Most of the work in creating a
Tplyr table is at the layer level, but there are a few
overarching properties that are worth spending some time discussing. One
of the things that we wanted to make sure we did in
Tplyr is allow you to eliminate redundant code wherever
possible. Adding some processing to the tplyr_table()
level
allows us to do that. Furthermore, some settings simply need to be
applied table wide.
The tplyr_table()
function has 4 parameters:
treat_var
: The variable containing
treatment group assignmentswhere
: The overarching table subset
criteria. Each layer will use this subset by default. The
where
parameter at the table level will be called
in addition to the layer subset criteria.cols
: Grouping variables used in
addition to the by
variables set at the layer level, but
will be transposed into columns in addition to
treat_var
.Let’s look at an example:
tplyr_table(tplyr_adsl, TRT01P, where= SAFFL =="Y", cols = SEX) %>%
add_layer(
group_count(RACE, by = "Race")
) %>%
add_layer(
group_desc(AGE, by = "Age (Years)")
) %>%
build() %>%
kable()
row_label1 | row_label2 | var1_Placebo_F | var1_Placebo_M | var1_Xanomeline High Dose_F | var1_Xanomeline High Dose_M | var1_Xanomeline Low Dose_F | var1_Xanomeline Low Dose_M | ord_layer_index | ord_layer_1 | ord_layer_2 |
---|---|---|---|---|---|---|---|---|---|---|
Race | AMERICAN INDIAN OR ALASKA NATIVE | 0 ( 0.0%) | 0 ( 0.0%) | 0 ( 0.0%) | 1 ( 2.3%) | 0 ( 0.0%) | 0 ( 0.0%) | 1 | 1 | 1 |
Race | BLACK OR AFRICAN AMERICAN | 5 ( 9.4%) | 3 ( 9.1%) | 6 ( 15.0%) | 3 ( 6.8%) | 6 ( 12.0%) | 0 ( 0.0%) | 1 | 1 | 2 |
Race | WHITE | 48 ( 90.6%) | 30 ( 90.9%) | 34 ( 85.0%) | 40 ( 90.9%) | 44 ( 88.0%) | 34 (100.0%) | 1 | 1 | 3 |
Age (Years) | n | 53 | 33 | 40 | 44 | 50 | 34 | 2 | 1 | 1 |
Age (Years) | Mean (SD) | 78.1 ( 8.73) | 73.9 ( 8.15) | 76.0 ( 7.67) | 75.9 ( 8.16) | 77.4 ( 8.09) | 77.5 ( 8.69) | 2 | 1 | 2 |
Age (Years) | Median | 78.0 | 74.0 | 76.0 | 77.0 | 77.5 | 77.5 | 2 | 1 | 3 |
Age (Years) | Q1, Q3 | 69.0, 84.0 | 69.0, 80.0 | 72.0, 79.0 | 69.0, 80.0 | 72.0, 81.0 | 68.0, 82.0 | 2 | 1 | 4 |
Age (Years) | Min, Max | 59, 89 | 52, 85 | 56, 88 | 56, 86 | 54, 87 | 51, 88 | 2 | 1 | 5 |
Age (Years) | Missing | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 6 |
In the example above, the where
parameter is passed
forward into both the RACE
and AGE
layers.
Furthermore, note how the cols
parameter works. By default,
the target variables from the layers are transposed by the
treat_var
variables. The cols
argument adds an
additional variable to transpose by, and the values of these variable
are added as a suffix to the variable name. You are able to use multiple
cols
variables just like by
, by using
dplyr::vars()
. But use with caution - as depending on the
distinct variable values in the dataset, this could get quite wide.
