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Dynamic tabulation of MMRM results with tables

Source: R/summarize_mmrm.R
summarize_mmrm.Rd

[Stable]

These functions can be used to produce tables for MMRM results, within tables which are split by arms and visits. This is helpful when higher-level row splits are needed (e.g. splits by parameter or subgroup).

Usage

s_summarize_mmrm(
  df,
  .var,
  variables,
  ref_levels,
  .spl_context,
  alternative = c("two.sided", "less", "greater"),
  show_relative = c("reduction", "increase"),
  ...
)

a_summarize_mmrm(
  df,
  .var,
  .spl_context,
  ...,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

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.

variables

(named list of string)
list of additional analysis variables.

ref_levels

(list)
with visit and arm reference levels.

.spl_context

(data.frame)
gives information about ancestor split states that is passed by rtables.

alternative

(string)
whether two.sided, or one-sided less or greater p-value should be displayed.

show_relative

should the 'reduction' (control - treatment, default) or the 'increase' (treatment - control) be shown for the relative change from baseline?

...

eventually passed to fit_mmrm_j() via h_summarize_mmrm().

.stats

(character)
statistics to select for the table.

.formats

(named character or list)
formats for the statistics. See Details in analyze_vars for more information on the 'auto' setting.

.labels

(named character)
labels for the statistics (without indent).

.indent_mods

(named integer)
indent modifiers for the labels. Defaults to 0, which corresponds to the unmodified default behavior. Can be negative.

Value

Functions

  • s_summarize_mmrm(): Statistics function which is extracting estimates, not including any results when in the reference visit, and only showing LS mean estimates when in the reference arm and not in reference visit. It uses s_lsmeans() for the final processing.

  • a_summarize_mmrm(): Formatted analysis function which is used as afun.

Examples

set.seed(123)
longdat <- data.frame(
  ID = rep(DM$ID, 5),
  AVAL = c(
    rep(0, nrow(DM)),
    rnorm(n = nrow(DM) * 4)
  ),
  VISIT = factor(rep(paste0("V", 0:4), each = nrow(DM)))
) |>
  dplyr::inner_join(DM, by = "ID")

basic_table() |>
  split_rows_by("VISIT") |>
  split_cols_by("ARM") |>
  analyze(
    vars = "AVAL",
    afun = a_summarize_mmrm,
    na_str = tern::default_na_str(),
    show_labels = "hidden",
    extra_args = list(
      variables = list(
        covariates = c("AGE"),
        id = "ID",
        arm = "ARM",
        visit = "VISIT"
      ),
      conf_level = 0.9,
      cor_struct = "toeplitz",
      ref_levels = list(VISIT = "V0", ARM = "B: Placebo")
    )
  ) |>
  build_table(longdat) |>
  prune_table(all_zero)
#>                                                   A: Drug X                B: Placebo             C: Combination    
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> V1                                                                                                                  
#>   n                                                  121                      106                      129          
#>   Adjusted Mean (SE)                            0.081 (0.090)            -0.139 (0.096)           0.104 (0.087)     
#>     Adjusted Mean 90% CI                       (-0.066, 0.229)          (-0.297, 0.019)          (-0.039, 0.247)    
#>     Adjusted Mean (90% CI)                  0.081 (-0.066, 0.229)    -0.139 (-0.297, 0.019)   0.104 (-0.039, 0.247) 
#>   Difference in Adjusted Means (SE)             0.220 (0.132)                                     0.243 (0.130)     
#>     Difference in Adjusted Means 90% CI         (0.004, 0.437)                                    (0.030, 0.457)    
#>     Difference in Adjusted Means (90% CI)    0.220 (0.004, 0.437)                              0.243 (0.030, 0.457) 
#>     Relative Reduction (%)                          158.6%                                            175.1%        
#>     2-sided p-value                                 0.094                                             0.061         
#> V2                                                                                                                  
#>   n                                                  121                      106                      129          
#>   Adjusted Mean (SE)                            -0.079 (0.090)           -0.124 (0.096)           0.076 (0.087)     
#>     Adjusted Mean 90% CI                       (-0.226, 0.069)          (-0.282, 0.034)          (-0.067, 0.219)    
#>     Adjusted Mean (90% CI)                  -0.079 (-0.226, 0.069)   -0.124 (-0.282, 0.034)   0.076 (-0.067, 0.219) 
#>   Difference in Adjusted Means (SE)             0.046 (0.132)                                     0.200 (0.130)     
#>     Difference in Adjusted Means 90% CI        (-0.171, 0.262)                                   (-0.013, 0.413)    
#>     Difference in Adjusted Means (90% CI)   0.046 (-0.171, 0.262)                             0.200 (-0.013, 0.413) 
#>     Relative Reduction (%)                          36.8%                                             161.0%        
#>     2-sided p-value                                 0.728                                             0.123         
#> V3                                                                                                                  
#>   n                                                  121                      106                      129          
#>   Adjusted Mean (SE)                            0.060 (0.090)            0.101 (0.096)            0.110 (0.087)     
#>     Adjusted Mean 90% CI                       (-0.088, 0.208)          (-0.057, 0.259)          (-0.033, 0.253)    
#>     Adjusted Mean (90% CI)                  0.060 (-0.088, 0.208)    0.101 (-0.057, 0.259)    0.110 (-0.033, 0.253) 
#>   Difference in Adjusted Means (SE)             -0.041 (0.132)                                    0.009 (0.130)     
#>     Difference in Adjusted Means 90% CI        (-0.257, 0.176)                                   (-0.204, 0.223)    
#>     Difference in Adjusted Means (90% CI)   -0.041 (-0.257, 0.176)                            0.009 (-0.204, 0.223) 
#>     Relative Reduction (%)                          40.3%                                             -9.1%         
#>     2-sided p-value                                 0.757                                             0.943         
#> V4                                                                                                                  
#>   n                                                  121                      106                      129          
#>   Adjusted Mean (SE)                            0.113 (0.090)            -0.133 (0.096)           -0.022 (0.087)    
#>     Adjusted Mean 90% CI                       (-0.035, 0.260)          (-0.291, 0.025)          (-0.165, 0.121)    
#>     Adjusted Mean (90% CI)                  0.113 (-0.035, 0.260)    -0.133 (-0.291, 0.025)   -0.022 (-0.165, 0.121)
#>   Difference in Adjusted Means (SE)             0.245 (0.132)                                     0.111 (0.130)     
#>     Difference in Adjusted Means 90% CI         (0.029, 0.462)                                   (-0.103, 0.324)    
#>     Difference in Adjusted Means (90% CI)    0.245 (0.029, 0.462)                             0.111 (-0.103, 0.324) 
#>     Relative Reduction (%)                          184.9%                                            83.5%         
#>     2-sided p-value                                 0.062                                             0.393