Integration Point: Analysis - Continuous Outcome, Multiple Arm, Fixed Sample Design • CyneRgy

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Input Variables

When creating a custom R script, you can optionally use specific variables provided by East Horizon’s engine itself. These variables are automatically available and do not need to be set by the user, except for the UserParam variable. Refer to the table below for the variables that are available for this integration point, outcome, and study objective.

Variable	Type	Description
SimData	Data Frame	Subject data generated in current simulation, one row per subject. To access these variables in your R code, use the syntax: `SimData$NameOfTheVariable`, replacing `NameOfTheVariable` with the appropriate variable name. Refer to the table below for more information.
DesignParam	List	Input parameters which may be needed to compute test statistic and perform test. To access these variables in your R code, use the syntax: `DesignParam$NameOfTheVariable`, replacing `NameOfTheVariable` with the appropriate variable name. Refer to the table below for more information.
LookInfo	List	Input parameters related to multiple looks. Empty when the statistical design is Fixed Sample. However, it is used in the functions `CyneRgy::GetDecisionString` and `CyneRgy::GetDecision` to get the decision value. See below for more information.
UserParam	List	Contains all user-defined parameters specified in the East Horizon interface (refer to the Instructions section). To access these parameters in your R code, use the syntax: `UserParam$NameOfTheVariable`, replacing `NameOfTheVariable` with the appropriate parameter name.

Variables of SimData

The variables in SimData are generated during data generation, and depend on the current simulation. Some common and useful variables are:

Variable	Type	Description
SimData$ArrivalTime	Vector of Numeric	Vector of length equal to the number of subjects, containing the generated arrival times for all subjects.
SimData$TreatmentID	Vector of Integer	Vector of length equal to the number of subjects, containing the allocation indices for all subjects: – `0`: Control arm. – `1`: First experimental arm. – etc.
SimData$Response	Vector of Numeric	Vector of length equal to the number of subjects, containing the generated responses for all subjects.
SimData$CensorIndOrg	Vector of Integer	Vector of length equal to the number of subjects, containing the generated censor indicator values for all subjects: – `0`: Dropout. – `1`: Completer.

Variables of DesignParam

Variable	Type	Description
DesignParam$Alpha	Numeric	Type I Error (for one-sided tests).
DesignParam$TrialType	Integer	Trial Type: – `0`: Superiority.
DesignParam$TestType	Integer	Test Type: – `0`: One-sided.
DesignParam$TailType	Integer	Nature of critical region: – `0`: Left-tailed. – `1`: Right-tailed.
DesignParam$InitialAllocInfo	Vector of Numeric	Vector of length equal to the number of treatment arms, containing the ratios of the treatment group sample sizes to control group sample size.
DesignParam$CriticalPoint	Numeric	Critical value (for one-sided tests).
DesignParam$SampleSize	Integer	Sample size of the trial.
DesignParam$MaxCompleters	Integer	Maximum number of completers.
DesignParam$RespLag	Numeric	Follow-up duration.
DesignParam$MultAdjMethod	Integer	Multiple comparison procedure: – `0`: Bonferroni. – `1`: Sidak (not available in East Horizon Explore). – `2`: Weighted Bonferroni<.br>– `3`: Holm’s Step Down (not available in East Horizon Explore). – `4`: Hochberg’s Step Up. – `5`: Hommel’s Step Up (not available in East Horizon Explore). – `6`: Fixed Sequence. – `7`: Fallback. – `8`: Dunnett’s Single Step. – `9`: Dunnett’s Step Down (not available in East Horizon Explore). – `10`: Dunnett’s Step Up (not available in East Horizon Explore).
DesignParam$NumTreatments	Integer	Number of treatment arms.
DesignParam$AlphaProp	Vector of Numeric	Vector of length `DesignParam$NumTreatments`, containing the proportion of Alpha for each treatment arm.
DesignParam$TestSeq	Vector of Integer	Vector of length `DesignParam$NumTreatments`, containing the test sequence for each comparison (each treatment arm).

Expected Output Variable

East Horizon expects an output of a specific type. Refer to the table below for the expected output for this integration point:

Type	Description
List	A named list containing `ErrorCode` and one of the following: `Decision`, `TestStat`, `AdjPVal`, `RawPVal`.

The output list can take one of these two forms.

Option 1 (Decision): Expected Members of the Output List

Members	Type	Description
Decision	Vector of Integer	Vector of length `DesignParam$NumTreatments`, containing the boundary crossing decision for each treatment arm: – `0`: No boundary crossed. – `1`: Lower efficacy boundary crossed. – `2`: Upper efficacy boundary crossed. – `4`: Equivalence boundary crossed (not available in East Horizon Explore). You can use the functions `CyneRgy::GetDecisionString` and `CyneRgy::GetDecision` to get the decision value. See the template below for the correct usage.
ErrorCode	Integer	Optional. Can be used to handle errors in your script: – `0`: No error. – `Positive Integer`: Nonfatal error, the current simulation will be aborted, but the next simulation will proceed. – `Negative Integer`: Fatal error, no further simulations will be attempted.

Option 2 (TestStat): Expected Members of the Output List

Members	Type	Description
TestStat	Vector of Numeric	Vector of length `DesignParam$NumTreatments`, containing the value of appropriate test statistic on Wald ﴾Z﴿ scale for each treatment arm.
ErrorCode	Integer	Optional. Can be used to handle errors in your script: – `0`: No error. – `Positive Integer`: Nonfatal error, the current simulation will be aborted, but the next simulation will proceed. – `Negative Integer`: Fatal error, no further simulations will be attempted.

