![[Stable]](figures/lifecycle-stable.svg)
These functions can be used to produce tables from a fitted MMRM produced with
fit_mmrm().
Usage
# S3 method for tern_mmrm
as.rtable(x, type = c("fixed", "cov", "diagnostic"), ...)
h_mmrm_fixed(x, format = "xx.xxxx")
h_mmrm_cov(x, format = "xx.xxxx")
h_mmrm_diagnostic(x, format = "xx.xxxx")
# S3 method for tern_mmrm
tidy(x, ...)
s_mmrm_lsmeans(df, .in_ref_col, show_relative = c("reduction", "increase"))
a_mmrm_lsmeans(df, .in_ref_col, show_relative = c("reduction", "increase"))
s_mmrm_lsmeans_single(df)
a_mmrm_lsmeans_single(df)
summarize_lsmeans(
lyt,
arms = TRUE,
...,
table_names = "lsmeans_summary",
.stats = NULL,
.formats = NULL,
.indent_mods = NULL,
.labels = NULL
)Arguments
- x
(
tern_mmrm)
the original result fromfit_mmrm().- type
(
string)
type of table which should be returned.- ...
additional argument
formatfor controlling the numeric format.- format
(
string)
format for the numbers in the table.- df
(
data frame)
data set containing all analysis variables.- .in_ref_col
(
logical)TRUEwhen working with the reference level,FALSEotherwise.- show_relative
should the "reduction" (
control - treatment, default) or the "increase" (treatment - control) be shown for the relative change from baseline?- lyt
(
layout)
input layout where analyses will be added to.- arms
(
flag)
should treatment variable be considered when usingsummarize_lsmeanslayout generating function.- table_names
(
character)
this can be customized in case that the samevarsare analyzed multiple times, to avoid warnings fromrtables.- .stats
(
character)
statistics to select for the table.- .formats
(named
characterorlist)
formats for the statistics.- .indent_mods
(named
integer)
indent modifiers for the labels.- .labels
(named
character)
labels for the statistics (without indent).
Value
as.rtable.tern_mmrm() returns the fixed effects, covariance estimate or
diagnostic statistics tables.
Functions
as.rtable(tern_mmrm): Produce simpleMMRMtables via the genericas.rtable().h_mmrm_fixed(): Helper function to produce fixed effects table.h_mmrm_cov(): Helper function to produce a covariance matrix table.h_mmrm_diagnostic(): Helper function to produce a diagnostic statistics table.tidy(tern_mmrm): Helper method (forbroom::tidy()) to prepare adata.framefrom antern_mmrmobject containing the least-squares means and contrasts.s_mmrm_lsmeans(): Statistics function which is extracting estimates from a tidied least-squares means data frame.a_mmrm_lsmeans(): Formatted Analysis function which can be further customized by callingrtables::make_afun()on it. It is used asafuninrtables::analyze().s_mmrm_lsmeans_single(): Statistics function which is extracting estimates from a tidied least-squares means data frame whenARMis not considered in the model.a_mmrm_lsmeans_single(): Formatted Analysis function (whenARMis not considered in the model) which can be further customized by callingrtables::make_afun()on it. It is used asafuninrtables::analyze().summarize_lsmeans(): Analyze function for tabulating least-squares means estimates from tidiedmmrmresults.
Examples
result <- fit_mmrm(
vars = list(
response = "FEV1",
covariates = c("RACE", "SEX"),
id = "USUBJID",
arm = "ARMCD",
visit = "AVISIT"
),
data = mmrm_test_data,
cor_struct = "unstructured",
weights_emmeans = "equal"
)
as.rtable(result, type = "cov", format = "xx.x")
#> VIS1 VIS2 VIS3 VIS4
#> --------------------------------
#> VIS1 40.6 14.4 5.0 13.4
#> VIS2 14.4 26.6 2.8 7.5
#> VIS3 5.0 2.8 14.9 0.9
#> VIS4 13.4 7.5 0.9 95.6
result_no_arm <- fit_mmrm(
vars = list(
response = "FEV1",
covariates = c("RACE", "SEX"),
id = "USUBJID",
visit = "AVISIT"
),
data = mmrm_test_data,
cor_struct = "unstructured",
weights_emmeans = "equal"
)
as.rtable(result_no_arm, type = "cov", format = "xx.x")
#> VIS1 VIS2 VIS3 VIS4
#> --------------------------------
#> VIS1 43.3 17.8 7.3 18.1
#> VIS2 17.8 30.4 6.0 10.7
#> VIS3 7.3 6.0 17.0 5.0
#> VIS4 18.1 10.7 5.0 98.9
df <- broom::tidy(result)
df_no_arm <- broom::tidy(result_no_arm)
s_mmrm_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] "95% CI"
#>
#> $diff_mean_se
#> [1] 4.398457 1.680545
#>
#> $diff_mean_ci
#> [1] 1.074493 7.722422
#> attr(,"label")
#> [1] "95% CI"
#>
#> $change
#> [1] -0.09090396
#> attr(,"label")
#> [1] "Relative Reduction (%)"
#>
#> $p_value
#> [1] 0.009886854
#>
s_mmrm_lsmeans_single(df_no_arm[4, ])
#> $n
#> [1] 134
#>
#> $adj_mean_se
#> [1] 50.6510864 0.8508787
#>
#> $adj_mean_ci
#> [1] 48.96834 52.33383
#> attr(,"label")
#> [1] "95% CI"
#>
library(dplyr)
dat_adsl <- mmrm_test_data %>%
select(USUBJID, ARMCD) %>%
unique()
basic_table() %>%
split_cols_by("ARMCD", ref_group = result$ref_level) %>%
add_colcounts() %>%
split_rows_by("AVISIT") %>%
summarize_lsmeans(
.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)"),
.formats = c(adj_mean_se = sprintf_format("%.1f (%.2f)"))
) %>%
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)
basic_table() %>%
split_rows_by("AVISIT") %>%
summarize_lsmeans(arms = FALSE) %>%
build_table(
df = broom::tidy(result_no_arm),
alt_counts_df = dat_adsl
)
#> all obs
#> ---------------------------------------
#> VIS1
#> n 134
#> Adjusted Mean (SE) 35.191 (0.550)
#> 95% CI (34.105, 36.277)
#> VIS2
#> n 140
#> Adjusted Mean (SE) 40.039 (0.455)
#> 95% CI (39.140, 40.938)
#> VIS3
#> n 129
#> Adjusted Mean (SE) 45.029 (0.361)
#> 95% CI (44.314, 45.744)
#> VIS4
#> n 134
#> Adjusted Mean (SE) 50.651 (0.851)
#> 95% CI (48.968, 52.334)