Case Study: Clinical Tables
Source:vignettes/case-study-clinical-tables.Rmd
case-study-clinical-tables.RmdThe gt package contains the rx_adsl
dataset, which resembles the structure of a common ADSL ADaM dataset for
clinical trial data. Each record refers to demographic information for a
single subject in the fictional trial. Every column is equipped with a
label attribute allowing the users to get familiar with the data.
rx_adsl |> str()
#> tibble [182 × 14] (S3: tbl_df/tbl/data.frame)
#> $ STUDYID : chr [1:182] "GT01" "GT01" "GT01" "GT01" ...
#> ..- attr(*, "label")= chr "Unique Study Identifier"
#> $ STUDYIDN: chr [1:182] "4001" "4001" "4001" "4001" ...
#> ..- attr(*, "label")= chr "Unique Study Identifier (N)"
#> $ USUBJID : chr [1:182] "GT1000" "GT1001" "GT1002" "GT1003" ...
#> ..- attr(*, "label")= chr "Unique Subject Identifier"
#> $ TRTA : Factor w/ 2 levels "Placebo","Drug 1": NA 1 1 1 1 1 1 1 1 1 ...
#> ..- attr(*, "label")= chr "Actual Treatment"
#> $ TRTAN : num [1:182] 3 1 1 1 1 1 1 1 1 1 ...
#> ..- attr(*, "label")= chr "Actual Treatment (N)"
#> $ ITTFL : chr [1:182] "N" "Y" "Y" "Y" ...
#> ..- attr(*, "label")= chr "ITT Population Flag"
#> $ RANDFL : chr [1:182] "N" "Y" "Y" "Y" ...
#> ..- attr(*, "label")= chr "Randomization Flag"
#> $ SCRFREAS: chr [1:182] "WITHDRAWAL BY SUBJECT" "" "" "" ...
#> ..- attr(*, "label")= chr "Reason for Screen Failure"
#> $ AGE : int [1:182] 37 41 39 38 45 35 42 35 42 38 ...
#> ..- attr(*, "label")= chr "Age"
#> ..- attr(*, "units")= chr "Years"
#> $ AAGEGR1 : Factor w/ 2 levels "<40",">=40": 1 2 1 1 2 1 2 1 2 1 ...
#> ..- attr(*, "label")= chr "Age Group"
#> $ SEX : Factor w/ 3 levels "Male","Female",..: 1 1 2 1 1 2 2 1 1 2 ...
#> ..- attr(*, "label")= chr "Sex"
#> $ ETHNIC : Factor w/ 3 levels "Hispanic or Latino",..: 1 2 2 2 2 1 2 2 2 1 ...
#> ..- attr(*, "label")= chr "Ethnicity"
#> $ BLBMI : num [1:182] 33.8 33.4 30.5 22.9 23.9 ...
#> ..- attr(*, "label")= chr "Body Mass Index"
#> ..- attr(*, "units")= chr "kg/m2"
#> $ EVNTFL : chr [1:182] "" "Y" "Y" "N" ...
#> ..- attr(*, "label")= chr "Event Flag"Demographic Summary Tables
Let’s start with an example of a basic demographic summary table. In a first step, we use dplyr and tidyr to create a tibble with the shape of our desired table and then use gt functions to create the output table:
custom_summary <- function(df, group_var, sum_var) {
group_var <- rlang::ensym(group_var)
sum_var <- rlang::ensym(sum_var)
is_categorical <-
is.character(eval(expr(`$`(df, !!sum_var)))) |
is.factor(eval(expr(`$`(df, !!sum_var))))
if (is_categorical) {
category_lbl <-
sprintf("%s, n (%%)", attr(eval(expr(`$`(df, !!sum_var))), "label"))
df_out <-
df |>
dplyr::group_by(!!group_var) |>
dplyr::mutate(N = dplyr::n()) |>
dplyr::ungroup() |>
dplyr::group_by(!!group_var, !!sum_var) |>
dplyr::summarize(
val = dplyr::n(),
pct = dplyr::n()/mean(N),
.groups = "drop"
) |>
tidyr::pivot_wider(
id_cols = !!sum_var, names_from = !!group_var,
values_from = c(val, pct)
) |>
dplyr::rename(label = !!sum_var) |>
dplyr::mutate(
across(where(is.numeric), ~ifelse(is.na(.), 0, .)),
category = category_lbl
)
} else {
category_lbl <-
sprintf(
"%s (%s)",
attr(eval(expr(`$`(df, !!sum_var))), "label"),
attr(eval(expr(`$`(df, !!sum_var))), "units")
)
df_out <-
df |>
dplyr::group_by(!!group_var) |>
dplyr::summarize(
n = sum(!is.na(!!sum_var)),
mean = mean(!!sum_var, na.rm = TRUE),
sd = sd(!!sum_var, na.rm = TRUE),
median = median(!!sum_var, na.rm = TRUE),
min = min(!!sum_var, na.rm = TRUE),
max = max(!!sum_var, na.rm = TRUE),
min_max = NA,
.groups = "drop"
) |>
tidyr::pivot_longer(
cols = c(n, mean, median, min_max),
names_to = "label",
values_to = "val"
) |>
dplyr::mutate(
sd = ifelse(label == "mean", sd, NA),
max = ifelse(label == "min_max", max, NA),
min = ifelse(label == "min_max", min, NA),
label = dplyr::recode(
label,
"mean" = "Mean (SD)",
"min_max" = "Min - Max",
"median" = "Median"
)
) |>
tidyr::pivot_wider(
id_cols = label,
names_from = !!group_var,
values_from = c(val, sd, min, max)
) |>
dplyr::mutate(category = category_lbl)
}
return(df_out)
}
adsl_summary <-
dplyr::filter(rx_adsl, ITTFL == "Y") |>
(\(data) purrr::map_df(
.x = dplyr::vars(AGE, AAGEGR1, SEX, ETHNIC, BLBMI),
.f = \(x) custom_summary(df = data, group_var = TRTA, sum_var = !!x)
))()We can now start to expose our tibble with the summary of adsl
variables to gt using gt(). Values should
be grouped by category, with labels as rownames. In addition, we can
give our table a nice title and subtitle.
