S3 print method for the class gsd_graph_report — print.gsd_graph

A printed gsd_graph_report displays:

Test parameters: the initial graph, alpha, information fractions, p-values, spending functions, and per-hypothesis look_back settings.
Test summary: adjusted p-values, rejection decisions, the analysis at which each decision was made (Decision.at), the earliest analysis at which the boundary was crossed (First.Rej.at), look_back status, and the rejection sequence.
Per-analysis details (if test_values = TRUE): nominal p-values, boundaries, and rejection decisions at each analysis. For hypotheses rejected via look_back, additional rows show the boundary crossing at earlier analyses, marked with * and a footnote.
Boundary table (if verbose = TRUE): nominal p-value boundaries for all possible hypothesis weights from the graph's closure, enabling manual verification of rejection decisions.

Usage

# S3 method for class 'gsd_graph_report'
print(x, ..., precision = 6, indent = 2)

Arguments

x: An object of class gsd_graph_report to print.
...: Other values passed on to other methods (currently unused).
precision: An integer scalar indicating the number of decimal places to display.
indent: An integer scalar indicating how many spaces to indent results.

Value

An object x of class gsd_graph_report, invisibly.

References

Maurer, W., and Bretz, F. (2013). Multiple testing in group sequential trials using graphical approaches. Statistics in Biopharmaceutical Research, 5(4), 311-320.

Examples

hypotheses <- c(0.5, 0.5)
transitions <- rbind(c(0, 1), c(1, 0))
g <- graph_create(hypotheses, transitions)

p <- rbind(
  H1 = c(0.024, 0.01),
  H2 = c(0.015, 0.005)
)

graph_test_shortcut_gsd(
  graph = g,
  p = p,
  alpha = 0.025,
  info_frac = c(0.5, 1),
  spending_fn = spending_of
)
#> 
#> Test parameters ($inputs) ------------------------------------------------------
#>   Initial graph
#> 
#>   --- Hypothesis weights ---
#>   H1: 0.5
#>   H2: 0.5
#> 
#>   --- Transition weights ---
#>      H1 H2
#>   H1  0  1
#>   H2  1  0
#> 
#>   Alpha = 0.025
#> 
#>   Information fractions
#>    Analysis_1 Analysis_2
#> H1        0.5          1
#> H2        0.5          1
#> 
#>   P-values
#>    Analysis_1 Analysis_2
#> H1   0.024000   0.010000
#> H2   0.015000   0.005000
#> 
#>   Spending functions
#>     H1: O'Brien-Fleming
#>     H2: O'Brien-Fleming
#> 
#>   Look back = FALSE
#> 
#> Test summary ($outputs) --------------------------------------------------------
#>   Hypothesis   Adj.p* Reject Tested.at First.Rej.at Last.Rej.at Look.back
#>           H1 0.010094   TRUE         2            2           2     FALSE
#>           H2 0.010051   TRUE         2            2           2     FALSE
#>   (*) Adjusted p-values account for both the group sequential design and the
#>       graphical multiple comparison procedure. Based on repeated p-values when
#>       look_back = FALSE, and sequential p-values when look_back = TRUE.
#> 
#>   Rejection sequence: H2 -> H1
#> 
#>   Final updated graph after removing rejected hypotheses
#> 
#>   --- Hypothesis weights ---
#>   H1: NA
#>   H2: NA
#> 
#>   --- Transition weights ---
#>      H1 H2
#>   H1 NA NA
#>   H2 NA NA
#>