Analysis function 3-column presentation
Source:R/a_summarize_aval_chg_diff.R
a_summarize_aval_chg_diff_j.RdAnalysis functions to produce a 1-row summary presented in
a 3-column layout in the columns: column 1: N, column 2: Value, column 3: change
In the difference columns, only 1 column will be presented : difference + CI
When ancova = `TRUE`, the presented statistics will be based on ANCOVA method (`s_summarize_ancova_j`).
mean and ci (both for Value (column 2) and Chg (column 3)) using statistic `lsmean_ci`
mean and ci for the difference column are based on same ANCOVA model using statistic `lsmean_diffci`
When ancova = `FALSE`, descriptive statistics will be used instead.
In the difference column, the 2-sample t-test will be used.
Usage
a_summarize_aval_chg_diff_j(
df,
.df_row,
.spl_context,
ancova = FALSE,
comp_btw_group = TRUE,
ref_path = NULL,
.N_col,
denom = c("N", ".N_col"),
indatavar = NULL,
d = 0,
id = "USUBJID",
interaction_y = FALSE,
interaction_item = NULL,
conf_level = 0.95,
variables = list(arm = "TRT01A", covariates = NULL),
format_na_str = "",
.stats = list(col1 = "count_denom_frac", col23 = "mean_ci_3d", coldiff =
"meandiff_ci_3d"),
.formats = list(col1 = NULL, col23 = "xx.dx (xx.dx, xx.dx)", coldiff =
"xx.dx (xx.dx, xx.dx)"),
.formats_fun = list(col1 = jjcsformat_count_denom_fraction, col23 = jjcsformat_xx,
coldiff = jjcsformat_xx),
multivars = c("AVAL", "AVAL", "CHG")
)Arguments
- df
(`data.frame`)
data set containing all analysis variables.- .df_row
(`data.frame`)
data frame across all of the columns for the given row split.- .spl_context
(`data.frame`)
gives information about ancestor split states that is passed by `rtables`.- ancova
(`logical`)
If FALSE, only descriptive methods will be used.
If TRUE Ancova methods will be used for each of the columns : AVAL, CHG, DIFF.- comp_btw_group
(`logical`)
If TRUE,
When ancova = FALSE, the estimate of between group difference (on CHG) will be based upon a two-sample t-test.
When ancova = TRUE, the same ancova model will be used for the estimate of between group difference (on CHG).- ref_path
(`character`)
global reference group specification, see [get_ref_info()].- .N_col
(`integer`)
column-wise N (column count) for the full column being analyzed that is typically passed by `rtables`.- denom
(`string`)
choice of denominator for proportions. Options are: * `N`: number of records in this column/row split.
There is no check in place that the current split only has one record per subject. Users should be careful with this. * `.N_col`: number of records in this column intersection (based on alt_counts_df dataset)
(when alt_counts_df is a single record per subjects, this will match number of subjects)- indatavar
(`string`)
If not null, variable name to extra subset incoming df to non-missing values of this variable.- d
(default = 1)
choice of Decimal precision. Note that one extra precision will be added, as means are presented.
Options are: * numerical(1) * variable name containing information on the precision, this variable should be available on input dataset. The content of this variable should then be an integer.- id
(`string`)
subject variable name.- interaction_y
(`character`)
Will be passed onto the `tern` function `s_ancova`, when ancova = TRUE.- interaction_item
(`character`)
Will be passed onto the `tern` function `s_ancova`, when ancova = TRUE.- conf_level
(`proportion`)
Confidence level of the interval- variables
(named list of strings)
list of additional analysis variables, with expected elements: * arm (string)
group variable, for which the covariate adjusted means of multiple groups will be summarized. Specifically, the first level of arm variable is taken as the reference group. * covariates (character)
a vector that can contain single variable names (such as 'X1'), and/or interaction terms indicated by 'X1 * X2'.- format_na_str
(`string`)
- .stats
(named `list`)
column statistics to select for the table. The following column names are to be used: `col1`, `col23`, `coldiff`.
For `col1`, the following stats can be specified.
For `col23`, only `mean_ci_3d` is available. When ancova=`TRUE` these are LS Means, otherwise, arithmetic means.
For `coldiff`, only `meandiff_ci_3d` is available. When ancova=`TRUE` these are LS difference in means, otherwise, difference in means based upon 2-sample t-test.- .formats
(named `list`)
formats for the column statistics. `xx.d` style formats can be used.- .formats_fun
(named `list`)
formatting functions for the column statistics, to be applied after the conversion of `xx.d` style to the appropriate precision.- multivars
(`string(3)`)
Variables names to use in 3-col layout.
