tern TabulationThe tern R package provides functions to create common
analyses from clinical trials in R. The core functionality
for tabulation is built on the more general purpose rtables
package. New users should first begin by reading the “Introduction
to tern” and “Introduction
to rtables” vignettes.
The packages used in this vignette are:
tables.R
The datasets used in this vignette are:
tables.R
tern Analyze FunctionsAnalyze functions are used in combination with the
rtables layout functions, in the pipeline which creates the
rtables table. They apply some statistical logic to the
layout of the rtables table. The table layout is
materialized with the rtables::build_table function and the
data.
The tern analyze functions are wrappers around
rtables::analyze function, they offer various methods
useful from the perspective of clinical trials and other statistical
projects.
Examples of the tern analyze functions are
count_occurrences, summarize_ancova or
analyze_vars. As there is no one prefix to identify all
tern analyze functions it is recommended to use the the
tern website functions reference.
tern Analyze FunctionsPlease skip this subsection if you are not interested in the
internals of tern analyze functions.
Internally tern analyze functions like
summarize_ancova are mainly built in the 4 elements
chain:
h_ancova() -> tern:::s_ancova() -> tern:::a_ancova() -> summarize_ancova()The descriptions for each function type:
h_*. These functions are
useful to help define the analysis.s_*. Statistics functions should do
the computation of the numbers that are tabulated later. In order to
separate computation from formatting, they should not take care of
rcell type formatting themselves.a_*. These have the same
arguments as the corresponding statistics functions, and can be further
customized by calling rtables::make_afun() on them. They
are used as afun in rtables::analyze().rtables::analyze(..., afun = make_afun(tern::a_*)). Analyze
functions are used in combination with the rtables layout
functions, in the pipeline which creates the table. They are the last
element of the chain.We will use the native rtables::analyze function with
the tern formatted analysis functions as a
afun parameter.
l <- basic_table() %>%
split_cols_by(var = "ARM") %>%
split_rows_by(var = "AVISIT") %>%
analyze(vars = "AVAL", afun = a_summary)
build_table(l, df = adrs)The rtables::make_afun function is helpful when somebody
wants to attach some format to the formatted analysis function.
afun <- make_afun(
a_summary,
.stats = NULL,
.formats = c(median = "xx."),
.labels = c(median = "My median"),
.indent_mods = c(median = 1L)
)
l2 <- basic_table() %>%
split_cols_by(var = "ARM") %>%
split_rows_by(var = "AVISIT") %>%
analyze(vars = "AVAL", afun = afun)
build_table(l2, df = adrs)We are going to create 3 different tables using tern
analyze functions and the rtables interface.
| Table | tern analyze functions |
|---|---|
| Demographic Table | analyze_vars() and
summarize_num_patients() |
| Adverse event Table | count_occurrences() |
| Response Table | estimate_proportion(),
estimate_proportion_diff() and
test_proportion_diff() |
Demographic tables provide a summary of the characteristics of patients enrolled in a clinical trial. Typically the table columns represent treatment arms and variables summarized in the table are demographic properties such as age, sex, race, etc.
In the example below the only function from tern is
analyze_vars() and the remaining layout functions are from
rtables.
# Select variables to include in table.
vars <- c("AGE", "SEX")
var_labels <- c("Age (yr)", "Sex")
basic_table() %>%
split_cols_by(var = "ARM") %>%
add_overall_col("All Patients") %>%
add_colcounts() %>%
analyze_vars(
vars = vars,
var_labels = var_labels
) %>%
build_table(adsl)
#> A: Drug X B: Placebo C: Combination All Patients
#> (N=134) (N=134) (N=132) (N=400)
#> ——————————————————————————————————————————————————————————————————————————————
#> Age (yr)
#> n 134 134 132 400
#> Mean (SD) 33.8 (6.6) 35.4 (7.9) 35.4 (7.7) 34.9 (7.4)
#> Median 33.0 35.0 35.0 34.0
#> Min - Max 21.0 - 50.0 21.0 - 62.0 20.0 - 69.0 20.0 - 69.0
#> Sex
#> n 134 134 132 400
#> F 79 (59%) 77 (57.5%) 66 (50%) 222 (55.5%)
#> M 51 (38.1%) 55 (41%) 60 (45.5%) 166 (41.5%)
#> U 3 (2.2%) 2 (1.5%) 4 (3%) 9 (2.2%)
#> UNDIFFERENTIATED 1 (0.7%) 0 2 (1.5%) 3 (0.8%)tables.R
To change the display order of categorical variables in a table use
factor variables and explicitly set the order of the levels. This is the
case for the display order in columns and rows. Note that the
forcats package has many useful functions to help with
these types of data processing steps (not used below).
