getSimulationRates: Get Simulation Rates
In rpact: Confirmatory Adaptive Clinical Trial Design and Analysis

View source: R/f_simulation_base_rates.R

getSimulationRates

R Documentation

Get Simulation Rates

Description

Returns the simulated power, stopping probabilities, conditional power, and expected sample size for testing rates in a one or two treatment groups testing situation.

Usage

getSimulationRates(
  design = NULL,
  ...,
  groups = 2L,
  normalApproximation = TRUE,
  riskRatio = FALSE,
  thetaH0 = ifelse(riskRatio, 1, 0),
  pi1 = seq(0.2, 0.5, 0.1),
  pi2 = NA_real_,
  plannedSubjects = NA_real_,
  directionUpper = TRUE,
  allocationRatioPlanned = NA_real_,
  minNumberOfSubjectsPerStage = NA_real_,
  maxNumberOfSubjectsPerStage = NA_real_,
  conditionalPower = NA_real_,
  pi1H1 = NA_real_,
  pi2H1 = NA_real_,
  maxNumberOfIterations = 1000L,
  seed = NA_real_,
  calcSubjectsFunction = NULL,
  showStatistics = FALSE
)

Arguments

`design`	The trial design. If no trial design is specified, a fixed sample size design is used. In this case, Type I error rate `alpha`, Type II error rate `beta`, `twoSidedPower`, and `sided` can be directly entered as argument where necessary.
`...`	Ensures that all arguments (starting from the "...") are to be named and that a warning will be displayed if unknown arguments are passed.
`groups`	The number of treatment groups (1 or 2), default is `2`.
`normalApproximation`	The type of computation of the p-values. Default is `FALSE` for testing means (i.e., the t test is used) and `TRUE` for testing rates and the hazard ratio. For testing rates, if `normalApproximation = FALSE` is specified, the binomial test (one sample) or the exact test of Fisher (two samples) is used for calculating the p-values. In the survival setting `normalApproximation = FALSE` has no effect.
`riskRatio`	If `TRUE`, the design characteristics for one-sided testing of H0: `pi1 / pi2 = thetaH0` are simulated, default is `FALSE`.
`thetaH0`	The null hypothesis value, default is `0` for the normal and the binary case (testing means and rates, respectively), it is `1` for the survival case (testing the hazard ratio). For non-inferiority designs, `thetaH0` is the non-inferiority bound. That is, in case of (one-sided) testing of means: a value `!= 0` (or a value `!= 1` for testing the mean ratio) can be specified. rates: a value `!= 0` (or a value `!= 1` for testing the risk ratio `pi1 / pi2`) can be specified. survival data: a bound for testing H0: `hazard ratio = thetaH0 != 1` can be specified. For testing a rate in one sample, a value `thetaH0` in (0, 1) has to be specified for defining the null hypothesis H0: `pi = thetaH0`.
`pi1`	A numeric value or vector that represents the assumed probability in the active treatment group if two treatment groups are considered, or the alternative probability for a one treatment group design, default is `seq(0.2, 0.5, 0.1)` (power calculations and simulations) or `seq(0.4, 0.6, 0.1)` (sample size calculations).
`pi2`	A numeric value that represents the assumed probability in the reference group if two treatment groups are considered, default is `0.2`.
`plannedSubjects`	`plannedSubjects` is a numeric vector of length `kMax` (the number of stages of the design) that determines the number of cumulated (overall) subjects when the interim stages are planned. For two treatment arms, it is the number of subjects for both treatment arms. For multi-arm designs, `plannedSubjects` refers to the number of subjects per selected active arm.
`directionUpper`	Logical. Specifies the direction of the alternative, only applicable for one-sided testing; default is `TRUE` which means that larger values of the test statistics yield smaller p-values.
`allocationRatioPlanned`	The planned allocation ratio `n1 / n2` for a two treatment groups design, default is `1`. For multi-arm designs, it is the allocation ratio relating the active arm(s) to the control. For simulating means and rates for a two treatment groups design, it can be a vector of length kMax, the number of stages. It can be a vector of length kMax, too, for multi-arm and enrichment designs. In these cases, a change of allocating subjects to treatment groups over the stages can be assessed.
`minNumberOfSubjectsPerStage`	When performing a data driven sample size recalculation, the numeric vector `minNumberOfSubjectsPerStage` with length kMax determines the minimum number of subjects per stage (i.e., not cumulated), the first element is not taken into account. For two treatment arms, it is the number of subjects for both treatment arms. For multi-arm designs `minNumberOfSubjectsPerStage` refers to the minimum number of subjects per selected active arm.
`maxNumberOfSubjectsPerStage`	When performing a data driven sample size recalculation, the numeric vector `maxNumberOfSubjectsPerStage` with length kMax determines the maximum number of subjects per stage (i.e., not cumulated), the first element is not taken into account. For two treatment arms, it is the number of subjects for both treatment arms. For multi-arm designs `maxNumberOfSubjectsPerStage` refers to the maximum number of subjects per selected active arm.
`conditionalPower`	If `conditionalPower` together with `minNumberOfSubjectsPerStage` and `maxNumberOfSubjectsPerStage` (or `minNumberOfEventsPerStage` and `maxNumberOfEventsPerStage` for survival designs) is specified, a sample size recalculation based on the specified conditional power is performed. It is defined as the power for the subsequent stage given the current data. By default, the conditional power will be calculated under the observed effect size. Optionally, you can also specify `thetaH1` and `stDevH1` (for simulating means), `pi1H1` and `pi2H1` (for simulating rates), or `thetaH1` (for simulating hazard ratios) as parameters under which it is calculated and the sample size recalculation is performed.
`pi1H1`	If specified, the assumed probability in the active treatment group if two treatment groups are considered, or the assumed probability for a one treatment group design, for which the conditional power was calculated.
`pi2H1`	If specified, the assumed probability in the reference group if two treatment groups are considered, for which the conditional power was calculated.
`maxNumberOfIterations`	The number of simulation iterations, default is `1000`. Must be a positive integer of length 1.
`seed`	The seed to reproduce the simulation, default is a random seed.
`calcSubjectsFunction`	Optionally, a function can be entered that defines the way of performing the sample size recalculation. By default, sample size recalculation is performed with conditional power and specified `minNumberOfSubjectsPerStage` and `maxNumberOfSubjectsPerStage` (see details and examples).
`showStatistics`	Logical. If `TRUE`, summary statistics of the simulated data are displayed for the `print` command, otherwise the output is suppressed, default is `FALSE`.

