Package 'PRIMEplus' reference manual

Title:	Study Design for Immunotherapy Clinical Trials
Description:	Perform sample size, power calculation and subsequent analysis for Immuno-oncology (IO) trials composed of responders and non-responders.
Authors:	Zhenzhen Xu [aut], Yongsoek Park [aut], Zhu Bin [aut], Bill Wheeler [cre]
Maintainer:	Bill Wheeler <[email protected]>
License:	GPL-2
Version:	1.0.16
Built:	2024-11-06 02:39:40 UTC
Source:	https://github.com/cran/PRIMEplus

Study design for immunotherapy clinical trials

Description

Perform sample size, power calculation and subsequent analysis for Immuno-oncology (IO) trials composed of responders and nonresponders.

Details

This package is an extension of the Immunotherapy.Design R package but allows for response categories of more than two categories among the treatment population, such as complete responders, partial responders, as well as non-responders.

Author(s)

Zhenzhen Xu <[email protected]>, Yongsoek Park <[email protected]> and Bin Zhu <[email protected]>

References

Xu, Z., Park, Y., Liu, K. and Zhu, B. Treating non-responders: pitfalls and implications for cancer immunotherapy trial design. Journal of Hematology & Oncology 13, 20 (2020).

Xu, Z., Zhu, B. and Park, Y. Designing immuno-oncology clinical trials composed of responders and nonresponders. Statistics in Medicine. (Under Revision).

Data for examples

Description

Data for examples.

Details

A data frame used in the examples.

Examples


 data(data, package="PRIMEplus")

 # Display some of the data
 data[1:5, ]
data(data, package="PRIMEplus")

 # Display some of the data
 data[1:5, ]

Simulated data

Description

Generate simulated data

Usage

 
generate_data(N=500, rand_ratio=0.5, effect_p=0.6, enroll_rate=380*0.25/6, 
              lambda1=0.117, HR=0.5, tau=12*5, t1=1)
generate_data(N=500, rand_ratio=0.5, effect_p=0.6, enroll_rate=380*0.25/6, 
              lambda1=0.117, HR=0.5, tau=12*5, t1=1)

Arguments

`N`	Maximum sample size
`rand_ratio`	Allocation ratio
`effect_p`	Vector for proportion of responders in the treatment arm at baseline (see details)
`enroll_rate`	Enrollment rate in subjects per month
`lambda1`	Baseline hazard in terms of months
`HR`	Vector of hazard ratios for responders against controls (see details)
`tau`	Total study duration
`t1`	Delayed duration

Details

The length and order of effect_p must be the same as HR. Both of these vectors should contain information only for the groups of responders. For example, if there are full responders and partial responders, then effect_p and HR would be vectors of length two.

Value

A data frame with columns:

Name	Description
id	id variable
trt	treatment allocation: 1 = treatment arm
D1	patient's response status for group 1
D2	patient's response status for group 2
Dm	patient's response status for non-responders
tau	total study duration
enroll_time	patients' enrollment times
time_to_event	patients' event times
event_status	censoring indicator
X	observational time
t1	delayed duration

Author(s)

Zhenzhen Xu <[email protected]>, Yongsoek Park <[email protected]> and Bin Zhu <[email protected]>

Examples

  data <- generate_data()
  data[1:5, ]
data <- generate_data()
  data[1:5, ]

Compute initial estimates for the baseline hazard

Description

Calls the coxph function to compute initial estimates for the baseline hazard

Usage

 getHazard(time, treatment, event_status, t.fail.o=NULL)
getHazard(time, treatment, event_status, t.fail.o=NULL)

Arguments

`time`	Vector of times.
`treatment`	Binary vector of treatments (1=subject received treatment).
`event_status`	Binary vector of event status (1=subject experienced an event).
`t.fail.o`	NULL or vector of event times.

Value

List containing the baseline hazards ordered by the event times.

Author(s)

Zhenzhen Xu <[email protected]> and Bin Zhu <[email protected]>

Examples

  data(data, package="PRIMEplus")
  lambda0 <- getHazard(data[, "X"], data[, "trt"], data[, "event_status"])$hazard
  lambda0[1:10]
data(data, package="PRIMEplus")
  lambda0 <- getHazard(data[, "X"], data[, "trt"], data[, "event_status"])$hazard
  lambda0[1:10]

EM algorithm

Description

EM algorithm

Usage

 PRIMEplus.EM(data, effect_p, beta0, time.var="X", trt.var="trt", 
        status.var="event_status", id.var="id", t1=1, lambda0=NULL, 
        stopTol=1e-4, maxiter=100000, print=0)
PRIMEplus.EM(data, effect_p, beta0, time.var="X", trt.var="trt", 
        status.var="event_status", id.var="id", t1=1, lambda0=NULL, 
        stopTol=1e-4, maxiter=100000, print=0)