Note: Treatment groups and additional column variables presented in the final output are always taken from the pre-filtered population data. This means that if a filter completed excludes a treatment group or group within a column variable, columns will still be created for those groups and will be empty/zero filled.
tplyr_table(tplyr_adsl, TRT01P, where= SAFFL =="Y", cols = vars(SEX, RACE)) %>%
add_layer(
group_desc(AGE, by = "Age (Years)")
) %>%
build() %>%
kable()
row_label1 | row_label2 | var1_Placebo_F_AMERICAN INDIAN OR ALASKA NATIVE | var1_Placebo_F_BLACK OR AFRICAN AMERICAN | var1_Placebo_F_WHITE | var1_Placebo_M_AMERICAN INDIAN OR ALASKA NATIVE | var1_Placebo_M_BLACK OR AFRICAN AMERICAN | var1_Placebo_M_WHITE | var1_Xanomeline High Dose_F_AMERICAN INDIAN OR ALASKA NATIVE | var1_Xanomeline High Dose_F_BLACK OR AFRICAN AMERICAN | var1_Xanomeline High Dose_F_WHITE | var1_Xanomeline High Dose_M_AMERICAN INDIAN OR ALASKA NATIVE | var1_Xanomeline High Dose_M_BLACK OR AFRICAN AMERICAN | var1_Xanomeline High Dose_M_WHITE | var1_Xanomeline Low Dose_F_AMERICAN INDIAN OR ALASKA NATIVE | var1_Xanomeline Low Dose_F_BLACK OR AFRICAN AMERICAN | var1_Xanomeline Low Dose_F_WHITE | var1_Xanomeline Low Dose_M_AMERICAN INDIAN OR ALASKA NATIVE | var1_Xanomeline Low Dose_M_BLACK OR AFRICAN AMERICAN | var1_Xanomeline Low Dose_M_WHITE | ord_layer_index | ord_layer_1 | ord_layer_2 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Age (Years) | n | 5 | 48 | 3 | 30 | 6 | 34 | 1 | 3 | 40 | 6 | 44 | 34 | 1 | 1 | 1 | ||||||
Age (Years) | Mean (SD) | 80.0 ( 7.79) | 77.9 ( 8.89) | 67.0 ( 6.81) | 74.8 ( 7.84) | 73.5 ( 6.08) | 76.4 ( 7.91) | 61.0 ( ) | 79.0 ( 2.52) | 75.5 ( 8.16) | 75.0 (11.78) | 77.8 ( 7.54) | 77.5 ( 8.69) | 1 | 1 | 2 | ||||||
Age (Years) | Median | 80.0 | 78.0 | 67.0 | 74.5 | 73.5 | 76.0 | 61.0 | 79.0 | 76.0 | 75.0 | 78.0 | 77.5 | 1 | 1 | 3 | ||||||
Age (Years) | Q1, Q3 | 64.0, 81.0 | 69.0, 84.0 | 57.0, 67.0 | 70.0, 80.0 | 67.0, 74.0 | 72.0, 80.0 | 61.0, 61.0 | 77.0, 79.0 | 69.0, 80.0 | 60.0, 76.0 | 72.0, 81.0 | 68.0, 82.0 | 1 | 1 | 4 | ||||||
Age (Years) | Min, Max | 64, 81 | 59, 89 | 57, 70 | 52, 85 | 63, 79 | 56, 88 | 61, 61 | 77, 82 | 56, 86 | 57, 87 | 54, 86 | 51, 88 | 1 | 1 | 5 | ||||||
Age (Years) | Missing | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 6 |
Another important feature that works at the table level is the addition of treatment groups. By adding additional treatment groups, you’re able to do a number of things:
We’ve added the function add_treat_grps()
to do this
work for you. With this function, you can create new treatment groups by
combining existing treatment groups from values within
treat_var
. Additionally, to simplify the process we added
an abstraction of add_treat_grps()
named
add_total_group()
to simplify the process of creating a
“Total” group.
