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This function generates a table of events for given experimental arms and a control group based on specified hypotheses.

Source: R/generate_event_cc.R
generate_event_table_cc.Rd

This function generates a table of events for given experimental arms and a control group based on specified hypotheses.

Usage

generate_event_table_cc(event, hypothesis)

Arguments

event

A dataframe containing the following columns:

  • Population: A character vector listing the population groups (e.g., experimental arms and control).

  • IA: A numeric vector indicating the number of events observed in each group during interim analysis.

  • FA: A numeric vector indicating the number of events observed in each group during final analysis. The dataframe must contain at least these columns and can include additional analysis columns as needed.

hypothesis

A list containing hypotheses specifying comparisons between experimental arms and the control group, as well as comparisons among experimental arms.

Value

A dataframe with columns:

  • one_hypothesis: The index of the first selected hypothesis from the provided list.

  • another_hypothesis: The index of the second selected hypothesis from the provided list.

  • analysis: The index indicating which analysis is being performed (e.g., interim or final).

  • common_events: The calculated number of common events associated with the selected hypotheses.

Examples

#------------------------Example of IA and FA
event <- data.frame(
  Population = c("Experimental 1", "Experimental 2", "Experimental 3", "Control"),
  IA = c(70, 75, 80, 85), # Interim Analysis values indicating the number of events observed in each group
  FA = c(135, 150, 165, 170)
)

hypothesis <- list(
  H1 = "Experimental 1 vs. Control",
  H2 = "Experimental 2 vs. Control",
  H3 = "Experimental 1 vs. Experimental 2"
)

generate_event_table_cc(event, hypothesis)
#> # A tibble: 12 × 4
#>    one_hypothesis another_hypothesis analysis common_events
#>             <int>              <int>    <int>         <dbl>
#>  1              1                  1        1           155
#>  2              1                  2        1            85
#>  3              1                  3        1            70
#>  4              2                  2        1           160
#>  5              2                  3        1            75
#>  6              3                  3        1           165
#>  7              1                  1        2           305
#>  8              1                  2        2           170
#>  9              1                  3        2           135
#> 10              2                  2        2           320
#> 11              2                  3        2           150
#> 12              3                  3        2           335

#----------------------Example of two IAs and FA
event <- data.frame(
  Population = c("Experimental 1", "Experimental 2", "Experimental 3", "Control"),
  IA1 = c(70, 75, 80, 85), # First Interim Analysis values indicating the number of events observed in each group
  IA2 = c(90, 95, 100, 105), # Second Interim Analysis values indicating the number of events observed in each group
  FA = c(135, 150, 165, 170)
)

hypothesis <- list(
  H1 = "Experimental 1 vs. Control",
  H2 = "Experimental 2 vs. Control",
  H3 = "Experimental 1 vs. Experimental 2"
)

generate_event_table_cc(event, hypothesis)
#> # A tibble: 18 × 4
#>    one_hypothesis another_hypothesis analysis common_events
#>             <int>              <int>    <int>         <dbl>
#>  1              1                  1        1           155
#>  2              1                  2        1            85
#>  3              1                  3        1            70
#>  4              2                  2        1           160
#>  5              2                  3        1            75
#>  6              3                  3        1           165
#>  7              1                  1        2           195
#>  8              1                  2        2           105
#>  9              1                  3        2            90
#> 10              2                  2        2           200
#> 11              2                  3        2            95
#> 12              3                  3        2           205
#> 13              1                  1        3           305
#> 14              1                  2        3           170
#> 15              1                  3        3           135
#> 16              2                  2        3           320
#> 17              2                  3        3           150
#> 18              3                  3        3           335