Arguments

`data`	Data frame or matrix containing a time-to-event variable (`time.var`), a treatment variable (`trt.var`), and a censoring variable (`status.var`).
`effect_p`	Vector of proportions for groups of responders in the treatment arm at baseline (see details).
`beta0`	Vector or matrix of initial estimates for the log-hazard ratios (see details).
`time.var`	Time-to-event variable name in `data`. The default is "X".
`trt.var`	Binary treatment variable name in `data` coded as 0 for controls and 1 for subjects that received treatment.
`status.var`	Name of the binary censoring variable in `data` coded as 0 for censored subjects and 1 for subjects that experienced an event.
`id.var`	NULL or subject id variable in `data`. The default is "id".
`t1`	Delayed duration. The default is 1.
`lambda0`	NULL or vector of initial estimates for the baseline hazards corrsponding to the ordered event times.
`stopTol`	Stopping tolerance. The default is 1e-4.
`maxiter`	Maximum number of iterations. The default is 100000.
`print`	0-2 to print information. Larger values will print more information. The default is 0.

Details

The EM algorithm is sensitive to the initial values of the log-hazard ratios (beta0), so different initial estimates should be tried to ensure the maximum log-likelihood is obtained. Thus, beta0 can be a vector or matrix, where in the case of a matrix, each row corresponds to a different set of intial estimates. Each set of initial estimates must contain distinct non-zero values. The length and order of effect_p must be the same as the columns of beta0. Both of these should contain information only for the groups of responders. For example, if there are full responders and partial responders, then effect_p would be a vector of length two, and beta0 would be a vector of length two or a matrix with two columns.

Value

A list containing the objects:

Name	Description
converged	TRUE if EM algorithm converged
beta	final log(hazard ratio) estimates of responders versus controls
lambda	final estimates of baseline hazards
probResponder	estimated probability of a subject being a responder
loglike	log-likelihood value at the final estimates
loglike.marg	marginal log-likelihood value at the final estimates

Author(s)

Zhenzhen Xu <[email protected]>, Yongsoek Park <[email protected]> and Bin Zhu <[email protected]>

Examples

  data(data, package="PRIMEplus")
  effp  <- c(0.3, 0.3)
  beta0 <- c(log(0.4), log(0.6))
  ret   <- PRIMEplus.EM(data, effp, beta0)
  ret$beta
data(data, package="PRIMEplus")
  effp  <- c(0.3, 0.3)
  beta0 <- c(log(0.4), log(0.6))
  ret   <- PRIMEplus.EM(data, effp, beta0)
  ret$beta

Likelihood Ratio Test

Description

PRIMEplus likelihood ratio test

Usage

 PRIMEplus.LRT(data, effect_p, beta0, time.var="X", trt.var="trt", 
       status.var="event_status", id.var="id", t1=1, lambda0=NULL, 
       stopTol=1e-4, maxiter=100000, print=0)
PRIMEplus.LRT(data, effect_p, beta0, time.var="X", trt.var="trt", 
       status.var="event_status", id.var="id", t1=1, lambda0=NULL, 
       stopTol=1e-4, maxiter=100000, print=0)

Arguments

`data`	Data frame or matrix containing a time-to-event variable (`time.var`), a treatment variable (`trt.var`), and a censoring variable (`status.var`).
`effect_p`	Vector of proportions for groups of responders in the treatment arm at baseline (see details).
`beta0`	Vector or matrix of initial estimates for the log-hazard ratios (see details).
`time.var`	Time-to-event variable name in `data`. The default is "X".
`trt.var`	Binary treatment variable name in `data` coded as 0 for controls and 1 for subjects that received treatment.
`status.var`	Name of the binary censoring variable in `data` coded as 0 for censored subjects and 1 for subjects that experienced an event.
`id.var`	NULL or subject id variable in `data`. The default is "id".
`t1`	Delayed duration. The default is 1.
`lambda0`	NULL or vector of initial estimates for the baseline hazards corrsponding to the ordered event times.
`stopTol`	Stopping tolerance. The default is 1e-4.
`maxiter`	Maximum number of iterations. The default is 100000.
`print`	0-2 to print information. Larger values will print more information. The default is 0.