tplyr_table(tplyr_adsl, TRT01P) %>%
add_treat_grps('Treated' = c("Xanomeline High Dose", "Xanomeline Low Dose")) %>%
add_total_group() %>%
add_layer(
group_desc(AGE, by = "Age (Years)")
) %>%
build() %>%
kable()
row_label1 | row_label2 | var1_Placebo | var1_Xanomeline High Dose | var1_Xanomeline Low Dose | var1_Treated | var1_Total | ord_layer_index | ord_layer_1 | ord_layer_2 |
---|---|---|---|---|---|---|---|---|---|
Age (Years) | n | 86 | 84 | 84 | 168 | 254 | 1 | 1 | 1 |
Age (Years) | Mean (SD) | 76.3 ( 8.59) | 75.9 ( 7.89) | 77.4 ( 8.29) | 76.6 ( 8.09) | 76.5 ( 8.25) | 1 | 1 | 2 |
Age (Years) | Median | 76.0 | 76.0 | 77.5 | 77.0 | 77.0 | 1 | 1 | 3 |
Age (Years) | Q1, Q3 | 69.0, 81.0 | 70.0, 80.0 | 71.0, 82.0 | 71.0, 81.0 | 70.0, 81.0 | 1 | 1 | 4 |
Age (Years) | Min, Max | 52, 89 | 56, 88 | 51, 88 | 51, 88 | 51, 89 | 1 | 1 | 5 |
Age (Years) | Missing | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 6 |
Note how in the above example, there are two new columns added to the
data - var1_Total
and var1_Treated
. The
summaries for the individual cohorts are left unchanged.
A last and very important aspect of table level properties in
Tplyr is the addition of a population dataset. In CDISC
standards, datasets like adae
only contain adverse events
when they occur. This means that if a subject did not experience an
adverse event, or did not experience an adverse event within the
criteria that you’re subsetting for, they don’t appear in the dataset.
When you’re looking at the proportion of subject who experienced an
adverse event compared to the total number of subjects in that cohort,
adae
itself leaves you no way to calculate that total - as
the subjects won’t exist in the data.
Tplyr allows you to provide a separate population
dataset to overcome this. Furthermore, you are also able to provide a
separate population dataset where
parameter and a
population treatment variable named pop_treat_var
, as
variable names may differ between the datasets.
t <- tplyr_table(tplyr_adae, TRTA, where = AEREL != "NONE") %>%
set_pop_data(tplyr_adsl) %>%
set_pop_treat_var(TRT01A) %>%
set_pop_where(TRUE) %>%
add_layer(
group_count(AEDECOD) %>%
set_distinct_by(USUBJID)
)
t %>%
build() %>%
kable()
row_label1 | var1_Placebo | var1_Xanomeline High Dose | var1_Xanomeline Low Dose | ord_layer_index | ord_layer_1 |
---|---|---|---|---|---|
ALOPECIA | 1 ( 1.2%) | 0 ( 0.0%) | 0 ( 0.0%) | 1 | 2 |
BLISTER | 0 ( 0.0%) | 1 ( 1.2%) | 5 ( 6.0%) | 1 | 3 |
COLD SWEAT | 1 ( 1.2%) | 0 ( 0.0%) | 0 ( 0.0%) | 1 | 4 |
DERMATITIS CONTACT | 0 ( 0.0%) | 0 ( 0.0%) | 1 ( 1.2%) | 1 | 6 |
ERYTHEMA | 9 ( 10.5%) | 14 ( 16.7%) | 13 ( 15.5%) | 1 | 8 |
HYPERHIDROSIS | 2 ( 2.3%) | 8 ( 9.5%) | 4 ( 4.8%) | 1 | 9 |
PRURITUS | 8 ( 9.3%) | 26 ( 31.0%) | 21 ( 25.0%) | 1 | 10 |
PRURITUS GENERALISED | 0 ( 0.0%) | 1 ( 1.2%) | 1 ( 1.2%) | 1 | 11 |
RASH | 4 ( 4.7%) | 8 ( 9.5%) | 13 ( 15.5%) | 1 | 12 |
RASH ERYTHEMATOUS | 0 ( 0.0%) | 0 ( 0.0%) | 2 ( 2.4%) | 1 | 13 |
RASH MACULO-PAPULAR | 0 ( 0.0%) | 1 ( 1.2%) | 0 ( 0.0%) | 1 | 14 |
RASH PAPULAR | 0 ( 0.0%) | 1 ( 1.2%) | 0 ( 0.0%) | 1 | 15 |
RASH PRURITIC | 0 ( 0.0%) | 2 ( 2.4%) | 1 ( 1.2%) | 1 | 16 |
SKIN EXFOLIATION | 0 ( 0.0%) | 0 ( 0.0%) | 1 ( 1.2%) | 1 | 17 |
SKIN IRRITATION | 2 ( 2.3%) | 5 ( 6.0%) | 6 ( 7.1%) | 1 | 18 |
SKIN ODOUR ABNORMAL | 0 ( 0.0%) | 1 ( 1.2%) | 0 ( 0.0%) | 1 | 19 |
SKIN ULCER | 1 ( 1.2%) | 0 ( 0.0%) | 0 ( 0.0%) | 1 | 20 |
URTICARIA | 0 ( 0.0%) | 1 ( 1.2%) | 1 ( 1.2%) | 1 | 21 |
In the above example, AEREL
doesn’t exist in
adsl
, therefore we used set_pop_where()
to
remove the filter criteria on the population data. Setting the
population dataset where parameter to TRUE
removes any
filter applied by the population data. If set_pop_where()
is not set for the population data, it will default to the
where
parameter used in tplyr_table()
. The
same logic applies to the population treatment variable.