rx_adsl_tbl <-
adsl_summary |>
gt(
rowname_col = "label",
groupname_col = "category"
) |>
tab_header(
title = "x.x: Demographic Characteristics",
subtitle = "x.x.x: Demographic Characteristics - ITT Analysis Set"
)
rx_adsl_tbl| x.x: Demographic Characteristics | ||||||||||
| x.x.x: Demographic Characteristics - ITT Analysis Set | ||||||||||
| val_Placebo | val_Drug 1 | sd_Placebo | sd_Drug 1 | min_Placebo | min_Drug 1 | max_Placebo | max_Drug 1 | pct_Placebo | pct_Drug 1 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Age (Years) | ||||||||||
| n | 90.00000 | 90.00000 | NA | NA | NA | NA | NA | NA | NA | NA |
| Mean (SD) | 41.16667 | 39.17778 | 6.137470 | 5.620144 | NA | NA | NA | NA | NA | NA |
| Median | 41.00000 | 38.50000 | NA | NA | NA | NA | NA | NA | NA | NA |
| Min - Max | NA | NA | NA | NA | 27.00000 | 29.00000 | 56.00000 | 55.00000 | NA | NA |
| Age Group, n (%) | ||||||||||
| <40 | 36.00000 | 49.00000 | NA | NA | NA | NA | NA | NA | 0.40000000 | 0.54444444 |
| >=40 | 54.00000 | 41.00000 | NA | NA | NA | NA | NA | NA | 0.60000000 | 0.45555556 |
| Sex, n (%) | ||||||||||
| Male | 59.00000 | 57.00000 | NA | NA | NA | NA | NA | NA | 0.65555556 | 0.63333333 |
| Female | 25.00000 | 32.00000 | NA | NA | NA | NA | NA | NA | 0.27777778 | 0.35555556 |
| Undifferentiated | 6.00000 | 1.00000 | NA | NA | NA | NA | NA | NA | 0.06666667 | 0.01111111 |
| Ethnicity, n (%) | ||||||||||
| Hispanic or Latino | 38.00000 | 29.00000 | NA | NA | NA | NA | NA | NA | 0.42222222 | 0.32222222 |
| Not Hispanic or Latino | 46.00000 | 50.00000 | NA | NA | NA | NA | NA | NA | 0.51111111 | 0.55555556 |
| Missing | 6.00000 | 11.00000 | NA | NA | NA | NA | NA | NA | 0.06666667 | 0.12222222 |
| Body Mass Index (kg/m2) | ||||||||||
| n | 90.00000 | 90.00000 | NA | NA | NA | NA | NA | NA | NA | NA |
| Mean (SD) | 26.17161 | 27.16897 | 4.884631 | 4.973972 | NA | NA | NA | NA | NA | NA |
| Median | 26.04285 | 26.89705 | NA | NA | NA | NA | NA | NA | NA | NA |
| Min - Max | NA | NA | NA | NA | 18.08861 | 18.02748 | 34.48886 | 34.80553 | NA | NA |
As a first step, let’s try to format the columns, formatting counts,
min, max and medians with fmt_integer(), percentages with
fmt_percent(), and mean and sd values with
fmt_number() using 1 and 2
decimals, respectively. We are intentionally keeping the NA
values for now, as these will be needed in the cols_merge()
pattern in the next step.
rx_adsl_tbl <-
rx_adsl_tbl |>
fmt_integer(
columns = starts_with(c("val_", "min_", "max_")),
rows = label %in% c("n", "Median", "Min - Max")
) |>
fmt_percent(
columns = starts_with("pct_"),
decimals = 1
) |>
fmt_number(
columns = starts_with("val_"),
rows = label == "Mean (SD)",
decimals = 1
) |>
fmt_number(
columns = starts_with("sd_"),
rows = label == "Mean (SD)",
decimals = 2
)
rx_adsl_tbl| x.x: Demographic Characteristics | ||||||||||
| x.x.x: Demographic Characteristics - ITT Analysis Set | ||||||||||
| val_Placebo | val_Drug 1 | sd_Placebo | sd_Drug 1 | min_Placebo | min_Drug 1 | max_Placebo | max_Drug 1 | pct_Placebo | pct_Drug 1 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Age (Years) | ||||||||||
| n | 90 | 90 | NA | NA | NA | NA | NA | NA | NA | NA |
| Mean (SD) | 41.2 | 39.2 | 6.14 | 5.62 | NA | NA | NA | NA | NA | NA |
| Median | 41 | 38 | NA | NA | NA | NA | NA | NA | NA | NA |
| Min - Max | NA | NA | NA | NA | 27 | 29 | 56 | 55 | NA | NA |
| Age Group, n (%) | ||||||||||
| <40 | 36 | 49 | NA | NA | NA | NA | NA | NA | 40.0% | 54.4% |
| >=40 | 54 | 41 | NA | NA | NA | NA | NA | NA | 60.0% | 45.6% |
| Sex, n (%) | ||||||||||
| Male | 59 | 57 | NA | NA | NA | NA | NA | NA | 65.6% | 63.3% |
| Female | 25 | 32 | NA | NA | NA | NA | NA | NA | 27.8% | 35.6% |
| Undifferentiated | 6 | 1 | NA | NA | NA | NA | NA | NA | 6.7% | 1.1% |
| Ethnicity, n (%) | ||||||||||
| Hispanic or Latino | 38 | 29 | NA | NA | NA | NA | NA | NA | 42.2% | 32.2% |
| Not Hispanic or Latino | 46 | 50 | NA | NA | NA | NA | NA | NA | 51.1% | 55.6% |
| Missing | 6 | 11 | NA | NA | NA | NA | NA | NA | 6.7% | 12.2% |
| Body Mass Index (kg/m2) | ||||||||||
| n | 90 | 90 | NA | NA | NA | NA | NA | NA | NA | NA |
| Mean (SD) | 26.2 | 27.2 | 4.88 | 4.97 | NA | NA | NA | NA | NA | NA |
| Median | 26 | 27 | NA | NA | NA | NA | NA | NA | NA | NA |
| Min - Max | NA | NA | NA | NA | 18 | 18 | 34 | 35 | NA | NA |
This looks way better but our table still has a rather wide style. To
collapse the columns appropriately, we will use
cols_merge(), combining mean and SD, min and max, as well
as n and percentages, respectively. We will use the pattern
argument to specify our custom merging pattern.
