`r lifecycle::badge('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).
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.
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