Details

At given design the function simulates the power, stopping probabilities, conditional power, and expected sample size at given number of subjects and parameter configuration. Additionally, an allocation ratio = n1/n2 can be specified where n1 and n2 are the number of subjects in the two treatment groups.

The definition of pi1H1 and/or pi2H1 makes only sense if kMax > 1 and if conditionalPower, minNumberOfSubjectsPerStage, and maxNumberOfSubjectsPerStage (or calcSubjectsFunction) are defined.

calcSubjectsFunction
This function returns the number of subjects at given conditional power and conditional critical value for specified testing situation. The function might depend on variables stage, riskRatio, thetaH0, groups, plannedSubjects, sampleSizesPerStage, directionUpper, allocationRatioPlanned, minNumberOfSubjectsPerStage, maxNumberOfSubjectsPerStage, conditionalPower, conditionalCriticalValue, overallRate, farringtonManningValue1, and farringtonManningValue2. The function has to contain the three-dots argument '...' (see examples).

Value

Returns a SimulationResults object. The following generics (R generic functions) are available for this 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.

Simulation Data

The summary statistics "Simulated data" contains the following parameters: median range; mean +/-sd

$show(showStatistics = FALSE) or $setShowStatistics(FALSE) can be used to disable the output of the aggregated simulated data.

Example 1:
simulationResults <- getSimulationRates(plannedSubjects = 40)
simulationResults$show(showStatistics = FALSE)