rx_adsl_tbl <-
rx_adsl_tbl |>
cols_merge(
columns = c("val_Placebo", "pct_Placebo", "sd_Placebo", "min_Placebo", "max_Placebo"),
pattern = "<<{1}>><< ({2})>><< ({3})>><<{4} - {5}>>"
) |>
cols_merge(
columns = c("val_Drug 1", "pct_Drug 1", "sd_Drug 1", "min_Drug 1", "max_Drug 1"),
pattern = "<<{1}>><< ({2})>><< ({3})>><<{4} - {5}>>"
)
rx_adsl_tbl| x.x: Demographic Characteristics | ||
| x.x.x: Demographic Characteristics - ITT Analysis Set | ||
| val_Placebo | val_Drug 1 | |
|---|---|---|
| Age (Years) | ||
| n | 90 | 90 |
| Mean (SD) | 41.2 (6.14) | 39.2 (5.62) |
| Median | 41 | 38 |
| Min - Max | 27 - 56 | 29 - 55 |
| Age Group, n (%) | ||
| <40 | 36 (40.0%) | 49 (54.4%) |
| >=40 | 54 (60.0%) | 41 (45.6%) |
| Sex, n (%) | ||
| Male | 59 (65.6%) | 57 (63.3%) |
| Female | 25 (27.8%) | 32 (35.6%) |
| Undifferentiated | 6 (6.7%) | 1 (1.1%) |
| Ethnicity, n (%) | ||
| Hispanic or Latino | 38 (42.2%) | 29 (32.2%) |
| Not Hispanic or Latino | 46 (51.1%) | 50 (55.6%) |
| Missing | 6 (6.7%) | 11 (12.2%) |
| Body Mass Index (kg/m2) | ||
| n | 90 | 90 |
| Mean (SD) | 26.2 (4.88) | 27.2 (4.97) |
| Median | 26 | 27 |
| Min - Max | 18 - 34 | 18 - 35 |
Now that looks more like a demographic table. But let’s take a step
back and understand the merging pattern. { } are used to
arrange the single column values in a row-wise fashion. The number in
curly braces corresponds to the order specified in the
columns = argument. We use << >>
to surround spans of text that will be omitted if any of the values
within contain missing values. Our first column in the call to
cols_merge() contains values for n’s, means and medians and
is printed if the values are not missing (meaning for the cells for
numeric and categorical n’s, means and medians but not for min and max).
The SD and percentages for categorical grouping variables are then
appended to the cells for mean and categorical n’s, because these are
the only rows that contain non-missing values. All of the previous
aspects are ignored for the min and max row, as n’s, percentages, SD’s
and medians are missing. Here, only min and max are arranged.
We can now start to look in to style features. Let us indent the
values in the stub using tab_stub_indent() and left-align
the title with opt_align_table_header().
rx_adsl_tbl <-
rx_adsl_tbl |>
tab_stub_indent(
rows = everything(),
indent = 5
) |>
opt_align_table_header(align = "left")
rx_adsl_tbl| x.x: Demographic Characteristics | ||
| x.x.x: Demographic Characteristics - ITT Analysis Set | ||
| val_Placebo | val_Drug 1 | |
|---|---|---|
| Age (Years) | ||
| n | 90 | 90 |
| Mean (SD) | 41.2 (6.14) | 39.2 (5.62) |
| Median | 41 | 38 |
| Min - Max | 27 - 56 | 29 - 55 |
| Age Group, n (%) | ||
| <40 | 36 (40.0%) | 49 (54.4%) |
| >=40 | 54 (60.0%) | 41 (45.6%) |
| Sex, n (%) | ||
| Male | 59 (65.6%) | 57 (63.3%) |
| Female | 25 (27.8%) | 32 (35.6%) |
| Undifferentiated | 6 (6.7%) | 1 (1.1%) |
| Ethnicity, n (%) | ||
| Hispanic or Latino | 38 (42.2%) | 29 (32.2%) |
| Not Hispanic or Latino | 46 (51.1%) | 50 (55.6%) |
| Missing | 6 (6.7%) | 11 (12.2%) |
| Body Mass Index (kg/m2) | ||
| n | 90 | 90 |
| Mean (SD) | 26.2 (4.88) | 27.2 (4.97) |
| Median | 26 | 27 |
| Min - Max | 18 - 34 | 18 - 35 |
Let’s now change the column width of our Placebo and Drug 1 columns
and align all values to the center, making use of
cols_width() and cols_align().
rx_adsl_tbl <-
rx_adsl_tbl |>
cols_width(
starts_with("val_") ~ px(200),
1 ~ px(250)
) |>
cols_align(
align = "center",
columns = starts_with("val_")
)
rx_adsl_tbl| x.x: Demographic Characteristics | ||
| x.x.x: Demographic Characteristics - ITT Analysis Set | ||
| val_Placebo | val_Drug 1 | |
|---|---|---|
| Age (Years) | ||
| n | 90 | 90 |
| Mean (SD) | 41.2 (6.14) | 39.2 (5.62) |
| Median | 41 | 38 |
| Min - Max | 27 - 56 | 29 - 55 |
| Age Group, n (%) | ||
| <40 | 36 (40.0%) | 49 (54.4%) |
| >=40 | 54 (60.0%) | 41 (45.6%) |
| Sex, n (%) | ||
| Male | 59 (65.6%) | 57 (63.3%) |
| Female | 25 (27.8%) | 32 (35.6%) |
| Undifferentiated | 6 (6.7%) | 1 (1.1%) |
| Ethnicity, n (%) | ||
| Hispanic or Latino | 38 (42.2%) | 29 (32.2%) |
| Not Hispanic or Latino | 46 (51.1%) | 50 (55.6%) |
| Missing | 6 (6.7%) | 11 (12.2%) |
| Body Mass Index (kg/m2) | ||
| n | 90 | 90 |
| Mean (SD) | 26.2 (4.88) | 27.2 (4.97) |
| Median | 26 | 27 |
| Min - Max | 18 - 34 | 18 - 35 |
In a final step we can now take care of the column names and assign
something more meaningful. Out column header should be the name of the
study intervention together with the respective subject count. To make
use of cols_label()’s ability to handle lists, we summarize
our new column labels in a named list.
