Wrappers Around tern::a_summary() with Row Split Exclusion

These wrappers around tern::a_summary() allow selected row split levels to be excluded from producing the analysis.

Usage

a_summary_j_with_exclude(df, .var, exclude_levels, .spl_context, ...)

Arguments

df: (data.frame)
data set containing all analysis variables.
.var: (string)
single variable name that is passed by rtables when requested by a statistics function.
exclude_levels: (list)
A named list where names correspond to split variables and values are vectors of levels to exclude.
.spl_context: (data.frame)
gives information about ancestor split states that is passed by rtables.
...: Additional arguments passed to tern::a_summary(), excluding the x argument, which is constructed internally from df[[.var]].

Value

Returns the same type of object as tern::a_summary(), or NULL when the current row split path is excluded by exclude_levels.

Details

tern::a_summary() is a formatted analysis function that computes summary statistics for a single variable. In typical use within the rtables framework, the analysis variable is first extracted from a data frame and then passed to tern::a_summary() as a standalone vector.

a_summary_j_with_exclude() extends this behavior by:

accepting a data frame (df) as input,
checking whether the current row split path should be excluded,
returning NULL for excluded row split levels,
extracting the analysis variable .var for all other row split levels,
and passing the resulting vector to tern::a_summary().

Functions

a_summary_j_with_exclude(): Analysis function which can exclude row split levels from producing the analysis. These have to be specified in the exclude_levels argument, see ?do_exclude_split for details.

Examples

df <- data.frame(
  USUBJID = 1:8,
  AVISIT = factor(rep(c("Baseline", "Week 1"), 4)),
  ARM = factor(rep(c("A", "B"), each = 4)),
  AVAL = c(1.2, 3, -1.5, 0.8, 3.5, 2, -1.1, 0.3)
)

lyt <- basic_table() |>
  split_cols_by("ARM") |>
  split_rows_by("AVISIT") |>
  analyze(
    "AVAL",
    afun = a_summary_j_with_exclude,
    extra_args = list(exclude_levels = list(AVISIT = "Baseline"))
  )

build_table(lyt, df)
#>                                           A                        B           
#> ———————————————————————————————————————————————————————————————————————————————
#> Baseline                                                                       
#>   a_summary_j_with_exclude                                                     
#> Week 1                                                                         
#>   n                                       2                        2           
#>   Sum                                    3.8                      2.3          
#>   Mean                                   1.9                      1.1          
#>   SD                                     1.6                      1.2          
#>   SE                                     1.1                      0.8          
#>   Mean (SD)                           1.9 (1.6)                1.1 (1.2)       
#>   Mean (SE)                           1.9 (1.1)                1.1 (0.8)       
#>   Mean 95% CI                      (-12.08, 15.88)          (-9.65, 11.95)     
#>   Mean -/+ 1xSE                     (0.80, 3.00)             (0.30, 2.00)      
#>   Mean -/+ 1xSD                     (0.34, 3.46)             (-0.05, 2.35)     
#>   Mean p-value (H0: mean = 0)          0.3341                   0.4052         
#>   Median                                 1.9                      1.1          
#>   Median Absolute Deviation              0.0                      0.0          
#>   Median 95% CI                          NA                       NA           
#>   25% and 75%-ile                     0.8 - 3.0                0.3 - 2.0       
#>   IQR                                    2.2                      1.7          
#>   Min - Max                           0.8 - 3.0                0.3 - 2.0       
#>   Minimum                                0.8                      0.3          
#>   Maximum                                3.0                      2.0          
#>   Median (Min - Max)               1.9 (0.8 - 3.0)          1.1 (0.3 - 2.0)    
#>   CV (%)                                81.9                     104.5         
#>   Geometric Mean                         1.5                      0.8          
#>   Geometric SD                           2.5                      3.8          
#>   Geometric Mean (SD)                 1.5 (2.5)                0.8 (3.8)       
#>   Geometric Mean 95% CI            (0.00, 6870.45)         (0.00, 132877.73)   
#>   CV % Geometric Mean                   118.1                    224.7         
#>   Median (95% CI)                  1.90 (NA - NA)           1.15 (NA - NA)     
#>   Mean (95% CI)                 1.90 (-12.08 - 15.88)    1.15 (-9.65 - 11.95)  
#>   Geometric Mean (95% CI)       1.55 (0.00 - 6870.45)   0.77 (0.00 - 132877.73)

Wrappers Around `tern::a_summary()` with Row Split Exclusion