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This function creates a table summarizing event counts based on specified hypotheses and user input data. It can handle two types of analysis: one comparing experimental groups to a common control and another analyzing the overlap of populations.

Source: R/generate_event_table_.R
generate_event_table_.Rd

This function creates a table summarizing event counts based on specified hypotheses and user input data. It can handle two types of analysis: one comparing experimental groups to a common control and another analyzing the overlap of populations.

Usage

generate_event_table_(
  event,
  hypothesis,
  type = c("common_control", "overlap_population")
)

Arguments

event

dataframe should have the following structure:

  • Population: A character vector indicating the population groups. For example, "Population 1", "Population 2", "Overall population" in overlap population situation; or experimental arms and control in common control situation.

  • 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 containing hypotheses that specify the comparisons to be made between the groups: - For example: - "Experimental 1 vs. Control" - "Efficacy in Population 1"

type

A character string specifying the type of analysis to conduct. It should be one of the following: - "common_control": Analyze the event counts comparing experimental groups to common control. - "overlap_population": Analyze the event counts to assess overlap in populations.

Value

A dataframe with four 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 common control
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 experimental group.
  FA = c(135, 150, 165, 170) # Final analysis values indicating the cumulative number of events observed in each group.
)

hypothesis <- list(
  H1 = "Experimental 1 vs. Control", # Hypothesis comparing Experimental 1 with Control.
  H2 = "Experimental 2 vs. Control", # Hypothesis comparing Experimental 2 with Control.
  H3 = "Experimental 1 vs. Experimental 2" # Hypothesis comparing Experimental 1 and Experimental 2.
)

generate_event_table_(event, hypothesis, type = "common_control")
#> # 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 overall population
event <- data.frame(
  Population = c("Population 1", "Population 2", "Population 1 Intersection 2", "Overall population"),
  IA = c(100, 110, 80, 225), # Interim analysis values for the overall population.
  FA = c(200, 220, 160, 450) # Final analysis values for the overall population.
)

hypothesis <- list(
  H1 = "Efficacy in Population 1", # Hypothesis assessing efficacy in Population 1.
  H2 = "Efficacy in Population 2", # Hypothesis assessing efficacy in Population 2.
  H3 = "Efficacy in Overall population" # Hypothesis assessing efficacy in the overall population.
)

generate_event_table_(event, hypothesis, type = "overlap_population")
#> # 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