### Count subjects per arm and summarize values in a list
arm_n <-
rx_adsl |>
dplyr::filter(ITTFL == "Y") |>
dplyr::group_by(TRTA) |>
dplyr::summarize(
lbl = sprintf("%s N=%i (100%%)", unique(TRTA), dplyr::n()),
.groups = "drop"
) |>
dplyr::arrange(TRTA)
collbl_list <- as.list(arm_n$lbl)
names(collbl_list) <- paste0("val_", arm_n$TRTA)
rx_adsl_tbl <-
rx_adsl_tbl |>
cols_label(.list = collbl_list)
rx_adsl_tbl| x.x: Demographic Characteristics | ||
| x.x.x: Demographic Characteristics - ITT Analysis Set | ||
| Placebo N=90 (100%) | Drug 1 N=90 (100%) | |
|---|---|---|
| Age (Years) | ||
| n | 90 | 90 |
| Mean (SD) | 41.2 (6.14) | 39.2 (5.62) |
| Median | 41 | 38 |
| Min - Max | 27 - 56 | 29 - 55 |
| Age Group, n (%) | ||
| <40 | 36 (40.0%) | 49 (54.4%) |
| >=40 | 54 (60.0%) | 41 (45.6%) |
| Sex, n (%) | ||
| Male | 59 (65.6%) | 57 (63.3%) |
| Female | 25 (27.8%) | 32 (35.6%) |
| Undifferentiated | 6 (6.7%) | 1 (1.1%) |
| Ethnicity, n (%) | ||
| Hispanic or Latino | 38 (42.2%) | 29 (32.2%) |
| Not Hispanic or Latino | 46 (51.1%) | 50 (55.6%) |
| Missing | 6 (6.7%) | 11 (12.2%) |
| Body Mass Index (kg/m2) | ||
| n | 90 | 90 |
| Mean (SD) | 26.2 (4.88) | 27.2 (4.97) |
| Median | 26 | 27 |
| Min - Max | 18 - 34 | 18 - 35 |
Response / Event Rate Analysis Tables
In another table, we can summarize the number of subjects with an
event per intervention in the subgroup defined by the age groups. Within
each intervention group we are counting the number and percentage of
participants with an event (EVNTFL == "Y") as well as the
total number of participants. The number of participants with an event
divided by the number without an event are the odds of experiencing the
event per study intervention. The odds ratio is then computed as the
odds under Drug 1 divided by the odds under Placebo.
The below code performs the calculation outlined above within the
subgroup defined by AAGEGR1, where confidence intervals
around the event rates are computed using the Clopper Pearson
method.
rx_responders <-
rx_adsl |>
dplyr::filter(ITTFL == "Y") |>
dplyr::group_by(TRTA, AAGEGR1) |>
dplyr::summarize(
n_resp = sum(EVNTFL == "Y"),
n_total = dplyr::n(),
pct = 100 * sum(EVNTFL == "Y") / dplyr::n(),
ci_up = 100 * (
1 + (dplyr::n() - sum(EVNTFL == "Y")) / (
(sum(EVNTFL == "Y") + 1) * qf(
0.975,
2 * (sum(EVNTFL == "Y") + 1),
2 * (dplyr::n() - sum(EVNTFL == "Y"))
)
)
)^(-1),
ci_low = ifelse(
sum(EVNTFL == "Y") == 0,
0,
100 * (
1 + (dplyr::n() - sum(EVNTFL == "Y") + 1) /
(sum(EVNTFL == "Y") * qf(
0.025,
2 * sum(EVNTFL == "Y"),
2 * (dplyr::n() - sum(EVNTFL == "Y") + 1)
)
)
)^(-1)
),
odds = sum(EVNTFL == "Y") / (dplyr::n() - sum(EVNTFL == "Y")),
.groups = "drop"
) |>
tidyr::pivot_wider(
id_cols = AAGEGR1,
names_from = TRTA,
values_from = c(n_resp, n_total, pct, ci_up, ci_low, odds)
) |>
dplyr::mutate(
or = ifelse(
odds_Placebo == 0,
NA_real_,
!! sym("odds_Drug 1") / odds_Placebo
),
or_ci_low = exp(
log(or) - qnorm(0.975) * sqrt(
1 / n_resp_Placebo +
1 / !!sym("n_resp_Drug 1") +
1 / (n_total_Placebo - n_resp_Placebo) +
1 / (!!sym("n_total_Drug 1") - !!sym("n_resp_Drug 1"))
)
),
or_ci_up = exp(
log(or) + qnorm(0.975) * sqrt(
1 / n_resp_Placebo +
1 / !!sym("n_resp_Drug 1") +
1 / (n_total_Placebo - n_resp_Placebo) +
1 / (!!sym("n_total_Drug 1") - !!sym("n_resp_Drug 1"))
)
)
) |>
dplyr::select(-tidyselect::starts_with("odds_"))Let’s first create a basic gt table with a
left-aligned table title and subtitle. Here we are using
tab_header() and opt_align_table_header()
again.
rx_resp_tbl <- rx_responders |>
gt() |>
tab_header(
title = "x.x: Efficacy Data",
subtitle = "x.x.x: Occurence of Event per Subgroup - {gt} Analysis Set"
) |>
opt_align_table_header(align = "left")
rx_resp_tbl| x.x: Efficacy Data | |||||||||||||
| x.x.x: Occurence of Event per Subgroup - {gt} Analysis Set | |||||||||||||
| Age Group | n_resp_Placebo | n_resp_Drug 1 | n_total_Placebo | n_total_Drug 1 | pct_Placebo | pct_Drug 1 | ci_up_Placebo | ci_up_Drug 1 | ci_low_Placebo | ci_low_Drug 1 | or | or_ci_low | or_ci_up |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <40 | 17 | 33 | 36 | 49 | 47.22222 | 67.34694 | 64.51362 | 80.05147 | 30.40506 | 52.45993 | 2.305147 | 0.950602 | 5.589829 |
| >=40 | 23 | 29 | 54 | 41 | 42.59259 | 70.73171 | 56.79174 | 83.87014 | 29.23473 | 54.46260 | 3.257246 | 1.375207 | 7.714953 |
Next, we are formatting the columns for counts to integers with
fmt_integer(), percentages and CI’s around percentages as
numbers with one decimal and odds ratio and the CI around the odds ratio
as numbers with two decimals, in both cases using
fmt_number().
rx_resp_tbl <-
rx_resp_tbl |>
fmt_integer(columns = starts_with("n_")) |>
fmt_number(columns = starts_with(c("pct_", "ci_")), decimals = 1) |>
fmt_number(columns = starts_with("or"), decimals = 2)
rx_resp_tbl| x.x: Efficacy Data | |||||||||||||
| x.x.x: Occurence of Event per Subgroup - {gt} Analysis Set | |||||||||||||
| Age Group | n_resp_Placebo | n_resp_Drug 1 | n_total_Placebo | n_total_Drug 1 | pct_Placebo | pct_Drug 1 | ci_up_Placebo | ci_up_Drug 1 | ci_low_Placebo | ci_low_Drug 1 | or | or_ci_low | or_ci_up |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <40 | 17 | 33 | 36 | 49 | 47.2 | 67.3 | 64.5 | 80.1 | 30.4 | 52.5 | 2.31 | 0.95 | 5.59 |
| >=40 | 23 | 29 | 54 | 41 | 42.6 | 70.7 | 56.8 | 83.9 | 29.2 | 54.5 | 3.26 | 1.38 | 7.71 |
We can now merge the columns for participants with events, total
number of participants and percentage of participants with events, as
well as the 95% CI’s around the event rate using
cols_merge(). To indicate the intervention group we are
adding tab spanners with tab_spanner().