Details

Value

A list containing the objects:

Name	Description
converged	TRUE if EM algorithm converged
beta	final log(hazard ratio) estimates of responders versus controls
lambda	final estimates of baseline hazards
probResponder	estimated probability of a subject being a responder
loglike	log-likelihood value at the final estimates
loglike.marg	marginal log-likelihood value at the final estimates
loglike.marg.0	marginal log-likelihood value under the null hypothesis
LRT	likelihood-ratio test statistic
pvalue	p-value of the likelihood ratio test

Author(s)

Zhenzhen Xu <[email protected]>, Yongsoek Park <[email protected]> and Bin Zhu <[email protected]>

Examples

  data(data, package="PRIMEplus")
  effp  <- c(0.3, 0.3)
  beta0 <- c(log(0.4), log(0.6))
  ret   <- PRIMEplus.LRT(data, effp, beta0)
  ret$LRT 
  ret$pvalue
data(data, package="PRIMEplus")
  effp  <- c(0.3, 0.3)
  beta0 <- c(log(0.4), log(0.6))
  ret   <- PRIMEplus.LRT(data, effp, beta0)
  ret$LRT 
  ret$pvalue

Power

Description

Compute the power using LRT Re-randomization test

Usage

 
PRIMEplus.Power(nmax=500, rand_ratio=0.5, effect_p=0.6, 
    enroll_rate=380*0.25/6, lambda1=0.117, HR=0.5, tau=12*5, t1=1, 
    maxiter=100000, stopTol=1e-4, alpha=0.05, num_rand=1000, nsim=10000, 
    print=0, min.sample.size=50, min.n.event=5, min.per.trt=0.25)
PRIMEplus.Power(nmax=500, rand_ratio=0.5, effect_p=0.6, 
    enroll_rate=380*0.25/6, lambda1=0.117, HR=0.5, tau=12*5, t1=1, 
    maxiter=100000, stopTol=1e-4, alpha=0.05, num_rand=1000, nsim=10000, 
    print=0, min.sample.size=50, min.n.event=5, min.per.trt=0.25)

Arguments

`nmax`	Sample size
`rand_ratio`	Probability of assignment to treatment arm
`effect_p`	Vector for proportion of responders in the treatment arm at baseline (see details)
`enroll_rate`	Enrollment rate in subjects per month
`lambda1`	Baseline hazard in terms of months
`HR`	Vector of hazard ratios for responders against controls (see details)
`tau`	Total study duration
`t1`	Delayed duration in months
`maxiter`	Maximum number of iterations in the EM algorithm. The default is 100000.
`stopTol`	Stopping tolerance in the EM algorithm. The default is 1e-4.
`alpha`	Significance level. The default is 0.05.
`num_rand`	The number of replications in the re-randomization test. The default is 1000.
`nsim`	The number of simulations. The default is 1000.
`print`	0 or 1 to print information. The default is 0.
`min.sample.size`	Minimum sample size. The default is 50.
`min.n.event`	Minimum number of events. The default is 5.
`min.per.trt`	Minimum proportion of controls and treated subjects. The default is 0.25.

Details

For each simulation, a simulated data set is created from the generate_data function and then an estimated p-value is computed by calling PRIMEplus.ReRand.LRT. The power is calculated as the proportion of iterations whose estimated p-value was less than or equal to alpha.

Value

A list containing the power and the number of simulated datasets used in the calculation.

Author(s)

Zhenzhen Xu <[email protected]>, Yongsoek Park <[email protected]> and Bin Zhu <[email protected]>

Randomization Test

Description

PRIMEplus randomization likelihood ratio test

Usage

 PRIMEplus.ReRand.LRT(data, effect_p, beta0, time.var="X", trt.var="trt", 
                             status.var="event_status", id.var="id", t1=1, lambda0=NULL, 
                             stopTol=1e-4, maxiter=100000, print=0, num_rand=1000)
PRIMEplus.ReRand.LRT(data, effect_p, beta0, time.var="X", trt.var="trt", 
                             status.var="event_status", id.var="id", t1=1, lambda0=NULL, 
                             stopTol=1e-4, maxiter=100000, print=0, num_rand=1000)

Arguments

`data`	Data frame or matrix containing a time-to-event variable (`time.var`), a treatment variable (`trt.var`), and a censoring variable (`status.var`).
`effect_p`	Vector of proportions for groups of responders in the treatment arm at baseline (see details).
`beta0`	Vector or matrix of initial estimates for the log-hazard ratios (see details).
`time.var`	Time-to-event variable name in `data`. The default is "X".
`trt.var`	Binary treatment variable name in `data` coded as 0 for controls and 1 for subjects that received treatment.
`status.var`	Name of the binary censoring variable in `data` coded as 0 for censored subjects and 1 for subjects that experienced an event.
`id.var`	NULL or subject id variable in `data`. The default is "id".
`t1`	Delayed duration. The default is 1.
`lambda0`	NULL or vector of initial estimates for the baseline hazards corrsponding to the ordered event times.
`stopTol`	Stopping tolerance. The default is 1e-4.
`maxiter`	Maximum number of iterations. The default is 100000.
`print`	0-2 to print information. Larger values will print more information. The default is 0.
`num_rand`	The number of randomizations. The default is 1000.

