getAccrualTime: Get Accrual Time

In rpact: Confirmatory Adaptive Clinical Trial Design and Analysis

Get Accrual Time

Description

Returns an AccrualTime object that contains the accrual time and the accrual intensity.

Usage

getAccrualTime(
  accrualTime = NA_real_,
  ...,
  accrualIntensity = NA_real_,
  accrualIntensityType = c("auto", "absolute", "relative"),
  maxNumberOfSubjects = NA_real_
)

Arguments

accrualTime	The assumed accrual time intervals for the study, default is c(0, 12) (for details see getAccrualTime()).
...	Ensures that all arguments (starting from the "...") are to be named and that a warning will be displayed if unknown arguments are passed.
accrualIntensity	A numeric vector of accrual intensities, default is the relative intensity 0.1 (for details see getAccrualTime()).
accrualIntensityType	A character value specifying the accrual intensity input type. Must be one of "auto", "absolute", or "relative"; default is "auto", i.e., if all values are < 1 the type is "relative", otherwise it is "absolute".
maxNumberOfSubjects	The maximum number of subjects.

Value

Returns an AccrualTime object. The following generics (R generic functions) are available for this result object:

• names() to obtain the field names,

• print() to print the object,

• summary() to display a summary of the object,

• plot() to plot the object,

• as.data.frame() to coerce the object to a data.frame,

• as.matrix() to coerce the object to a matrix.

Staggered patient entry

accrualTime is the time period of subjects' accrual in a study. It can be a value that defines the end of accrual or a vector. In this case, accrualTime can be used to define a non-constant accrual over time. For this, accrualTime is a vector that defines the accrual intervals. The first element of accrualTime must be equal to 0 and, additionally, accrualIntensity needs to be specified. accrualIntensity itself is a value or a vector (depending on the length of accrualTime) that defines the intensity how subjects enter the trial in the intervals defined through accrualTime.

accrualTime can also be a list that combines the definition of the accrual time and accrual intensity (see below and examples for details).

If the length of accrualTime and the length of accrualIntensity are the same (i.e., the end of accrual is undefined), maxNumberOfSubjects > 0 needs to be specified and the end of accrual is calculated. In that case, accrualIntensity is the number of subjects per time unit, i.e., the absolute accrual intensity.

If the length of accrualTime equals the length of accrualIntensity - 1 (i.e., the end of accrual is defined), maxNumberOfSubjects is calculated if the absolute accrual intensity is given. If all elements in accrualIntensity are smaller than 1, accrualIntensity defines the relative intensity how subjects enter the trial. For example, accrualIntensity = c(0.1, 0.2) specifies that in the second accrual interval the intensity is doubled as compared to the first accrual interval. The actual (absolute) accrual intensity is calculated for the calculated or given maxNumberOfSubjects. Note that the default is accrualIntensity = 0.1 meaning that the absolute accrual intensity will be calculated.

How to get help for generic functions

Click on the link of a generic in the list above to go directly to the help documentation of the rpact specific implementation of the generic. Note that you can use the R function methods to get all the methods of a generic and to identify the object specific name of it, e.g., use methods("plot") to get all the methods for the plot generic. There you can find, e.g., plot.AnalysisResults and obtain the specific help documentation linked above by typing ?plot.AnalysisResults.

See Also

getNumberOfSubjects() for calculating the number of subjects at given time points.

Examples

## Not run: 
# Assume that in a trial the accrual after the first 6 months is doubled 
# and the total accrual time is 30 months.
# Further assume that a total of 1000 subjects are entered in the trial.
# The number of subjects to be accrued in the first 6 months and afterwards 
# is achieved through   
getAccrualTime(accrualTime = c(0, 6, 30), 
    accrualIntensity = c(0.1, 0.2), maxNumberOfSubjects = 1000)  

# The same result is obtained via the list based definition
getAccrualTime(list(
     "0 - <6"   = 0.1,
     "6 - <=30" = 0.2), 
     maxNumberOfSubjects = 1000)

# Calculate the end of accrual at given absolute intensity:
getAccrualTime(accrualTime = c(0, 6),
    accrualIntensity = c(18, 36), maxNumberOfSubjects = 1000)

# Via the list based definition this is
getAccrualTime(list(
    "0 - <6" = 18,
    ">=6" = 36), 
    maxNumberOfSubjects = 1000)

# You can use an accrual time object in getSampleSizeSurvival() or 
# getPowerSurvival().
# For example, if the maximum number of subjects and the follow up 
# time needs to be calculated for a given effect size: 
accrualTime = getAccrualTime(accrualTime = c(0, 6, 30), 
    accrualIntensity = c(0.1, 0.2))
getSampleSizeSurvival(accrualTime = accrualTime, pi1 = 0.4, pi2 = 0.2)

# Or if the power and follow up time needs to be calculated for given 
# number of events and subjects:
accrualTime = getAccrualTime(accrualTime = c(0, 6, 30),
    accrualIntensity = c(0.1, 0.2), maxNumberOfSubjects = 110)
getPowerSurvival(accrualTime = accrualTime, pi1 = 0.4, pi2 = 0.2, 
maxNumberOfEvents = 46)

# How to show accrual time details

# You can use a sample size or power object as argument for the function 
# getAccrualTime():
sampleSize <- 
getSampleSizeSurvival(accrualTime = c(0, 6), accrualIntensity = c(22, 53),  
    lambda2 = 0.05, hazardRatio = 0.8, followUpTime = 6)
sampleSize
accrualTime <- getAccrualTime(sampleSize)
accrualTime

## End(Not run)

rpact documentation built on July 9, 2023, 6:30 p.m.