rx_resp_tbl <-
rx_resp_tbl |>
cols_merge(
columns = c("n_resp_Placebo", "n_total_Placebo", "pct_Placebo"),
pattern = "{1}/{2} ({3})"
) |>
cols_merge(
columns = c("n_resp_Drug 1", "n_total_Drug 1", "pct_Drug 1"),
pattern = "{1}/{2} ({3})"
) |>
cols_merge(
columns = c("ci_low_Placebo", "ci_up_Placebo"),
pattern = "[{1}, {2}]"
) |>
cols_merge(
columns = c("ci_low_Drug 1", "ci_up_Drug 1"),
pattern = "[{1}, {2}]"
) |>
cols_merge(
columns = c("or_ci_low", "or_ci_up"),
pattern = "[{1}, {2}]"
) |>
tab_spanner(
label = "Drug 1",
columns = c("n_resp_Drug 1", "ci_low_Drug 1")
) |>
tab_spanner(
label = "Placebo",
columns = c("n_resp_Placebo", "ci_low_Placebo")
)
rx_resp_tbl| x.x: Efficacy Data | ||||||
| x.x.x: Occurence of Event per Subgroup - {gt} Analysis Set | ||||||
| Age Group | Placebo | Drug 1 | or | or_ci_low | ||
|---|---|---|---|---|---|---|
| n_resp_Placebo | ci_low_Placebo | n_resp_Drug 1 | ci_low_Drug 1 | |||
| <40 | 17/36 (47.2) | [30.4, 64.5] | 33/49 (67.3) | [52.5, 80.1] | 2.31 | [0.95, 5.59] |
| >=40 | 23/54 (42.6) | [29.2, 56.8] | 29/41 (70.7) | [54.5, 83.9] | 3.26 | [1.38, 7.71] |
The table is looking way better now. Let’s now group the two
categories and highlight the fact that these are actually age subgroups.
We are using tab_row_group() to manually add a row group
label Age.
rx_resp_tbl <-
rx_resp_tbl |>
tab_row_group(
label = "Age",
rows = everything()
)
rx_resp_tbl| x.x: Efficacy Data | ||||||
| x.x.x: Occurence of Event per Subgroup - {gt} Analysis Set | ||||||
| Age Group | Placebo | Drug 1 | or | or_ci_low | ||
|---|---|---|---|---|---|---|
| n_resp_Placebo | ci_low_Placebo | n_resp_Drug 1 | ci_low_Drug 1 | |||
| Age | ||||||
| <40 | 17/36 (47.2) | [30.4, 64.5] | 33/49 (67.3) | [52.5, 80.1] | 2.31 | [0.95, 5.59] |
| >=40 | 23/54 (42.6) | [29.2, 56.8] | 29/41 (70.7) | [54.5, 83.9] | 3.26 | [1.38, 7.71] |
Next, we’ll take care of the column labels. As we now have the
tab_row_group() label in place, we no longer need the label
for the first column and can assign an empty string. Also, because of
the two tab spanners, we can assign equal column labels for event rates
and 95% CI’s in both intervention groups.
Using cols_width() and cols_align() we can
apply a more convenient column width and left-align the first
column.
rx_resp_tbl <-
rx_resp_tbl |>
cols_align(
align = "center",
columns = starts_with(c("n_", "ci", "or"))
) |>
cols_label(
.list = c(
"AAGEGR1" = "",
"n_resp_Placebo" = "Event Rate (%)",
"ci_low_Placebo" = "[95% CI]",
"n_resp_Drug 1" = "Event Rate (%)",
"ci_low_Drug 1" = "[95% CI]",
"or" = "Odds ratio",
"or_ci_low" = "[95% CI]"
)
) |>
cols_width(
1 ~ px(80),
everything() ~ px(120)
) |>
cols_align(align = "left", columns = 1)
rx_resp_tbl| x.x: Efficacy Data | ||||||
| x.x.x: Occurence of Event per Subgroup - {gt} Analysis Set | ||||||
| Placebo | Drug 1 | Odds ratio | [95% CI] | |||
|---|---|---|---|---|---|---|
| Event Rate (%) | [95% CI] | Event Rate (%) | [95% CI] | |||
| Age | ||||||
| <40 | 17/36 (47.2) | [30.4, 64.5] | 33/49 (67.3) | [52.5, 80.1] | 2.31 | [0.95, 5.59] |
| >=40 | 23/54 (42.6) | [29.2, 56.8] | 29/41 (70.7) | [54.5, 83.9] | 3.26 | [1.38, 7.71] |
Finally, we make use of tab_footnote() and can add a
footnote to the columns with the 95% CI’s around event rates, indicating
that these were derived from the Clopper-Pearson method. To change the
default symbol choice of tab_footnote() from numbers to
letters, we add tab_options(footnote.marks = letters).
rx_resp_tbl <-
rx_resp_tbl |>
tab_footnote(
footnote = "Event rate 95% exact confidence interval uses the Clopper−Pearson method.",
locations = cells_column_labels(
columns = c("ci_low_Placebo", "ci_low_Drug 1")
),
placement = "right"
) |>
tab_options(footnotes.marks = letters)
rx_resp_tbl| x.x: Efficacy Data | ||||||
| x.x.x: Occurence of Event per Subgroup - {gt} Analysis Set | ||||||
| Placebo | Drug 1 | Odds ratio | [95% CI] | |||
|---|---|---|---|---|---|---|
| Event Rate (%) | [95% CI]a | Event Rate (%) | [95% CI]a | |||
| Age | ||||||
| <40 | 17/36 (47.2) | [30.4, 64.5] | 33/49 (67.3) | [52.5, 80.1] | 2.31 | [0.95, 5.59] |
| >=40 | 23/54 (42.6) | [29.2, 56.8] | 29/41 (70.7) | [54.5, 83.9] | 3.26 | [1.38, 7.71] |
| a Event rate 95% exact confidence interval uses the Clopper−Pearson method. | ||||||
Protocol Deviation Table
For the summary table for protocol deviations (PDs) we will use a
second CDISC-flavored dataset, namely rx_addv. This dataset
contains a summary row, indicating whether a subject in the ITT
population in rx_adsl experienced at least one major PD or
not. In addition to the subject level summary, individual PDs are
summarized.
rx_addv |> str()
#> tibble [291 × 20] (S3: tbl_df/tbl/data.frame)
#> $ STUDYID : chr [1:291] "GT01" "GT01" "GT01" "GT01" ...
