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Tabulation of Least Square Means Results

Source: R/tabulate_lsmeans.R
tabulate_lsmeans.Rd

[Stable]

These functions can be used to produce tables from LS means, e.g. from fit_mmrm_j() or fit_ancova().

Usage

# S3 method for class 'tern_model'
tidy(x, ...)

s_lsmeans(
  df,
  .in_ref_col,
  alternative = c("two.sided", "less", "greater"),
  show_relative = c("reduction", "increase")
)

a_lsmeans(
  df,
  ref_path,
  .spl_context,
  ...,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

Arguments

x

(numeric)
vector of numbers we want to analyze.

...

additional arguments for the lower level functions.

df

(data.frame)
data set containing all analysis variables.

.in_ref_col

(logical)
TRUE when working with the reference level, FALSE otherwise.

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?

ref_path

(character)
global reference group specification, see get_ref_info().

.spl_context

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

.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

for s_lsmeans, a list containing the same statistics returned by tern.mmrm::s_mmrm_lsmeans, with the additional diff_mean_est_ci three-dimensional statistic. For a_lsmeans, a VertalRowsSection as returned by rtables::in_rows.

Functions

  • tidy(tern_model): Helper method (for broom::tidy()) to prepare a data.frame from an tern_model object containing the least-squares means and contrasts.

  • s_lsmeans(): Statistics function which is extracting estimates from a tidied least-squares means data frame.

  • a_lsmeans(): Formatted Analysis function to be used as afun

Note

These functions have been forked from the tern.mmrm package. Additional features are:

  • Additional ref_path argument for tern.mmrm::summarize_lsmeans().

  • The function is more general in that it also works for LS means results from ANCOVA

  • Additional statistic diff_mean_est_ci is returned

  • P-value sidedness can be chosen

Examples

result <- fit_mmrm_j(
  vars = list(
    response = "FEV1",
    covariates = c("RACE", "SEX"),
    id = "USUBJID",
    arm = "ARMCD",
    visit = "AVISIT"
  ),
  data = mmrm::fev_data,
  cor_struct = "unstructured",
  weights_emmeans = "equal"
)

df <- broom::tidy(result)

s_lsmeans(df[8, ], .in_ref_col = FALSE)
#> $n
#> [1] 67
#> 
#> $adj_mean_se
#> [1] 52.78422  1.18776
#> 
#> $adj_mean_ci
#> [1] 50.43481 55.13362
#> attr(,"label")
#> [1] "Adjusted Mean 95% CI"
#> 
#> $adj_mean_est_ci
#> [1] 52.78422 50.43481 55.13362
#> attr(,"label")
#> [1] "Adjusted Mean (95% CI)"
#> 
#> $diff_mean_se
#> [1] 4.398457 1.680545
#> 
#> $diff_mean_ci
#> [1] 1.074493 7.722422
#> attr(,"label")
#> [1] "Difference in Adjusted Means 95% CI"
#> 
#> $diff_mean_est_ci
#> [1] 4.398457 1.074493 7.722422
#> attr(,"label")
#> [1] "Difference in Adjusted Means (95% CI)"
#> 
#> $change
#> [1] -0.09090396
#> attr(,"label")
#> [1] "Relative Reduction (%)"
#> 
#> $p_value
#> [1] 0.009886854
#> attr(,"label")
#> [1] "2-sided p-value"
#> 
s_lsmeans(df[8, ], .in_ref_col = FALSE, alternative = "greater", show_relative = "increase")
#> $n
#> [1] 67
#> 
#> $adj_mean_se
#> [1] 52.78422  1.18776
#> 
#> $adj_mean_ci
#> [1] 50.43481 55.13362
#> attr(,"label")
#> [1] "Adjusted Mean 95% CI"
#> 
#> $adj_mean_est_ci
#> [1] 52.78422 50.43481 55.13362
#> attr(,"label")
#> [1] "Adjusted Mean (95% CI)"
#> 
#> $diff_mean_se
#> [1] 4.398457 1.680545
#> 
#> $diff_mean_ci
#> [1] 1.074493 7.722422
#> attr(,"label")
#> [1] "Difference in Adjusted Means 95% CI"
#> 
#> $diff_mean_est_ci
#> [1] 4.398457 1.074493 7.722422
#> attr(,"label")
#> [1] "Difference in Adjusted Means (95% CI)"
#> 
#> $change
#> [1] 0.09090396
#> attr(,"label")
#> [1] "Relative Increase (%)"
#> 
#> $p_value
#> [1] 0.004943427
#> attr(,"label")
#> [1] "1-sided p-value (greater)"
#> 

dat_adsl <- mmrm::fev_data |>
  dplyr::select(USUBJID, ARMCD) |>
  unique()

basic_table() |>
  split_cols_by("ARMCD") |>
  add_colcounts() |>
  split_rows_by("AVISIT") |>
  analyze(
    "AVISIT",
    afun = a_lsmeans,
    show_labels = "hidden",
    na_str = tern::default_na_str(),
    extra_args = list(
      .stats = c(
        "n",
        "adj_mean_se",
        "adj_mean_ci",
        "diff_mean_se",
        "diff_mean_ci"
      ),
      .labels = c(
        adj_mean_se = "Adj. LS Mean (Std. Error)",
        adj_mean_ci = "95% CI",
        diff_mean_ci = "95% CI"
      ),
      .formats = c(adj_mean_se = jjcsformat_xx("xx.x (xx.xx)")),
      alternative = "greater",
      ref_path = c("ARMCD", result$ref_level)
    )
  ) |>
  build_table(
    df = broom::tidy(result),
    alt_counts_df = dat_adsl
  )
#>                                             PBO                TRT       
#>                                           (N=105)             (N=95)     
#> —————————————————————————————————————————————————————————————————————————
#> VIS1                                                                     
#>   n                                          68                 66       
#>   Adj. LS Mean (Std. Error)             33.3 (0.76)        37.1 (0.76)   
#>     95% CI                            (31.839, 34.825)   (35.599, 38.613)
#>   Difference in Adjusted Means (SE)                       3.774 (1.074)  
#>     95% CI                                                (1.651, 5.897) 
#> VIS2                                                                     
#>   n                                          69                 71       
#>   Adj. LS Mean (Std. Error)             38.2 (0.61)        41.9 (0.60)   
#>     95% CI                            (36.963, 39.380)   (40.713, 43.094)
#>   Difference in Adjusted Means (SE)                       3.732 (0.859)  
#>     95% CI                                                (2.035, 5.430) 
#> VIS3                                                                     
#>   n                                          71                 58       
#>   Adj. LS Mean (Std. Error)             43.7 (0.46)        46.8 (0.51)   
#>     95% CI                            (42.760, 44.588)   (45.748, 47.761)
#>   Difference in Adjusted Means (SE)                       3.081 (0.690)  
#>     95% CI                                                (1.716, 4.445) 
#> VIS4                                                                     
#>   n                                          67                 67       
#>   Adj. LS Mean (Std. Error)             48.4 (1.19)        52.8 (1.19)   
#>     95% CI                            (46.035, 50.737)   (50.435, 55.134)
#>   Difference in Adjusted Means (SE)                       4.398 (1.681)  
#>     95% CI                                                (1.074, 7.722)