TRTA
does not exist in adsl
either, so we used
set_pop_treat_var()
to change it to the appropriate
variable in adsl
.
Note the percentage values in the summary above. By setting the population data, Tplyr now knew to use those values when calculating the percentages for the distinct counts of subjects who experienced the summarized adverse events. Furthermore, with the population data provided, Tplyr is able to calculate your header N’s properly:
TRT01A | n |
---|---|
Placebo | 86 |
Xanomeline High Dose | 84 |
Xanomeline Low Dose | 84 |
Note: it’s expected the set_distinct_by()
function is
used with population data. This is because it does not make sense to use
population data denominators unless you have distinct counts. The entire
point of population data is to use subject counts, so non-distinct
counts would potentially count multiple records per subject and then the
percentage doesn’t make any sense.
When creating summary tables, often we have to mock up the potential values of data, even if those values aren’t present in the data we’re summarizing. Tplyr does its best effort to do this for you. Let’s consider the following dataset:
USUBJID | AVISIT | PECAT | PARAM | TRT01A | AVALC | AVAL | BASEC |
---|---|---|---|---|---|---|---|
101-001 | Screening | A | Head | TRT A | Normal | 1 | Abnormal |
101-001 | Screening | A | Lungs | TRT A | Normal | 2 | Semi-Normal |
101-001 | Day -1 | A | Lungs | TRT A | Normal | 3 | Normal |
101-001 | Day 5 | A | Lungs | TRT A | Normal | 4 | Normal |
101-002 | Screening | A | Head | TRT B | Semi-Normal | 5 | Normal |
101-002 | Screening | A | Lungs | TRT B | Normal | 6 | Normal |
Let’s say we want to create a count summary for this dataset, and
report it by PARAM and AVISIT. Note that in the data,
PARAM=="HEAD"
is only collected at screening, while
LUNGS
is collected at Screening, Day -1, and Day 5.
tplyr_table(tplyr_adpe, TRT01A) %>%
add_layer(
group_count(AVALC, by = vars(PARAM, AVISIT))
) %>%
build() %>%
select(-starts_with('ord')) %>%
head(18) %>%
kable()
row_label1 | row_label2 | row_label3 | var1_TRT A | var1_TRT B |
---|---|---|---|---|
Head | Screening | Normal | 2 ( 14.3%) | 0 ( 0.0%) |
Head | Screening | Semi-Normal | 0 ( 0.0%) | 1 ( 14.3%) |
Head | Screening | Abnormal | 0 ( 0.0%) | 0 ( 0.0%) |
Head | Day -1 | Normal | 0 ( 0.0%) | 0 ( 0.0%) |
Head | Day -1 | Semi-Normal | 0 ( 0.0%) | 0 ( 0.0%) |
Head | Day -1 | Abnormal | 0 ( 0.0%) | 0 ( 0.0%) |
Head | Day 5 | Normal | 0 ( 0.0%) | 0 ( 0.0%) |
Head | Day 5 | Semi-Normal | 0 ( 0.0%) | 0 ( 0.0%) |
Head | Day 5 | Abnormal | 0 ( 0.0%) | 0 ( 0.0%) |
Lungs | Screening | Normal | 2 ( 14.3%) | 2 ( 28.6%) |
Lungs | Screening | Semi-Normal | 0 ( 0.0%) | 0 ( 0.0%) |
Lungs | Screening | Abnormal | 2 ( 14.3%) | 0 ( 0.0%) |
Lungs | Day -1 | Normal | 4 ( 28.6%) | 2 ( 28.6%) |
Lungs | Day -1 | Semi-Normal | 0 ( 0.0%) | 0 ( 0.0%) |
Lungs | Day -1 | Abnormal | 0 ( 0.0%) | 0 ( 0.0%) |
Lungs | Day 5 | Normal | 2 ( 14.3%) | 2 ( 28.6%) |
Lungs | Day 5 | Semi-Normal | 0 ( 0.0%) | 0 ( 0.0%) |
Lungs | Day 5 | Abnormal | 2 ( 14.3%) | 0 ( 0.0%) |
By default, given the by
variables of PARAM and AVISIT,
all of the potential visits have dummy rows created that are 0 filled -
meaning results of 0 records for all treatment groups are presented.
