Integration Point: Analysis - Continuous Outcome with Repeated Measures, 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$DropoutVisitID	Vector of Integer	Vector of length equal to the number of subjects, containing the ID of the visit where the patient was censored.
Response1	Vector of Numeric	Vector of length equal to the number of subjects, containing the generated responses for all subjects for the first visit.
Response2	Vector of Numeric	Vector of length equal to the number of subjects, containing the generated responses for all subjects for the second visit.
…		Responses for additional visits follow the same format, incrementing the visit number (e.g., Response3, Response4, etc.).
ResponseNumVisit	Vector of Numeric	Final vector of length equal to the number of subjects, containing the generated responses for all subjects for the last visit. `NumVisit` should be replaced by the number of visits.
ArrTimeVisit1	Vector of Numeric	Vector of length equal to the number of subjects, containing the arrival time in the trial for all subjects at the first visit.
ArrTimeVisit2	Vector of Numeric	Vector of length equal to the number of subjects, containing the arrival time in the trial for all subjects at the second visit.
…		Responses for additional visits follow the same format, incrementing the visit number (e.g., ArrTimeVisit3, ArrTimeVisit4, etc.).
ArrTimeVisitNumVisit	Vector of Numeric	Final vector of length equal to the number of subjects, containing the arrival time in the trial for all subjects at the last visit. `NumVisit` should be replaced by the number of visits.
CensorInd1	Vector of Numeric	Vector of length equal to the number of subjects, containing the generated censor indicator values for all subjects at the first visit: – `0`: Dropout. – `1`: Completer.
CensorInd2	Vector of Numeric	Vector of length equal to the number of subjects, containing the generated censor indicator values for all subjects at the second visit: – `0`: Dropout. – `1`: Completer.
…		Responses for additional visits follow the same format, incrementing the visit number (e.g., CensorInd3, CensorInd4, etc.).
CensorIndNumVisit	Vector of Numeric	Vector of length equal to the number of subjects, containing the generated censor indicator values for all subjects at the last visit: – `0`: Dropout. – `1`: Completer. `NumVisit` should be replaced by the number of visits.

Variables of DesignParam

Variable	Type	Description
DesignParam$Alpha	Numeric	Type I Error (for one-sided tests).
DesignParam$LowerAlpha	Numeric	Lower Type I Error (for two-sided tests). Not available in East Horizon Explore.
DesignParam$UpperAlpha	Numeric	Upper Type I Error (for two-sided tests). Not available in East Horizon Explore.
DesignParam$TrialType	Integer	Trial Type: – `0`: Superiority. – `1`: Non-inferiority. – `2`: Equivalence (not available in East Horizon Explore). – `3`: Super-superiority.
DesignParam$TestType	Integer	Test Type: – `0`: One-sided. – `1`: Two-sided symmetric (not available in East Horizon Explore). – `2`: Two-sided asymmetric (not available in East Horizon Explore).
DesignParam$TailType	Integer	Nature of critical region: – `0`: Left-tailed. – `1`: Right-tailed.
DesignParam$AllocInfo	Vector of Numeric	Vector of length equal to the number of treatment arms, containing the ratios of the treatment group sample sizes to control group sample size.
DesignParam$CriticalPoint	Numeric	Critical value (for one-sided tests).
DesignParam$LowerCriticalPoint	Numeric	Lower critical value (for two-sided tests). Not available in East Horizon Explore.
DesignParam$UpperCriticalPoint	Numeric	Upper critical value (for two-sided tests). Not available in East Horizon Explore.
DesignParam$SampleSize	Integer	Sample size of the trial.
DesignParam$MaxCompleters	Integer	Maximum number of completers.
DesignParam$RespLag	Numeric	Follow-up duration.
DesignParam$NumVisit	Integer	Number of visits.
DesignParam$VisitStatus	Vector of Integer	Vector of length `NumVisit`, containing the visit selection status for each visit: – `0`: Visit has not been selected for analysis. – `1`: Visit has been selected for analysis.
DesignParam$PrimContrastCoeff	Vector of Numeric	Vector of length `NumVisit`, containing the primary contrast coefficient for each visit.
DesignParam$DropImp	Integer	Dropout imputation method: – `0`: None. – `1`: Last observation carried forward.

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`.

The output list can take one of these two forms.

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

Members	Type	Description
Decision	Integer	Boundary crossing decision: – `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	Numeric	Value of appropriate test statistic on Wald ﴾Z﴿ scale.
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.

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.RepeatedMeasures.R.

Minimal Template for Option 1 (Decision)

PerformDecision <- function( SimData, DesignParam, LookInfo = NULL, UserParam = NULL )
{
    library( CyneRgy )
    nError              <- 0 # Error handling (no error)
    
    # This is an example using GetDecisionString and GetDecision.
    
    # Write the actual code here.
    # 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'.
    
    # These variables are set because it is a fixed sample design.
    nQtyOfLooks          <- 1
    nLookIndex           <- 1
    nQtyOfPatsInAnalysis <- nrow( SimData )
    TailType             <- DesignParam$TailType
    
    strDecision <- CyneRgy::GetDecisionString( LookInfo, nLookIndex, nQtyOfLooks,
                                               bFAEfficacyCondition = bFAEfficacyCheck)

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

Minimal Template for Option 2 (TestStat)

ComputeTestStat <- function( SimData, DesignParam,  LookInfo = NULL, UserParam = NULL )
{
    nError              <- 0 # Error handling (no error)
    dTestStatistic      <- 0
    
    # Write the actual code here.
    # Store the computed test statistic in dTestStatistic.
    
    return( list( TestStat = as.double( dTestStatistic ), ErrorCode = as.integer( nError ) ) )
}

Examples

Explore the following example for more context:

Analyze.RepeatedMeasures.R

Integration Point: Analysis - Continuous Outcome with Repeated Measures, Fixed Sample Design

Gabriel Potvin

January 16, 2025