default) the level skips will be permitted. |
downskipping |
Boolean option used for determining the next dose level. If TRUE no level skip in desescalation will be allowed. If FALSE (default) the level skips will be permitted. |
r |
Integer, number of digits for percentages in output. |
seed |
If not empty, the seed to use for random generation. |
Value
data |
Summarized result in a "np1" view. |
norecommendation |
Percentage of studies with no recommendation for the MTD (in case of the first level is considered as toxic). |
mean.npt |
Mean number of enrolled patients. |
mean.ndlt |
Mean number of observed DLTs. |
mean.lastdose |
Mean last experimented dose level. |
mean.prob |
Mean of posterior DLT probabilities. |
Author(s)
Benjamin Esterni, Baboukar Mane. Unite de Biostatistique et de Methodologie, Institut Paoli-Calmettes, Marseille, France.
References
O'Quigley J., Pepe M., Fisher L. (1990). Continual Reassessment Method: a practical design for Phase I clinical trials in cancer. Biometrics 46, 33-48. <https://doi.org/10.2307/2531628>
O'Quigley J., Shen LZ. (1996). Continual Reassessment Method: a likelihood approach. Biometrics 52, 673-684. <https://doi.org/10.2307/2532905>
Paoletti X., Kramar A. (2009). A comparison of model choices for the Continual Reassessment Method in phase I cancer trials. Statistics in Medecine 28, 3012-3028. <https://doi.org/10.1002/sim.3682>
Chamorey Emmanuel. (2009). Methodologie des essais de phase precoce en cancerologie: evolution des schemas et apport de la pharmacologie. These.
Garret-Mayer Elizabeth. (2006). The Continual Reassessment Method for dose-finding studies: a tutorial. Clinical Trials: 57-71. <https://doi.org/10.1191/1740774506cn134oa>
See Also
Examples
ssimCrm(c(0.1,0.2,0.3,0.35,0.45),firstdose=1,target=0.33,n=100)
Dose escalation with the 3+3 design
Description
The function gives the next level to include patients following a 3+3 design. Needs an updated input dataframe with the CreData() structure.
Usage
troisPtrois(data = data, lastdose)
Arguments
data |
Study dataframe with CreData() structure. |
lastdose |
Integer representing the last experimented dose level. |
Value
nextdose |
An integer representing the next recommended dose to experiment. |
mtd |
If reached, an integer representing the MTD. |
Author(s)
Benjamin Esterni, Baboukar Mane. Unite de Biostatistique et de Methodologie, Institut Paoli-Calmettes, Marseille, France.
References
O'Quigley J., Pepe M., Fisher L. (1990). Continual Reassessment Method: a practical design for Phase I clinical trials in cancer. Biometrics 46, 33-48. <https://doi.org/10.2307/2531628>
O'Quigley J., Shen LZ. (1996). Continual Reassessment Method: a likelihood approach. Biometrics 52, 673-684. <https://doi.org/10.2307/2532905>
Paoletti X., Kramar A. (2009). A comparison of model choices for the Continual Reassessment Method in phase I cancer trials. Statistics in Medecine 28, 3012-3028. <https://doi.org/10.1002/sim.3682>
Chamorey Emmanuel. (2009). Methodologie des essais de phase precoce en cancerologie: evolution des schemas et apport de la pharmacologie. These.
Garret-Mayer Elizabeth. (2006). The Continual Reassessment Method for dose-finding studies: a tutorial. Clinical Trials: 57-71. <https://doi.org/10.1191/1740774506cn134oa>
See Also
Examples
# Study initialization
data<- CreData(5,c("5 mg/m2","7 mg/m2","10 mg/m2","15 mg/m2","20 mg/m2"))
data
# Three patients are treated at the dose 1, without any observed DLT:
data<- updata(data,lastdose=1,npt=3,ndlt=0)
data
# 3+3 design
troisPtrois(data,lastdose=1)
Update the CRM dataframe after new patients' collected data
Description
This function uptdates the CRM dataframe (result of the CreData routine) with new treated patients or observed DLTs.
Usage
updata(data = data, lastdose, npt, ndlt)
Arguments
data |
Dataframe to be updated. |
lastdose |
Integer representing the dose to be updated. |
npt |
Number of new treated patients. |
ndlt |
Number of DLTs among the npt patients. |
Value
Updated dataframe.
Author(s)
Benjamin Esterni, Baboukar Mane. Unite de Biostatistique et de Methodologie, Institut Paoli-Calmettes, Marseille, France.
References
O'Quigley J., Pepe M., Fisher L. (1990). Continual Reassessment Method: a practical design for Phase I clinical trials in cancer. Biometrics 46, 33-48. <https://doi.org/10.2307/2531628>
O'Quigley J., Shen LZ. (1996). Continual Reassessment Method: a likelihood approach. Biometrics 52, 673-684. <https://doi.org/10.2307/2532905>
Paoletti X., Kramar A. (2009). A comparison of model choices for the Continual Reassessment Method in phase I cancer trials. Statistics in Medecine 28, 3012-3028. <https://doi.org/10.1002/sim.3682>
Chamorey Emmanuel. (2009). Methodologie des essais de phase precoce en cancerologie: evolution des schemas et apport de la pharmacologie. These.
Garret-Mayer Elizabeth. (2006). The Continual Reassessment Method for dose-finding studies: a tutorial. Clinical Trials: 57-71. <https://doi.org/10.1191/1740774506cn134oa>
See Also
Examples
# Study initialization
data<- CreData(5,c("5 mg/m2","7 mg/m2","10 mg/m2","15 mg/m2","20 mg/m2"))
data
# Three patients are treated at the dose 1, without any observed DLT:
data<- updata(data,lastdose=1,npt=3,ndlt=0)
data