However, that might not be what you wish to present. Perhaps
HEAD
was only intended to be collected at the Screening
visit so it’s unnecessary to present other visits. To address this, you
can use the set_limit_data_by()
function.
tplyr_table(tplyr_adpe, TRT01A) %>%
add_layer(
group_count(AVALC, by = vars(PARAM, AVISIT)) %>%
set_limit_data_by(PARAM, AVISIT)
) %>%
build() %>%
select(-starts_with('ord')) %>%
head(12) %>%
kable()
row_label1 | row_label2 | row_label3 | var1_TRT A | var1_TRT B |
---|---|---|---|---|
Head | Screening | Normal | 2 ( 14.3%) | 0 ( 0.0%) |
Head | Screening | Semi-Normal | 0 ( 0.0%) | 1 ( 14.3%) |
Head | Screening | Abnormal | 0 ( 0.0%) | 0 ( 0.0%) |
Lungs | Screening | Normal | 2 ( 14.3%) | 2 ( 28.6%) |
Lungs | Screening | Semi-Normal | 0 ( 0.0%) | 0 ( 0.0%) |
Lungs | Screening | Abnormal | 2 ( 14.3%) | 0 ( 0.0%) |
Lungs | Day -1 | Normal | 4 ( 28.6%) | 2 ( 28.6%) |
Lungs | Day -1 | Semi-Normal | 0 ( 0.0%) | 0 ( 0.0%) |
Lungs | Day -1 | Abnormal | 0 ( 0.0%) | 0 ( 0.0%) |
Lungs | Day 5 | Normal | 2 ( 14.3%) | 2 ( 28.6%) |
Lungs | Day 5 | Semi-Normal | 0 ( 0.0%) | 0 ( 0.0%) |
Lungs | Day 5 | Abnormal | 2 ( 14.3%) | 0 ( 0.0%) |
Here you can see that now records for HEAD
only present
the screening visit. For count and shift layers, you can additionally
dig further in to use target variables:
tplyr_table(tplyr_adpe, TRT01A) %>%
add_layer(
group_count(AVALC, by = vars(PARAM, AVISIT)) %>%
set_limit_data_by(PARAM, AVISIT, AVALC)
) %>%
build() %>%
select(-starts_with('ord')) %>%
kable()
row_label1 | row_label2 | row_label3 | var1_TRT A | var1_TRT B |
---|---|---|---|---|
Head | Screening | Normal | 2 ( 14.3%) | 0 ( 0.0%) |
Lungs | Screening | Normal | 2 ( 14.3%) | 2 ( 28.6%) |
Lungs | Screening | Abnormal | 2 ( 14.3%) | 0 ( 0.0%) |
Lungs | Day -1 | Normal | 4 ( 28.6%) | 2 ( 28.6%) |
Lungs | Day 5 | Normal | 2 ( 14.3%) | 2 ( 28.6%) |
Lungs | Day 5 | Abnormal | 2 ( 14.3%) | 0 ( 0.0%) |
Head | Screening | Semi-Normal | 0 ( 0.0%) | 1 ( 14.3%) |
This effectively limits to the values present in the data itself.
With the table level settings under control, now you’re ready to learn more about what Tplyr has to offer in each layer.
vignette("desc")
vignette("count")
vignette("shift")
vignette("riskdiff")
vignette("sort")
vignette("options")
vignette("styled-table")