#> ..- attr(*, "label")= chr "Unique Study Identifier"
#> $ STUDYIDN: chr [1:291] "4001" "4001" "4001" "4001" ...
#> ..- attr(*, "label")= chr "Unique Study Identifier (N)"
#> $ USUBJID : chr [1:291] "GT1001" "GT1002" "GT1002" "GT1003" ...
#> ..- attr(*, "label")= chr "Unique Subject Identifier"
#> $ TRTA : Factor w/ 2 levels "Placebo","Drug 1": 1 1 1 1 1 1 1 1 1 1 ...
#> ..- attr(*, "label")= chr "Actual Treatment"
#> $ TRTAN : num [1:291] 1 1 1 1 1 1 1 1 1 1 ...
#> ..- attr(*, "label")= chr "Actual Treatment (N)"
#> $ ITTFL : chr [1:291] "Y" "Y" "Y" "Y" ...
#> ..- attr(*, "label")= chr "ITT Population Flag"
#> $ AGE : int [1:291] 41 39 39 38 38 38 45 45 35 35 ...
#> ..- attr(*, "label")= chr "Age"
#> ..- attr(*, "units")= chr "Years"
#> $ AAGEGR1 : Factor w/ 2 levels "<40",">=40": 2 1 1 1 1 1 2 2 1 1 ...
#> ..- attr(*, "label")= chr "Age Group"
#> $ SEX : Factor w/ 3 levels "Male","Female",..: 1 2 2 1 1 1 1 1 2 2 ...
#> ..- attr(*, "label")= chr "Sex"
#> $ ETHNIC : Factor w/ 3 levels "Hispanic or Latino",..: 2 2 2 2 2 2 2 2 1 1 ...
#> ..- attr(*, "label")= chr "Ethnicity"
#> $ BLBMI : num [1:291] 33.4 30.5 30.5 22.9 22.9 ...
#> ..- attr(*, "label")= chr "Body Mass Index"
#> ..- attr(*, "units")= chr "kg/m2"
#> $ DVTERM : chr [1:291] "" "" "Lab values not taken at month 3" "" ...
#> ..- attr(*, "label")= chr "Protocol Deviation Term"
#> $ PARAMCD : Factor w/ 6 levels "PDANYM","PDEV01",..: 1 1 3 1 2 3 1 2 1 1 ...
#> ..- attr(*, "label")= chr "Parameter Code"
#> $ PARAM : Factor w/ 6 levels "At least one major Protocol Deviation",..: 1 1 3 1 2 3 1 2 1 1 ...
#> ..- attr(*, "label")= chr "Parameter"
#> $ PARCAT1 : chr [1:291] "OVERALL" "OVERALL" "PROTOCOL DEVIATION" "OVERALL" ...
#> ..- attr(*, "label")= chr "Parameter Category 1"
#> $ DVCAT : chr [1:291] "" "" "Major" "" ...
#> ..- attr(*, "label")= chr "Protocol Deviation Category"
#> $ ACAT1 : chr [1:291] "" "" "Study Procedures Criteria Deviations" "" ...
#> ..- attr(*, "label")= chr "Analysis Category 1"
#> $ AVAL : num [1:291] 0 1 1 0 1 1 1 1 0 1 ...
#> ..- attr(*, "label")= chr "Analysis Value"
#> $ CRIT1 : chr [1:291] "COVID-19 Related" "COVID-19 Related" "COVID-19 Related" "COVID-19 Related" ...
#> ..- attr(*, "label")= chr "Analysis Criterion 1"
#> $ CRIT1FL : chr [1:291] "N" "N" "N" "N" ...
#> ..- attr(*, "label")= chr "Criterion 1 Evaluation Flag"We would now like to build a table to summarize overall and counts of individual PDs by treatment arm and furthermore indicate, whether the PD was related to COVID-19 or not. In order to build this table, we first need to apply some data wrangling with functions from dplyr and tidyr.
addv_sum <-
rx_addv |>
dplyr::group_by(TRTA) |>
dplyr::mutate(
NTOT = n_distinct(USUBJID),
.groups = "drop"
) |>
dplyr::group_by(TRTA, PARCAT1, PARAM, CRIT1FL) |>
dplyr::summarize(
n = sum(AVAL, na.rm = TRUE),
pct = 100 * sum(AVAL, na.rm = TRUE) / mean(NTOT),
.groups = "drop"
) |>
tidyr::pivot_wider(
id_cols = c(PARCAT1, PARAM),
names_from = c(TRTA, CRIT1FL),
values_from = c(n, pct)
) |>
dplyr::mutate(across(where(is.numeric), ~ifelse(is.na(.), 0, .))) |>
dplyr::add_row(PARAM = "Subjects with at least:", .before = 1)
addv_sum
#> # A tibble: 7 × 10
#> PARCAT1 PARAM n_Placebo_N n_Placebo_Y `n_Drug 1_N` `n_Drug 1_Y` pct_Placebo_N
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 NA Subj… NA NA NA NA NA
#> 2 OVERALL At l… 24 15 23 5 26.7
#> 3 PROTOCO… GTQ … 7 0 12 0 7.78
#> 4 PROTOCO… Lab … 12 3 7 0 13.3
#> 5 PROTOCO… GTQ … 7 2 7 1 7.78
#> 6 PROTOCO… Mont… 9 4 3 1 10
#> 7 PROTOCO… Mont… 9 8 9 3 10
#> # ℹ 3 more variables: pct_Placebo_Y <dbl>, `pct_Drug 1_N` <dbl>,
#> # `pct_Drug 1_Y` <dbl>This is the dataset that serves as a starting point for
gt. We will start by exposing the dataset to
gt and add our usual left-aligned headers.