# Reorder the levels in the ARM variable.
adsl$ARM <- factor(adsl$ARM, levels = c("B: Placebo", "A: Drug X", "C: Combination"))
# Reorder the levels in the SEX variable.
adsl$SEX <- factor(adsl$SEX, levels = c("M", "F", "U", "UNDIFFERENTIATED"))
basic_table() %>%
split_cols_by(var = "ARM") %>%
add_overall_col("All Patients") %>%
add_colcounts() %>%
analyze_vars(
vars = vars,
var_labels = var_labels
) %>%
build_table(adsl)
#> B: Placebo A: Drug X C: Combination All Patients
#> (N=134) (N=134) (N=132) (N=400)
#> ——————————————————————————————————————————————————————————————————————————————
#> Age (yr)
#> n 134 134 132 400
#> Mean (SD) 35.4 (7.9) 33.8 (6.6) 35.4 (7.7) 34.9 (7.4)
#> Median 35.0 33.0 35.0 34.0
#> Min - Max 21.0 - 62.0 21.0 - 50.0 20.0 - 69.0 20.0 - 69.0
#> Sex
#> n 134 134 132 400
#> M 55 (41%) 51 (38.1%) 60 (45.5%) 166 (41.5%)
#> F 77 (57.5%) 79 (59%) 66 (50%) 222 (55.5%)
#> U 2 (1.5%) 3 (2.2%) 4 (3%) 9 (2.2%)
#> UNDIFFERENTIATED 0 1 (0.7%) 2 (1.5%) 3 (0.8%)tables.R
The tern package includes many functions similar to
analyze_vars(). These functions are called layout creating
functions and are used in combination with other rtables
layout functions just like in the examples above. Layout creating
functions are wrapping calls to rtables
analyze(), analyze_colvars() and
summarize_row_groups() and provide options for easy
formatting and analysis modifications.
To customize the display for the demographics table, we can do so via
the arguments in analyze_vars(). Most layout creating
functions in tern include the standard arguments
.stats, .formats, .labels and
.indent_mods which control which statistics are displayed
and how the numbers are formatted. Refer to the package help with
help("analyze_vars") or ?analyze_vars to see
the full set of options.
For this example we will change the default summary for numeric variables to include the number of records, and the mean and standard deviation (in a single statistic, i.e. within a single cell). For categorical variables we modify the summary to include the number of records and the counts of categories. We also modify the display format for the mean and standard deviation to print two decimal places instead of just one.
