This function generates a table of events for specified populations based on the provided hypotheses.

Usage

generate_event_table_ol(event, hypothesis)

Arguments

event

dataframe should have the following structure:

Population: A character vector indicating the population groups (e.g., "Population 1", "Population 2", "Population 1 Intersection 2", and "Overall population").
IA: Numeric vector indicating the number of events observed in each group during interim analysis.
FA: 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 of strings where each item represents a hypothesis regarding efficacy, formatted as follows: - H1: "Efficacy in Population 1" - H2: "Efficacy in Population 2" - H3: "Efficacy in Overall population" Each hypothesis is used for comparisons in the generated event table.

Value

A dataframe with the following 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("Population 1", "Population 2", "Population 1 Intersection 2", "Overall population"),
  IA = c(100, 110, 80, 225), # Interim Analysis values indicating the number of events observed in each group
  FA = c(200, 220, 160, 450)
)

hypothesis <- list(
  H1 = "Efficacy in Population 1",
  H2 = "Efficacy in Population 2",
  H3 = "Efficacy in Overall population"
)

generate_event_table_ol(event, hypothesis)
#> # A tibble: 12 × 4
#>    one_hypothesis another_hypothesis analysis common_events
#>             <int>              <int>    <int>         <dbl>
#>  1              1                  1        1           100
#>  2              1                  2        1            80
#>  3              1                  3        1           100
#>  4              2                  2        1           110
#>  5              2                  3        1           110
#>  6              3                  3        1           225
#>  7              1                  1        2           200
#>  8              1                  2        2           160
#>  9              1                  3        2           200
#> 10              2                  2        2           220
#> 11              2                  3        2           220
#> 12              3                  3        2           450

#----------------------Example of two IAs and FA
event <- data.frame(
  Population = c("Population 1", "Population 2", "Population 1 Intersection 2", "Overall population"),
  IA1 = c(100, 110, 80, 225), # First Interim Analysis values indicating the number of events observed in each group
  IA2 = c(120, 130, 90, 240), # Second Interim Analysis values indicating the number of events observed in each group
  FA = c(200, 220, 160, 450)
)

hypothesis <- list(
  H1 = "Efficacy in Population 1",
  H2 = "Efficacy in Population 2",
  H3 = "Efficacy in Overall population"
)

generate_event_table_ol(event, hypothesis)
#> # A tibble: 18 × 4
#>    one_hypothesis another_hypothesis analysis common_events
#>             <int>              <int>    <int>         <dbl>
#>  1              1                  1        1           100
#>  2              1                  2        1            80
#>  3              1                  3        1           100
#>  4              2                  2        1           110
#>  5              2                  3        1           110
#>  6              3                  3        1           225
#>  7              1                  1        2           120
#>  8              1                  2        2            90
#>  9              1                  3        2           120
#> 10              2                  2        2           130
#> 11              2                  3        2           130
#> 12              3                  3        2           240
#> 13              1                  1        3           200
#> 14              1                  2        3           160
#> 15              1                  3        3           200
#> 16              2                  2        3           220
#> 17              2                  3        3           220
#> 18              3                  3        3           450