Analysis function 3-column presentation

Analysis 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.

Value

A function that can be used in an analyze function call

Details

See Description

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() |>
  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"
  ) |>
  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"
  ) |>
  split_cols_by_multivar(c("CHG"), varlabels = c(" ")) |>
  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)

Usage

Arguments

Value

Details

See also

Examples