# Select statistics and modify default formats.
basic_table() %>%
split_cols_by(var = "ARM") %>%
add_overall_col("All Patients") %>%
add_colcounts() %>%
analyze_vars(
vars = vars,
var_labels = var_labels,
.stats = c("n", "mean_sd", "count"),
.formats = c(mean_sd = "xx.xx (xx.xx)")
) %>%
build_table(adsl)
#> B: Placebo A: Drug X C: Combination All Patients
#> (N=134) (N=134) (N=132) (N=400)
#> ————————————————————————————————————————————————————————————————————————————————
#> Age (yr)
#> n 134 134 132 400
#> Mean (SD) 35.43 (7.90) 33.77 (6.55) 35.43 (7.72) 34.88 (7.44)
#> Sex
#> n 134 134 132 400
#> M 55 51 60 166
#> F 77 79 66 222
#> U 2 3 4 9
#> UNDIFFERENTIATED 0 1 2 3tables.R
One feature of a layout is that it can be used with
different datasets to create different summaries. For example, here we
can easily create the same summary of demographics for the Brazil and
China subgroups, respectively:
lyt <- basic_table() %>%
split_cols_by(var = "ARM") %>%
add_overall_col("All Patients") %>%
add_colcounts() %>%
analyze_vars(
vars = vars,
var_labels = var_labels
)
build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "BRA"))
#> B: Placebo A: Drug X C: Combination All Patients
#> (N=7) (N=13) (N=10) (N=30)
#> ——————————————————————————————————————————————————————————————————————————————
#> Age (yr)
#> n 7 13 10 30
#> Mean (SD) 32.0 (6.1) 36.7 (6.4) 38.3 (10.6) 36.1 (8.1)
#> Median 32.0 37.0 35.0 35.5
#> Min - Max 25.0 - 42.0 24.0 - 47.0 25.0 - 64.0 24.0 - 64.0
#> Sex
#> n 7 13 10 30
#> M 4 (57.1%) 8 (61.5%) 5 (50%) 17 (56.7%)
#> F 3 (42.9%) 5 (38.5%) 5 (50%) 13 (43.3%)
#> U 0 0 0 0
#> UNDIFFERENTIATED 0 0 0 0
build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "CHN"))
#> B: Placebo A: Drug X C: Combination All Patients
#> (N=81) (N=74) (N=64) (N=219)
#> ——————————————————————————————————————————————————————————————————————————————
#> Age (yr)
#> n 81 74 64 219
#> Mean (SD) 35.7 (7.3) 33.0 (6.4) 35.2 (6.4) 34.6 (6.8)
#> Median 36.0 32.0 35.0 34.0
#> Min - Max 21.0 - 58.0 23.0 - 48.0 21.0 - 49.0 21.0 - 58.0
#> Sex
#> n 81 74 64 219
#> M 35 (43.2%) 27 (36.5%) 30 (46.9%) 92 (42%)
#> F 45 (55.6%) 44 (59.5%) 29 (45.3%) 118 (53.9%)
#> U 1 (1.2%) 2 (2.7%) 3 (4.7%) 6 (2.7%)
#> UNDIFFERENTIATED 0 1 (1.4%) 2 (3.1%) 3 (1.4%)tables.R
The standard table of adverse events is a summary by system organ class and preferred term. For frequency counts by preferred term, if there are multiple occurrences of the same AE in an individual we count them only once.
To create this table we will need to use a combination of several layout creating functions in a tabulation pipeline.
We start by creating the high-level summary. The layout creating
function in tern that can do this is
summarize_num_patients():
basic_table() %>%
split_cols_by(var = "ACTARM") %>%
add_colcounts() %>%
add_overall_col(label = "All Patients") %>%
summarize_num_patients(
var = "USUBJID",
.stats = c("unique", "nonunique"),
.labels = c(
unique = "Total number of patients with at least one AE",
nonunique = "Overall total number of events"
)
) %>%
build_table(
df = adae,
alt_counts_df = adsl
)
#> A: Drug X B: Placebo C: Combination All Patients
#> (N=134) (N=134) (N=132) (N=400)
#> —————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one AE 122 (91.0%) 123 (91.8%) 120 (90.9%) 365 (91.2%)
#> Overall total number of events 609 622 703 1934tables.R
Note that for this table, the denominator used for percentages and
shown in the header of the table (N = xx) is defined based
on the subject-level dataset adsl. This is done by using
the alt_df_counts argument in build_table(),
which provides an alternative data set for deriving the counts in the
header. This is often required when we work with data sets that include
multiple records per patient as df, such as
adae here.
Before building out the rest of the AE table it is helpful to
introduce some more tern package design conventions. Each
layout creating function in tern is a wrapper for a
Statistics function. Statistics functions are the ones that do the
actual computation of numbers in a table. These functions always return
named lists whose elements are the statistics available to include in a
layout via the .stats argument at the layout creating
function level.
Statistics functions follow a naming convention to always begin with
s_* and for ease of use are documented on the same page as
their layout creating function counterpart. It is helpful to review a
Statistic function to understand the logic used to calculate the numbers
in a table and see what options may be available to modify the
analysis.