As the design does not have any futility boundary, TestStat will be used to check for efficacy.

Option 3 (AdjPVal): Expected Members of the Output List

Members	Type	Description
AdjPVal	Vector of Numeric	Vector of length `DesignParam$NumTreatments`, containing the p-values computed from test statistics and adjusted for multiple comparison procedures for each treatment arm.
ErrorCode	Integer	Optional. Can be used to handle errors in your script: – `0`: No error. – `Positive Integer`: Nonfatal error, the current simulation will be aborted, but the next simulation will proceed. – `Negative Integer`: Fatal error, no further simulations will be attempted.

As the design does not have any futility boundary, AdjPVal will be used to check for efficacy.

Option 4 (RawPVal): Expected Members of the Output List

Members	Type	Description
RawPVal	Vector of Numeric	Vector of length `DesignParam$NumTreatments`, containing the p-values computed from test statistics for each treatment arm.
ErrorCode	Integer	Optional. Can be used to handle errors in your script: – `0`: No error. – `Positive Integer`: Nonfatal error, the current simulation will be aborted, but the next simulation will proceed. – `Negative Integer`: Fatal error, no further simulations will be attempted.

As the design does not have any futility boundary, RawPVal will be used to check for efficacy.

Minimal Templates

Your R script could contain a function such as these ones, with a name of your choice. All input variables must be declared, even if they are not used in the script. We recommend always declaring UserParam as a default NULL value in the function arguments, as this will ensure that the same function will work regardless of whether the user has specified any custom parameters in East Horizon. A detailed template with step-by-step explanations is available here: Analyze.Continuous.R.

Minimal Template for Option 1 (Decision)

PerformDecision <- function( SimData, DesignParam, LookInfo = NULL, UserParam = NULL )
{
    library( CyneRgy )
    nError              <- 0 # Error handling (no error)
    NumTreatments       <- DesignParam$NumTreatments
    vDecision           <- rep( 0, NumTreatments )  # Initializing decision vector to 0
    
    # This is an example using GetDecisionString and GetDecision.
    # Write the actual code here.
    
    # These variables are set because it is a fixed sample design.
    nQtyOfLooks          <- 1
    nLookIndex           <- 1 
    nQtyOfEvents         <- DesignParam$MaxCompleters
    nQtyOfPatsInAnalysis <- nrow( SimData )
    nTailType            <- DesignParam$TailType
    
    for( i in 1:NumTreatments )
    {
        # It is a fixed sample design, so no interim look nor futility check.
        bFAEfficacyCheck <- TRUE # If TRUE, declares efficacy.
        # Usually, bFAEfficacyCheck would be a conditional statement such as 'dTValue > dBoundary'.
        # This would be different for each treatment arm.
    
        strDecision <- CyneRgy::GetDecisionString( LookInfo, nLookIndex, nQtyOfLooks,
                                               bFAEfficacyCondition = bFAEfficacyCheck)

        nDecision <- CyneRgy::GetDecision( strDecision, DesignParam, LookInfo )
        vDecision[ i ] = nDecision
    }
    
    
    return( list( Decision = as.integer( vDecision ), ErrorCode = as.integer( nError ) ) )
}

Minimal Template for Option 2 (TestStat)

ComputeTestStat <- function( SimData, DesignParam, LookInfo = NULL, UserParam = NULL )
{
    nError              <- 0 # Error handling (no error)
    NumTreatments       <- DesignParam$NumTreatments
    vTestStatistic      <- rep( 0, NumTreatments )  # Initializing test statistic vector to 0   
    
    # Write the actual code here.
    # Store the computed test statistic for each treatment arm in vTestStatistic.
    
    return( list( TestStat = as.double( vTestStatistic ), ErrorCode = as.integer( nError ) ) )
}

Minimal Template for Option 3 (AdjPVal)

ComputeTestStat <- function( SimData, DesignParam, LookInfo = NULL, UserParam = NULL )
{
    nError              <- 0 # Error handling (no error)
    NumTreatments       <- DesignParam$NumTreatments
    vAdjPVal            <- rep( 0, NumTreatments )  # Initializing p-value vector to 0  
    
    # Write the actual code here.
    # Store the computed adjusted p-value for each treatment arm in vAdjPVal.
    
    return( list( AdjPVal = as.double( vTestStatistic ), ErrorCode = as.integer( nError ) ) )
}

Minimal Template for Option 4 (RawPVal)

ComputeTestStat <- function( SimData, DesignParam, LookInfo = NULL, UserParam = NULL )
{
    nError              <- 0 # Error handling (no error)
    NumTreatments       <- DesignParam$NumTreatments
    vRawPVal            <- rep( 0, NumTreatments )  # Initializing p-value vector to 0  
    
    # Write the actual code here.
    # Store the computed p-value for each treatment arm in vRawPVal.
    
    return( list( RawPVal = as.double( vTestStatistic ), ErrorCode = as.integer( nError ) ) )
}

Examples

Explore the following examples for more context:

2-Arm, Normal Outcome - Analysis
- This example focuses on a Two-Arm Confirmatory study objective but can still provide valuable insights.

Integration Point: Analysis - Continuous Outcome, Multiple Arm, Fixed Sample Design

Gabriel Potvin

January 16, 2025