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Tabulation of MMRM Subgroups Results

Source: R/subgroups.R
tabulate_mmrm_subgroups.Rd

[Experimental]

This function tabulates the results from extract_mmrm_subgroups().

Usage 

tabulate_mmrm_subgroups(
  lyt,
  df,
  vars = c("n_tot", "n", "lsmean", "diff", "ci"),
  .formats = list(n = "xx", n_tot = "xx", lsmean = "xx.x", diff = "xx.x", ci =
    "(xx.x, xx.x)", pval = "x.xxxx | (<0.0001)"),
  .labels = list(n = "n", n_tot = "Total n", lsmean = "Mean", diff = "Mean Difference",
    ci = paste0(round(100 * df$contrasts$conf_level[1]), "% CI"), pval = "p-value")
)

Arguments

lyt

(layout)
input layout where analyses will be added to.

df

(list)
of data frames containing all analysis variables, is the result from extract_mmrm_subgroups().

vars

(character)
the name of statistics to be reported among n_tot (total number of patients per group), n (number of patients per treatment arm and group), lsmean (least square mean point estimate), diff (difference of least square mean estimates between treatment and reference arm), ci (confidence interval of difference) and pval (p value of the diff, not adjusted for multiple comparisons). Note, the statistics n_tot, diff and ci are required.

.formats

(named list)
containing the formats for the statistics.

.labels

(named list)
containing the labels for the statistics.

Value

The rtables object.

Examples 

mmrm_results <- fit_mmrm(
  vars = list(
    response = "FEV1",
    covariates = "RACE",
    id = "USUBJID",
    arm = "ARMCD",
    visit = "AVISIT"
  ),
  data = mmrm_test_data,
  cor_struct = "compound symmetry",
  weights_emmeans = "equal",
  averages_emmeans = list(
    "VIS1+2" = c("VIS1", "VIS2")
  )
)

df <- extract_mmrm_subgroups(
  fit = mmrm_results,
  visit = "VIS3",
  subgroups = c("RACE", "SEX")
)

## Table with default columns.
basic_table() %>%
  tabulate_mmrm_subgroups(df)
#>                                            PBO         TRT                                   
#> Baseline Risk Factors         Total n   n    Mean   n    Mean   Mean Difference     95% CI   
#> —————————————————————————————————————————————————————————————————————————————————————————————
#> All Patients                    129     71   43.7   58   46.5         2.8         (0.5, 5.2) 
#> RACE                                                                                         
#>   Asian                         51      27   41.4   24   43.8         2.4         (-1.2, 6.0)
#>   Black or African American     42      29   42.6   13   46.1         3.5         (-0.8, 7.9)
#>   White                         36      15   47.0   21   50.0         3.0         (-1.5, 7.5)
#> SEX                                                                                          
#>   Male                          58      35   43.0   23   46.7         3.7         (0.2, 7.2) 
#>   Female                        71      36   44.2   35   46.4         2.2         (-1.0, 5.3)

## Table with selected columns.
tab <- basic_table() %>%
  tabulate_mmrm_subgroups(
    df = df,
    vars = c("n_tot", "diff", "ci", "pval")
  )
tab
#> Baseline Risk Factors         Total n   Mean Difference     95% CI      p-value
#> ———————————————————————————————————————————————————————————————————————————————
#> All Patients                    129           2.8         (0.5, 5.2)    0.0162 
#> RACE                                                                           
#>   Asian                         51            2.4         (-1.2, 6.0)   0.1853 
#>   Black or African American     42            3.5         (-0.8, 7.9)   0.1085 
#>   White                         36            3.0         (-1.5, 7.5)   0.1920 
#> SEX                                                                            
#>   Male                          58            3.7         (0.2, 7.2)    0.0370 
#>   Female                        71            2.2         (-1.0, 5.3)   0.1753 

## Forest plot can be produced based on this very easily.
g_forest(tab, logx = FALSE, xlim = c(-10, 10), x_at = c(-10, -5, 0, 5, 10), vline = 0)