addv_tbl <-
addv_sum |>
gt(rowname_col = "PARAM") |>
tab_header(
title = "xx.x: Demographic and Baseline Data",
subtitle = "xx.x.x: Major Protocol Deviations and Relationship to COVID-19 - ITT Set"
) |>
opt_align_table_header(align = "left")
addv_tbl| xx.x: Demographic and Baseline Data | |||||||||
| xx.x.x: Major Protocol Deviations and Relationship to COVID-19 - ITT Set | |||||||||
| PARCAT1 | n_Placebo_N | n_Placebo_Y | n_Drug 1_N | n_Drug 1_Y | pct_Placebo_N | pct_Placebo_Y | pct_Drug 1_N | pct_Drug 1_Y | |
|---|---|---|---|---|---|---|---|---|---|
| Subjects with at least: | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| At least one major Protocol Deviation | OVERALL | 24 | 15 | 23 | 5 | 26.666667 | 16.666667 | 25.555556 | 5.555556 |
| GTQ not completed month 3. | PROTOCOL DEVIATION | 7 | 0 | 12 | 0 | 7.777778 | 0.000000 | 13.333333 | 0.000000 |
| Lab values not taken month 3. | PROTOCOL DEVIATION | 12 | 3 | 7 | 0 | 13.333333 | 3.333333 | 7.777778 | 0.000000 |
| GTQ not completed month 6. | PROTOCOL DEVIATION | 7 | 2 | 7 | 1 | 7.777778 | 2.222222 | 7.777778 | 1.111111 |
| Month 3 Visit not done. | PROTOCOL DEVIATION | 9 | 4 | 3 | 1 | 10.000000 | 4.444444 | 3.333333 | 1.111111 |
| Month 6 Visit not done. | PROTOCOL DEVIATION | 9 | 8 | 9 | 3 | 10.000000 | 8.888889 | 10.000000 | 3.333333 |
In a next step, we would like to create a summary row for all
individual PDs to get the overall number of individual PDs, as well as
the corresponding percentage. For this, we will first create a row group
for individual PDs using tab_row_group() applied to all
rows where PARCAT1 is equal to
PROTOCOL DEVIATIONS. Then, we’ll arrange the order of the
row groups to list individual PDs after the overall summary. Finally, we
can create a summary row, using summary_rows() and
gt sums up all columns with n’s and percentages for us
(other summary functions are possible, but fn = 'sum' does
the job for us).
addv_tbl <-
addv_tbl |>
tab_row_group(
label = " ",
rows = PARCAT1 == "PROTOCOL DEVIATION"
) |>
row_group_order(groups = c(NA, " ")) |>
summary_rows(
groups = " ",
columns = where(is.numeric),
fns = list(label = "Study Procedure Deviations", fn = "sum"),
side = "top"
)
addv_tbl| xx.x: Demographic and Baseline Data | |||||||||
| xx.x.x: Major Protocol Deviations and Relationship to COVID-19 - ITT Set | |||||||||
| PARCAT1 | n_Placebo_N | n_Placebo_Y | n_Drug 1_N | n_Drug 1_Y | pct_Placebo_N | pct_Placebo_Y | pct_Drug 1_N | pct_Drug 1_Y | |
|---|---|---|---|---|---|---|---|---|---|
| Subjects with at least: | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| At least one major Protocol Deviation | OVERALL | 24 | 15 | 23 | 5 | 26.666667 | 16.666667 | 25.555556 | 5.555556 |
| Study Procedure Deviations | — | 44 | 17 | 38 | 5 | 48.88889 | 18.88889 | 42.22222 | 5.555556 |
| GTQ not completed month 3. | PROTOCOL DEVIATION | 7 | 0 | 12 | 0 | 7.777778 | 0.000000 | 13.333333 | 0.000000 |
| Lab values not taken month 3. | PROTOCOL DEVIATION | 12 | 3 | 7 | 0 | 13.333333 | 3.333333 | 7.777778 | 0.000000 |
| GTQ not completed month 6. | PROTOCOL DEVIATION | 7 | 2 | 7 | 1 | 7.777778 | 2.222222 | 7.777778 | 1.111111 |
| Month 3 Visit not done. | PROTOCOL DEVIATION | 9 | 4 | 3 | 1 | 10.000000 | 4.444444 | 3.333333 | 1.111111 |
| Month 6 Visit not done. | PROTOCOL DEVIATION | 9 | 8 | 9 | 3 | 10.000000 | 8.888889 | 10.000000 | 3.333333 |
We only kept the column PARCAT1 to facilitate the
generation of the row group. We can hide this column now using
cols_hide():
addv_tbl <-
addv_tbl |>
cols_hide(columns = "PARCAT1")
addv_tbl| xx.x: Demographic and Baseline Data | ||||||||
| xx.x.x: Major Protocol Deviations and Relationship to COVID-19 - ITT Set | ||||||||
| n_Placebo_N | n_Placebo_Y | n_Drug 1_N | n_Drug 1_Y | pct_Placebo_N | pct_Placebo_Y | pct_Drug 1_N | pct_Drug 1_Y | |
|---|---|---|---|---|---|---|---|---|
| Subjects with at least: | NA | NA | NA | NA | NA | NA | NA | NA |
| At least one major Protocol Deviation | 24 | 15 | 23 | 5 | 26.666667 | 16.666667 | 25.555556 | 5.555556 |
| Study Procedure Deviations | 44 | 17 | 38 | 5 | 48.88889 | 18.88889 | 42.22222 | 5.555556 |
| GTQ not completed month 3. | 7 | 0 | 12 | 0 | 7.777778 | 0.000000 | 13.333333 | 0.000000 |
| Lab values not taken month 3. | 12 | 3 | 7 | 0 | 13.333333 | 3.333333 | 7.777778 | 0.000000 |
| GTQ not completed month 6. | 7 | 2 | 7 | 1 | 7.777778 | 2.222222 | 7.777778 | 1.111111 |
| Month 3 Visit not done. | 9 | 4 | 3 | 1 | 10.000000 | 4.444444 | 3.333333 | 1.111111 |
| Month 6 Visit not done. | 9 | 8 | 9 | 3 | 10.000000 | 8.888889 | 10.000000 | 3.333333 |
Now that the table has roughly the right shape, we can start to format all numeric columns and merge columns for n’s and percentages by intervention group and COVID-19 relationship flag.
addv_tbl <-
addv_tbl |>
sub_missing(
rows = 1,
missing_text = ""
) |>
fmt_number(
columns = starts_with("pct"),
decimals = 1
) |>
cols_merge_n_pct(
col_n = "n_Placebo_Y",
col_pct = "pct_Placebo_Y"
) |>
cols_merge_n_pct(
col_n = "n_Placebo_N",
col_pct = "pct_Placebo_N"
) |>
cols_merge_n_pct(
col_n = "n_Drug 1_Y",
col_pct = "pct_Drug 1_Y"
) |>
cols_merge_n_pct(
col_n = "n_Drug 1_N",
col_pct = "pct_Drug 1_N"
)
addv_tbl| xx.x: Demographic and Baseline Data | ||||
| xx.x.x: Major Protocol Deviations and Relationship to COVID-19 - ITT Set | ||||
| n_Placebo_N | n_Placebo_Y | n_Drug 1_N | n_Drug 1_Y | |
|---|---|---|---|---|
| Subjects with at least: | ||||
| At least one major Protocol Deviation | 24 (26.7) | 15 (16.7) | 23 (25.6) | 5 (5.6) |
| Study Procedure Deviations | 44 | 17 | 38 | 5 |
| GTQ not completed month 3. | 7 (7.8) | 0 | 12 (13.3) | 0 |
| Lab values not taken month 3. | 12 (13.3) | 3 (3.3) | 7 (7.8) | 0 |
| GTQ not completed month 6. | 7 (7.8) | 2 (2.2) | 7 (7.8) | 1 (1.1) |
| Month 3 Visit not done. | 9 (10.0) | 4 (4.4) | 3 (3.3) | 1 (1.1) |
| Month 6 Visit not done. | 9 (10.0) | 8 (8.9) | 9 (10.0) | 3 (3.3) |
This looks more like a PD table! We can now modify the column names and create a cascade of column spanners.