For example, the Statistics function calculating the numbers in
summarize_num_patients() is s_num_patients().
The results of this Statistics function is a list with the elements
unique, nonunique and
unique_count:
s_num_patients(x = adae$USUBJID, labelstr = "", .N_col = nrow(adae))
#> $unique
#> [1] 365.000000 0.188728
#> attr(,"label")
#> [1] ""
#>
#> $nonunique
#> [1] 1934
#> attr(,"label")
#> [1] ""
#>
#> $unique_count
#> [1] 365
#> attr(,"label")
#> [1] "(n)"tables.R
From these results you can see that the unique and
nonunique statistics are those displayed in the “All
Patients” column in the initial AE table output above. Also you can see
that these are raw numbers and are not formatted in any way. All
formatting functionality is handled at the layout creating function
level with the .formats argument.
Now that we know what types of statistics can be derived by
s_num_patients(), we can try modifying the default layout
returned by summarize_num_patients(). Instead of reporting
the unique and nonqunie statistics, we specify
that the analysis should include only the unique_count
statistic. The result will show only the counts of unique patients. Note
we make this update in both the .stats and
.labels argument of
summarize_num_patients().
basic_table() %>%
split_cols_by(var = "ACTARM") %>%
add_colcounts() %>%
add_overall_col(label = "All Patients") %>%
summarize_num_patients(
var = "USUBJID",
.stats = "unique_count",
.labels = c(unique_count = "Total number of patients with at least one AE")
) %>%
build_table(
df = adae,
alt_counts_df = adsl
)
#> A: Drug X B: Placebo C: Combination All Patients
#> (N=134) (N=134) (N=132) (N=400)
#> ——————————————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one AE 122 123 120 365tables.R
Let’s now continue building on the layout for the adverse event table.
After we have the top-level summary, we can repeat the same summary
at each system organ class level. To do this we split the analysis data
with split_rows_by() before calling again
summarize_num_patients().
basic_table() %>%
split_cols_by(var = "ACTARM") %>%
add_colcounts() %>%
add_overall_col(label = "All Patients") %>%
summarize_num_patients(
var = "USUBJID",
.stats = c("unique", "nonunique"),
.labels = c(
unique = "Total number of patients with at least one AE",
nonunique = "Overall total number of events"
)
) %>%
split_rows_by(
"AEBODSYS",
child_labels = "visible",
nested = FALSE,
indent_mod = -1L,
split_fun = drop_split_levels
) %>%
summarize_num_patients(
var = "USUBJID",
.stats = c("unique", "nonunique"),
.labels = c(
unique = "Total number of patients with at least one AE",
nonunique = "Overall total number of events"
)
) %>%
build_table(
df = adae,
alt_counts_df = adsl
)
#> A: Drug X B: Placebo C: Combination All Patients
#> (N=134) (N=134) (N=132) (N=400)
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one AE 122 (91.0%) 123 (91.8%) 120 (90.9%) 365 (91.2%)
#> Overall total number of events 609 622 703 1934
#> cl A.1
#> Total number of patients with at least one AE 78 (58.2%) 75 (56.0%) 89 (67.4%) 242 (60.5%)
#> Overall total number of events 132 130 160 422
#> cl B.1
#> Total number of patients with at least one AE 47 (35.1%) 49 (36.6%) 43 (32.6%) 139 (34.8%)
#> Overall total number of events 56 60 62 178
#> cl B.2
#> Total number of patients with at least one AE 79 (59.0%) 74 (55.2%) 85 (64.4%) 238 (59.5%)
#> Overall total number of events 129 138 143 410
#> cl C.1
#> Total number of patients with at least one AE 43 (32.1%) 46 (34.3%) 43 (32.6%) 132 (33.0%)
#> Overall total number of events 55 63 64 182
#> cl C.2
#> Total number of patients with at least one AE 35 (26.1%) 48 (35.8%) 55 (41.7%) 138 (34.5%)
#> Overall total number of events 48 53 65 166
#> cl D.1
#> Total number of patients with at least one AE 79 (59.0%) 67 (50.0%) 80 (60.6%) 226 (56.5%)
#> Overall total number of events 127 106 135 368
#> cl D.2
#> Total number of patients with at least one AE 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%)
#> Overall total number of events 62 72 74 208tables.R
The table looks almost ready. For the final step, we need a layout
creating function that can produce a count table of event frequencies.