See also
s_summarize_ancova_j
Other Inclusion of ANCOVA Functions:
a_summarize_ancova_j(),
s_ancova_j()
Examples
library(dplyr)
ADEG <- data.frame(
STUDYID = c(
'DUMMY', 'DUMMY', 'DUMMY', 'DUMMY', 'DUMMY',
'DUMMY', 'DUMMY', 'DUMMY', 'DUMMY', 'DUMMY'
),
USUBJID = c(
'XXXXX01', 'XXXXX02', 'XXXXX03', 'XXXXX04', 'XXXXX05',
'XXXXX06', 'XXXXX07', 'XXXXX08', 'XXXXX09', 'XXXXX10'
),
TRT01A = c(
'ARMA', 'ARMA', 'ARMA', 'ARMA', 'ARMA', 'Placebo',
'Placebo', 'Placebo', 'ARMA', 'ARMA'
),
PARAM = c('BP', 'BP', 'BP', 'BP', 'BP', 'BP', 'BP', 'BP', 'BP', 'BP'),
AVISIT = c(
'Visit 1', 'Visit 1', 'Visit 1', 'Visit 1', 'Visit 1',
'Visit 1', 'Visit 1', 'Visit 1', 'Visit 1', 'Visit 1'
),
AVAL = c(56, 78, 67, 87, 88, 93, 39, 87, 65, 55),
CHG = c(2, 3, -1, 9, -2, 0, 6, -2, 5, 2)
)
ADEG <- ADEG |>
mutate(
TRT01A = as.factor(TRT01A),
STUDYID = as.factor(STUDYID)
)
ADEG$colspan_trt <- factor(ifelse(ADEG$TRT01A == 'Placebo', ' ', 'Active Study Agent'),
levels = c('Active Study Agent', ' ')
)
ADEG$rrisk_header <- 'Risk Difference (%) (95% CI)'
ADEG$rrisk_label <- paste(ADEG$TRT01A, paste('vs', 'Placebo'))
colspan_trt_map <- create_colspan_map(ADEG,
non_active_grp = 'Placebo',
non_active_grp_span_lbl = ' ',
active_grp_span_lbl = 'Active Study Agent',
colspan_var = 'colspan_trt',
trt_var = 'TRT01A'
)
ref_path <- c('colspan_trt', ' ', 'TRT01A', 'Placebo')
lyt <- basic_table() |>
### first columns
split_cols_by(
'colspan_trt',
split_fun = trim_levels_to_map(map = colspan_trt_map)
) |>
split_cols_by('TRT01A') |>
split_rows_by(
'PARAM',
label_pos = 'topleft',
split_label = 'Blood Pressure',
section_div = ' ',
split_fun = drop_split_levels
) |>
split_rows_by(
'AVISIT',
label_pos = 'topleft',
split_label = 'Study Visit',
split_fun = drop_split_levels,
child_labels = 'hidden'
) |>
## set up a 3 column split
split_cols_by_multivar(
c('AVAL', 'AVAL', 'CHG'),
varlabels = c('n/N (%)', 'Mean (CI)', 'CFB (CI)')
) |>
split_cols_by('rrisk_header', nested = FALSE) |>
split_cols_by(
'TRT01A',
split_fun = remove_split_levels('Placebo'),
labels_var = 'rrisk_label'
) |>
### difference columns : just 1 column & analysis needs to be done on change
split_cols_by_multivar(c('CHG'), varlabels = c(' ')) |>
# the variable passed here in analyze is not used (STUDYID),
# it is a dummy var passing, the function a_summarize_aval_chg_diff_j
# grabs the required vars from cols_by_multivar calls
analyze('STUDYID',
afun = a_summarize_aval_chg_diff_j,
extra_args = list(
format_na_str = '-', d = 0,
ref_path = ref_path, variables = list(arm = 'TRT01A', covariates = NULL)
)
)
result <- build_table(lyt, ADEG)
result
#> Active Study Agent Risk Difference (%) (95% CI)
#> Blood Pressure ARMA Placebo ARMA vs Placebo
#> Study Visit n/N (%) Mean (CI) CFB (CI) n/N (%) Mean (CI) CFB (CI)
#> —————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> BP
#> Visit 1 7/7 (100.0%) 70.9 (58.2, 83.5) 2.6 (-0.8, 6.0) 3/3 (100.0%) 73.0 (-0.5, 146.5) 1.3 (-9.0, 11.7) 1.2 (-7.0, 9.5)