addv_tbl <-
addv_tbl |>
tab_spanner(
label = md("COVID-19 Related"),
columns = c("n_Placebo_Y", "n_Placebo_N"),
id = "cov_pla"
) |>
tab_spanner(
label = md("COVID-19 Related"),
columns = c("n_Drug 1_Y", "n_Drug 1_N"),
id = "cov_dru"
) |>
tab_spanner(
label = md("Placebo \n N=90 (100%) \n n (%)"),
columns = c("n_Placebo_Y", "n_Placebo_N")
) |>
tab_spanner(
label = md("Drug 1 \n N=90 (100%) \n n (%)"),
columns = c("n_Drug 1_Y", "n_Drug 1_N")
) |>
cols_label(
.list = list(
"n_Placebo_Y" = "Yes",
"n_Placebo_N" = "No",
"n_Drug 1_Y" = "Yes",
"n_Drug 1_N" = "No"
)
) |>
tab_style(
style = cell_text(align = "center"),
locations = cells_column_spanners(spanners = everything())
)
addv_tbl| xx.x: Demographic and Baseline Data | ||||
| xx.x.x: Major Protocol Deviations and Relationship to COVID-19 - ITT Set | ||||
|
Placebo N=90 (100%) n (%) |
Drug 1 N=90 (100%) n (%) |
|||
|---|---|---|---|---|
| COVID-19 Related | COVID-19 Related | |||
| Yes | No | Yes | No | |
| Subjects with at least: | ||||
| At least one major Protocol Deviation | 15 (16.7) | 24 (26.7) | 5 (5.6) | 23 (25.6) |
| Study Procedure Deviations | 17 | 44 | 5 | 38 |
| GTQ not completed month 3. | 0 | 7 (7.8) | 0 | 12 (13.3) |
| Lab values not taken month 3. | 3 (3.3) | 12 (13.3) | 0 | 7 (7.8) |
| GTQ not completed month 6. | 2 (2.2) | 7 (7.8) | 1 (1.1) | 7 (7.8) |
| Month 3 Visit not done. | 4 (4.4) | 9 (10.0) | 1 (1.1) | 3 (3.3) |
| Month 6 Visit not done. | 8 (8.9) | 9 (10.0) | 3 (3.3) | 9 (10.0) |
We can now add a footnote, indicating that subjects can have more
than one PD during the course of the study. The footnote is added with
tab_footnote() to the row
At least one major Protocol Deviation.
addv_tbl <-
addv_tbl |>
tab_footnote(
footnote = "Subjects can have more than one Protocol Deviation throughout the study.",
locations = cells_stub(rows = c("At least one major Protocol Deviation")),
placement = "right"
)
addv_tbl| xx.x: Demographic and Baseline Data | ||||
| xx.x.x: Major Protocol Deviations and Relationship to COVID-19 - ITT Set | ||||
|
Placebo N=90 (100%) n (%) |
Drug 1 N=90 (100%) n (%) |
|||
|---|---|---|---|---|
| COVID-19 Related | COVID-19 Related | |||
| Yes | No | Yes | No | |
| Subjects with at least: | ||||
| At least one major Protocol Deviation1 | 15 (16.7) | 24 (26.7) | 5 (5.6) | 23 (25.6) |
| Study Procedure Deviations | 17 | 44 | 5 | 38 |
| GTQ not completed month 3. | 0 | 7 (7.8) | 0 | 12 (13.3) |
| Lab values not taken month 3. | 3 (3.3) | 12 (13.3) | 0 | 7 (7.8) |
| GTQ not completed month 6. | 2 (2.2) | 7 (7.8) | 1 (1.1) | 7 (7.8) |
| Month 3 Visit not done. | 4 (4.4) | 9 (10.0) | 1 (1.1) | 3 (3.3) |
| Month 6 Visit not done. | 8 (8.9) | 9 (10.0) | 3 (3.3) | 9 (10.0) |
| 1 Subjects can have more than one Protocol Deviation throughout the study. | ||||
Finally, we can style the table, indenting the individual PDs under
Study Procedure Deviations, left-aligning the first column
and centering all other columns. Note that for the indentation, we can
still use the hidden column PARCAT1 to identify individual
PDs.
addv_tbl |>
cols_align(
align = "center",
columns = 3:6
) |>
cols_align(
align = "left",
columns = 1:2
) |>
tab_stub_indent(
rows = PARCAT1 == "PROTOCOL DEVIATION",
indent = 5
)| xx.x: Demographic and Baseline Data | ||||
| xx.x.x: Major Protocol Deviations and Relationship to COVID-19 - ITT Set | ||||
|
Placebo N=90 (100%) n (%) |
Drug 1 N=90 (100%) n (%) |
|||
|---|---|---|---|---|
| COVID-19 Related | COVID-19 Related | |||
| Yes | No | Yes | No | |
| Subjects with at least: | ||||
| At least one major Protocol Deviation1 | 15 (16.7) | 24 (26.7) | 5 (5.6) | 23 (25.6) |
| Study Procedure Deviations | 17 | 44 | 5 | 38 |
| GTQ not completed month 3. | 0 | 7 (7.8) | 0 | 12 (13.3) |
| Lab values not taken month 3. | 3 (3.3) | 12 (13.3) | 0 | 7 (7.8) |
| GTQ not completed month 6. | 2 (2.2) | 7 (7.8) | 1 (1.1) | 7 (7.8) |
| Month 3 Visit not done. | 4 (4.4) | 9 (10.0) | 1 (1.1) | 3 (3.3) |
| Month 6 Visit not done. | 8 (8.9) | 9 (10.0) | 3 (3.3) | 9 (10.0) |
| 1 Subjects can have more than one Protocol Deviation throughout the study. | ||||