The layout creating function for this is
count_occurrences(). Let’s first try using this function in
a simpler layout without row splits:
basic_table() %>%
split_cols_by(var = "ACTARM") %>%
add_colcounts() %>%
add_overall_col(label = "All Patients") %>%
count_occurrences(vars = "AEDECOD") %>%
build_table(
df = adae,
alt_counts_df = adsl
)
#> A: Drug X B: Placebo C: Combination All Patients
#> (N=134) (N=134) (N=132) (N=400)
#> ———————————————————————————————————————————————————————————————————————
#> dcd A.1.1.1.1 50 (37.3%) 45 (33.6%) 63 (47.7%) 158 (39.5%)
#> dcd A.1.1.1.2 48 (35.8%) 48 (35.8%) 50 (37.9%) 146 (36.5%)
#> dcd B.1.1.1.1 47 (35.1%) 49 (36.6%) 43 (32.6%) 139 (34.8%)
#> dcd B.2.1.2.1 49 (36.6%) 44 (32.8%) 52 (39.4%) 145 (36.2%)
#> dcd B.2.2.3.1 48 (35.8%) 54 (40.3%) 51 (38.6%) 153 (38.2%)
#> dcd C.1.1.1.3 43 (32.1%) 46 (34.3%) 43 (32.6%) 132 (33.0%)
#> dcd C.2.1.2.1 35 (26.1%) 48 (35.8%) 55 (41.7%) 138 (34.5%)
#> dcd D.1.1.1.1 50 (37.3%) 42 (31.3%) 51 (38.6%) 143 (35.8%)
#> dcd D.1.1.4.2 48 (35.8%) 42 (31.3%) 50 (37.9%) 140 (35.0%)
#> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%)tables.R
Putting everything together, the final AE table looks like this:
basic_table() %>%
split_cols_by(var = "ACTARM") %>%
add_colcounts() %>%
add_overall_col(label = "All Patients") %>%
summarize_num_patients(
var = "USUBJID",
.stats = c("unique", "nonunique"),
.labels = c(
unique = "Total number of patients with at least one AE",
nonunique = "Overall total number of events"
)
) %>%
split_rows_by(
"AEBODSYS",
child_labels = "visible",
nested = FALSE,
indent_mod = -1L,
split_fun = drop_split_levels
) %>%
summarize_num_patients(
var = "USUBJID",
.stats = c("unique", "nonunique"),
.labels = c(
unique = "Total number of patients with at least one AE",
nonunique = "Overall total number of events"
)
) %>%
count_occurrences(vars = "AEDECOD") %>%
build_table(
df = adae,
alt_counts_df = adsl
)
#> A: Drug X B: Placebo C: Combination All Patients
#> (N=134) (N=134) (N=132) (N=400)
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one AE 122 (91.0%) 123 (91.8%) 120 (90.9%) 365 (91.2%)
#> Overall total number of events 609 622 703 1934
#> cl A.1
#> Total number of patients with at least one AE 78 (58.2%) 75 (56.0%) 89 (67.4%) 242 (60.5%)
#> Overall total number of events 132 130 160 422
#> dcd A.1.1.1.1 50 (37.3%) 45 (33.6%) 63 (47.7%) 158 (39.5%)
#> dcd A.1.1.1.2 48 (35.8%) 48 (35.8%) 50 (37.9%) 146 (36.5%)
#> cl B.1
#> Total number of patients with at least one AE 47 (35.1%) 49 (36.6%) 43 (32.6%) 139 (34.8%)
#> Overall total number of events 56 60 62 178
#> dcd B.1.1.1.1 47 (35.1%) 49 (36.6%) 43 (32.6%) 139 (34.8%)
#> cl B.2
#> Total number of patients with at least one AE 79 (59.0%) 74 (55.2%) 85 (64.4%) 238 (59.5%)
#> Overall total number of events 129 138 143 410
#> dcd B.2.1.2.1 49 (36.6%) 44 (32.8%) 52 (39.4%) 145 (36.2%)
#> dcd B.2.2.3.1 48 (35.8%) 54 (40.3%) 51 (38.6%) 153 (38.2%)
#> cl C.1
#> Total number of patients with at least one AE 43 (32.1%) 46 (34.3%) 43 (32.6%) 132 (33.0%)
#> Overall total number of events 55 63 64 182
#> dcd C.1.1.1.3 43 (32.1%) 46 (34.3%) 43 (32.6%) 132 (33.0%)
#> cl C.2
#> Total number of patients with at least one AE 35 (26.1%) 48 (35.8%) 55 (41.7%) 138 (34.5%)
#> Overall total number of events 48 53 65 166
#> dcd C.2.1.2.1 35 (26.1%) 48 (35.8%) 55 (41.7%) 138 (34.5%)
#> cl D.1
#> Total number of patients with at least one AE 79 (59.0%) 67 (50.0%) 80 (60.6%) 226 (56.5%)