Example 2:
simulationResults <- getSimulationRates(plannedSubjects = 40)
simulationResults$setShowStatistics(FALSE)
simulationResults

getData() can be used to get the aggregated simulated data from the object as data.frame. The data frame contains the following columns:

iterationNumber: The number of the simulation iteration.
stageNumber: The stage.
pi1: The assumed or derived event rate in the treatment group (if available).
pi2: The assumed or derived event rate in the control group (if available).
numberOfSubjects: The number of subjects under consideration when the (interim) analysis takes place.
rejectPerStage: 1 if null hypothesis can be rejected, 0 otherwise.
futilityPerStage: 1 if study should be stopped for futility, 0 otherwise.
testStatistic: The test statistic that is used for the test decision, depends on which design was chosen (group sequential, inverse normal, or Fisher combination test)'
testStatisticsPerStage: The test statistic for each stage if only data from the considered stage is taken into account.
overallRate1: The cumulative rate in treatment group 1.
overallRate2: The cumulative rate in treatment group 2.
stagewiseRates1: The stage-wise rate in treatment group 1.
stagewiseRates2: The stage-wise rate in treatment group 2.
sampleSizesPerStage1: The stage-wise sample size in treatment group 1.
sampleSizesPerStage2: The stage-wise sample size in treatment group 2.
trialStop: TRUE if study should be stopped for efficacy or futility or final stage, FALSE otherwise.
conditionalPowerAchieved: The conditional power for the subsequent stage of the trial for selected sample size and effect. The effect is either estimated from the data or can be user defined with pi1H1 and pi2H1.

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.

Examples

# Fixed sample size design (two groups) with total sample 
# size 120, pi1 = (0.3,0.4,0.5,0.6) and pi2 = 0.3
getSimulationRates(pi1 = seq(0.3, 0.6, 0.1), pi2 = 0.3, 
    plannedSubjects = 120, maxNumberOfIterations = 10)
## Not run: 
# Increase number of simulation iterations and compare results with power calculator
getSimulationRates(pi1 = seq(0.3, 0.6, 0.1), pi2 = 0.3, 
    plannedSubjects = 120, maxNumberOfIterations = 50)
getPowerRates(pi1 = seq(0.3, 0.6, 0.1), pi2 = 0.3, maxNumberOfSubjects = 120)

# Do the same for a two-stage Pocock inverse normal group sequential 
# design with non-binding futility stops
designIN <- getDesignInverseNormal(typeOfDesign = "P", futilityBounds = c(0))
getSimulationRates(designIN, pi1 = seq(0.3, 0.6, 0.1), pi2 = 0.3, 
    plannedSubjects = c(40, 80), maxNumberOfIterations = 50)
getPowerRates(designIN, pi1 = seq(0.3, 0.6, 0.1), pi2 = 0.3, maxNumberOfSubjects = 80)

# Assess power and average sample size if a sample size reassessment is 
# foreseen at conditional power 80% for the subsequent stage (decrease and increase) 
# based on observed overall (cumulative) rates and specified minNumberOfSubjectsPerStage 
# and maxNumberOfSubjectsPerStage

# Do the same under the assumption that a sample size increase only takes place 
# if the rate difference exceeds the value 0.1 at interim. For this, the sample 
# size recalculation method needs to be redefined:  
mySampleSizeCalculationFunction <- function(..., stage,
        plannedSubjects,
        minNumberOfSubjectsPerStage,
        maxNumberOfSubjectsPerStage,
        conditionalPower,
        conditionalCriticalValue,
        overallRate) {
    if (overallRate[1] - overallRate[2] < 0.1) {
        return(plannedSubjects[stage] - plannedSubjects[stage - 1]) 
    } else {
        rateUnderH0 <- (overallRate[1] + overallRate[2]) / 2 
        stageSubjects <- 2 * (max(0, conditionalCriticalValue * 
            sqrt(2 * rateUnderH0 * (1 - rateUnderH0)) + 
            stats::qnorm(conditionalPower) * sqrt(overallRate[1] * 
            (1 - overallRate[1]) + overallRate[2] * (1 - overallRate[2]))))^2 /
            (max(1e-12, (overallRate[1] - overallRate[2])))^2
        stageSubjects <- ceiling(min(max(
            minNumberOfSubjectsPerStage[stage], 
            stageSubjects), maxNumberOfSubjectsPerStage[stage]))
        return(stageSubjects)
    }
}
getSimulationRates(designIN, pi1 = seq(0.3, 0.6, 0.1), pi2 = 0.3, 
    plannedSubjects = c(40, 80), minNumberOfSubjectsPerStage = c(40, 20), 
    maxNumberOfSubjectsPerStage = c(40, 160), conditionalPower = 0.8, 
    calcSubjectsFunction = mySampleSizeCalculationFunction, maxNumberOfIterations = 50)

## End(Not run)

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