Details

Value

A list containing the objects:

Name	Description
pvalue.LRT	p-value of the randomization test based on the likelihood ratio test
pvalue.loglike.marg	p-value of the randomization test based on the marginal likelihood
n.randomizations	the number of randomizations that the p-values are based on

Author(s)

Zhenzhen Xu <[email protected]>, Yongsoek Park <[email protected]> and Bin Zhu <[email protected]>

Examples

  data(data, package="PRIMEplus")
  effp  <- c(0.3, 0.3)
  beta0 <- c(log(0.4), log(0.6))
  ret   <- PRIMEplus.ReRand.LRT(data, effp, beta0, num_rand=100)
  ret
data(data, package="PRIMEplus")
  effp  <- c(0.3, 0.3)
  beta0 <- c(log(0.4), log(0.6))
  ret   <- PRIMEplus.ReRand.LRT(data, effp, beta0, num_rand=100)
  ret

Sample Size

Description

Compute the sample size for a given power

Usage

 
PRIMEplus.SampleSize(power=0.8, rand_ratio=0.5, effect_p=0.6, enroll_rate=380*0.25/6, 
              lambda1=0.117, HR=0.5, tau=12*5, t1=1, maxiter=100000, stopTol=1e-4,
              alpha=0.05, num_rand=1000, nsim=10000, min.N=100, max.N=700,
              tol.power=0.01, tol.N=1, print=1,
              min.sample.size=50, min.n.event=5, min.per.trt=0.25)
PRIMEplus.SampleSize(power=0.8, rand_ratio=0.5, effect_p=0.6, enroll_rate=380*0.25/6, 
              lambda1=0.117, HR=0.5, tau=12*5, t1=1, maxiter=100000, stopTol=1e-4,
              alpha=0.05, num_rand=1000, nsim=10000, min.N=100, max.N=700,
              tol.power=0.01, tol.N=1, print=1,
              min.sample.size=50, min.n.event=5, min.per.trt=0.25)

Arguments

`power`	The desired power. The default is 0.8.
`rand_ratio`	Allocation ratio
`effect_p`	Vector for proportion of responders in the treatment arm at baseline (see details)
`enroll_rate`	Enrollment rate in subjects per month
`lambda1`	Baseline hazard in terms of months
`HR`	Vector of hazard ratios for responders against controls (see details)
`tau`	Total study duration
`t1`	Delayed duration
`maxiter`	Maximum number of iterations in the EM algorithm. The default is 100000.
`stopTol`	Stopping tolerance in the EM algorithm. The default is 1e-4.
`alpha`	Significance level. The default is 0.05.
`num_rand`	Number of replications in the re-randomization test. The default is 1000.
`nsim`	Number of simulations in computing power (see Details). The default is 10000.
`min.N`	Lower bound for the sample size. The default is 100.
`max.N`	Upper bound for the sample size. The default is 700.
`tol.power`	Stopping tolerance for the power. The default is 0.01.
`tol.N`	Stopping tolerance for the sample size. The default is 1.
`print`	0 or 1 to print information. The default is 1.
`min.sample.size`	Minimum sample size. The default is 50.
`min.n.event`	Minimum number of events. The default is 5.
`min.per.trt`	Minimum proportion of controls and treated subjects. The default is 0.25.

Details

This uses a bisection method to estimate the sample size. At each iteration, the estimated power power_est is computed using PRIMEplus.Power for a given sample size holding all other parameters fixed. The algorithm terminates when abs(power - power_est) <= tol.power or when the length of the estimated interval containing the sample size is less than or equal to tol.N.

NOTE:
It is important to note that the power for a given sample size is estimated by running a simulation. Thus, by setting a different seed, a different result may be returned. Therefore, to ensure a more precise estimated sample size, set the option nsim to a large value and/or run this function several times by setting different seeds and examine the distribution of returned sample sizes.

Value

A list containing the sample size and power.

Author(s)

Zhenzhen Xu <[email protected]>, Yongsoek Park <[email protected]> and Bin Zhu <[email protected]>

Package 'PRIMEplus'

Help Index

Study design for immunotherapy clinical trials

Description

Details

Author(s)

References

Data for examples

Description

Details

Examples

Simulated data

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Compute initial estimates for the baseline hazard

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

EM algorithm

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Likelihood Ratio Test

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Power

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Randomization Test

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Sample Size

Description

Usage

Arguments

Details

Value

Author(s)

See Also