#> Overall total number of events 127 106 135 368
#> dcd D.1.1.1.1 50 (37.3%) 42 (31.3%) 51 (38.6%) 143 (35.8%)
#> dcd D.1.1.4.2 48 (35.8%) 42 (31.3%) 50 (37.9%) 140 (35.0%)
#> cl D.2
#> Total number of patients with at least one AE 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%)
#> Overall total number of events 62 72 74 208
#> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%)tables.R
A typical response table for a binary clinical trial endpoint may be composed of several different analyses:
We can build a table layout like this by following the same approach
we used for the AE table: each table section will be produced using a
different layout creating function from tern.
First we start with some data preparation steps to set up the
analysis dataset. We select the endpoint to analyze from
PARAMCD and define the logical variable is_rsp
which indicates whether a patient is classified as a responder or
not.
# Preprocessing to select an analysis endpoint.
anl <- adrs %>%
dplyr::filter(PARAMCD == "BESRSPI") %>%
dplyr::mutate(is_rsp = AVALC %in% c("CR", "PR"))tables.R
To create a summary of the proportion of responders in each treatment
group, use the estimate_proportion() layout creating
function:
basic_table() %>%
split_cols_by(var = "ARM") %>%
add_colcounts() %>%
estimate_proportion(
vars = "is_rsp",
table_names = "est_prop"
) %>%
build_table(anl)
#> A: Drug X B: Placebo C: Combination
#> (N=134) (N=134) (N=132)
#> —————————————————————————————————————————————————————————————————————————————
#> Responders 114 (85.1%) 90 (67.2%) 120 (90.9%)
#> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2)tables.R
To specify which arm in the table should be used as the reference,
use the argument ref_group from
split_cols_by(). Below we change the reference arm to “B:
Placebo” and so this arm is displayed as the first column:
basic_table() %>%
split_cols_by(var = "ARM", ref_group = "B: Placebo") %>%
add_colcounts() %>%
estimate_proportion(
vars = "is_rsp"
) %>%
build_table(anl)
#> A: Drug X B: Placebo C: Combination
#> (N=134) (N=134) (N=132)
#> —————————————————————————————————————————————————————————————————————————————
#> Responders 114 (85.1%) 90 (67.2%) 120 (90.9%)
#> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2)tables.R
To further customize the analysis, we can use the method
and conf_level arguments to modify the type of confidence
interval that is calculated:
basic_table() %>%
split_cols_by(var = "ARM", ref_group = "B: Placebo") %>%
add_colcounts() %>%
estimate_proportion(
vars = "is_rsp",
method = "clopper-pearson",
conf_level = 0.9
) %>%
build_table(anl)
#> A: Drug X B: Placebo C: Combination
#> (N=134) (N=134) (N=132)
#> ———————————————————————————————————————————————————————————————————————
#> Responders 114 (85.1%) 90 (67.2%) 120 (90.9%)
#> 90% CI (Clopper-Pearson) (79.1, 89.9) (59.9, 73.9) (85.7, 94.7)tables.R
The next table section needed should summarize the difference in
response rates between the reference arm each comparison arm. Use
estimate_proportion_diff() layout creating function for
this:
basic_table() %>%
split_cols_by(var = "ARM", ref_group = "B: Placebo") %>%
add_colcounts() %>%
estimate_proportion_diff(
vars = "is_rsp",
show_labels = "visible",
var_labels = "Unstratified Analysis"
) %>%
build_table(anl)
#> A: Drug X B: Placebo C: Combination
#> (N=134) (N=134) (N=132)
