Create a Function for Descriptive Statistics of a Dataset

Usage

analyse_describe()

summarise_describe(name = NULL)

Arguments

name: name for the summarise function, appended to the name of the analysis method in the final results

Value

an analyse function that returns a list with the elements

followup follow up time
events table of events vs. treatment
ice if column ice is present, table of intercurrent events, events, treatment
subgroup if column subgroup is present, table of subgroup, events, treatment

A function that can be used in Summarise that returns a data frame with columns with means and standard deviations for every variable in the description.

Functions

summarise_describe(): Summarise Descriptive Statistics

Examples

condition <- merge(
    assumptions_delayed_effect(),
    design_fixed_followup(),
    by=NULL
  ) |>
  head(1)
dat <- generate_delayed_effect(condition)
analyse_describe()(condition, dat)
#> $n_pat
#> [1] 300
#> 
#> $max_followup
#> [1] 8901
#> 
#> $evt
#> [1] 300
#> 
#> $evt_ctrl
#> [1] 150
#> 
#> $evt_trt
#> [1] 150
#> 
#> $study_time
#> [1] NA
#> 
condition <- merge(
  assumptions_delayed_effect(),
  design_fixed_followup(),
  by=NULL
) |>
  tail(4) |>
  head(1)

summarise_all <- create_summarise_function(
  describe=summarise_describe()
)

# runs simulations
sim_results <- runSimulation(
  design=condition,
  replications=100,
  generate=generate_delayed_effect,
  analyse=list(
    describe=analyse_describe()
  ),
  summarise = summarise_all
)

# study time is missing, since there was no admin. censoring
sim_results[, 9:16]
#> # A tibble: 1 × 8
#>   describe.n_pat describe.max_followup describe.evt describe.evt_ctrl
#>            <dbl>                 <dbl>        <dbl>             <dbl>
#> 1            300                8866.8          300               150
#> # ℹ 4 more variables: describe.evt_trt <dbl>, describe.study_time <dbl>,
#> #   describe.sd_n_pat <dbl>, describe.sd_max_followup <dbl>