@@ -1189,6 +1189,34 @@ directly wrapped for use with `graph_test_shortcut_gsd()`. However,
11891189` gsDesign ` and ` rpact ` implement the same standard spending function
11901190formulas, so ` gsDesign ` wrappers can be used to achieve equivalent results.
11911191
1192+ However, it would be possible to use ` rpact ` if the ` graphicalMCP ` package functions interface
1193+ differently with ` rpact ` compared to ` gsDesign ` . In particular, this code snippet shows how to obtain the
1194+ repeated p-values from ` rpact ` shown in the above repeated p-values example:
1195+
1196+ ``` {r rpact-snippet}
1197+ rpact::setLogLevel("DISABLED")
1198+ repP <- function(pVals){
1199+ cum_n <- seq_along(pVals) * 2
1200+ design <- rpact::getDesignGroupSequential(typeOfDesign = "asOF", kMax = 3)
1201+ data <- rpact::getDataset(
1202+ cumMeans = c(qnorm(1 - pVals) / sqrt(cum_n)),
1203+ cumStDevs = rep(1, length(pVals)),
1204+ cumN = cum_n
1205+ )
1206+ stage_res <- rpact::getStageResults(design, data, normalApproximation = TRUE)
1207+ rpact::getRepeatedPValues(stage_res)
1208+ }
1209+
1210+ repP(c(0.0062, 0.0002))
1211+ repP(c(0.0170, 0.0035))
1212+ repP(c(0.0090, 0.0020))
1213+ repP(c(0.1300, 0.0600))
1214+ ```
1215+
1216+ It would be more beneficial to use the built-in ` rpact ` integration routines to obtain these
1217+ repeated p-values, because this would provide an alternative computation method.
1218+ Merely supplying another implementation of the same simple spending function formulas
1219+ would not provide a meaningful alternative.
11921220### User-Defined Spending Functions
11931221
11941222Users can define entirely custom spending functions as long as they accept
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