#> ——————————————————————————————————————————————————————————————————————————————
#> Unstratified Analysis
#> Difference in Response rate (%) 17.9 23.7
#> 95% CI (Wald, with correction) (7.2, 28.6) (13.7, 33.8)tables.R
The final section needed to complete the table includes a statistical
test for the difference in response rates. Use the
test_proportion_diff() layout creating function for
this:
basic_table() %>%
split_cols_by(var = "ARM", ref_group = "B: Placebo") %>%
add_colcounts() %>%
test_proportion_diff(vars = "is_rsp") %>%
build_table(anl)
#> A: Drug X B: Placebo C: Combination
#> (N=134) (N=134) (N=132)
#> ——————————————————————————————————————————————————————————————————————
#> p-value (Chi-Squared Test) 0.0006 <0.0001tables.R
To customize the output, we use the method argument to
select a Chi-Squared test with Schouten correction.
basic_table() %>%
split_cols_by(var = "ARM", ref_group = "B: Placebo") %>%
add_colcounts() %>%
test_proportion_diff(
vars = "is_rsp",
method = "schouten"
) %>%
build_table(anl)
#> A: Drug X B: Placebo C: Combination
#> (N=134) (N=134) (N=132)
#> ———————————————————————————————————————————————————————————————————————————————————————————————
#> p-value (Chi-Squared Test with Schouten Correction) 0.0008 <0.0001tables.R
Now we can put all the table sections together in one layout
pipeline. Note there is one more small change needed. Since the primary
analysis variable in all table sections is the same
(is_rsp), we need to give each sub-table a unique name.
This is done by adding the table_names argument and
providing unique names through that:
basic_table() %>%
split_cols_by(var = "ARM", ref_group = "B: Placebo") %>%
add_colcounts() %>%
estimate_proportion(
vars = "is_rsp",
method = "clopper-pearson",
conf_level = 0.9,
table_names = "est_prop"
) %>%
estimate_proportion_diff(
vars = "is_rsp",
show_labels = "visible",
var_labels = "Unstratified Analysis",
table_names = "est_prop_diff"
) %>%
test_proportion_diff(
vars = "is_rsp",
method = "schouten",
table_names = "test_prop_diff"
) %>%
build_table(anl)
#> A: Drug X B: Placebo C: Combination
#> (N=134) (N=134) (N=132)
#> ————————————————————————————————————————————————————————————————————————————————————————————————————
#> Responders 114 (85.1%) 90 (67.2%) 120 (90.9%)
#> 90% CI (Clopper-Pearson) (79.1, 89.9) (59.9, 73.9) (85.7, 94.7)
#> Unstratified Analysis
#> Difference in Response rate (%) 17.9 23.7
#> 95% CI (Wald, with correction) (7.2, 28.6) (13.7, 33.8)
#> p-value (Chi-Squared Test with Schouten Correction) 0.0008 <0.0001tables.R
Tabulation with tern builds on top of the the layout
tabulation framework from rtables. Complex tables are built
step by step in a pipeline by combining layout creating functions that
perform a specific type of analysis.
The tern analyze functions introduced in this vignette
are:
analyze_vars()summarize_num_patients()count_occurrences()estimate_proportion()estimate_proportion_diff()test_proportion_diff()Layout creating functions build a formatted layout by
controlling features such as labels, numerical display formats and
indentation. These functions are wrappers for the Statistics functions
which calculate the raw summaries of each analysis. You can easily spot
Statistics functions in the documentation because they always begin with
the prefix s_. It can be helpful to inspect and run
Statistics functions to understand ways an analysis can be
customized.