+
Demographic Table
+
+
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%)
+
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")) # nolint
+
+# Reorder the levels in the SEX variable.
+adsl$SEX <- factor(adsl$SEX, levels = c("M", "F", "U", "UNDIFFERENTIATED")) # nolint
+
+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%)
+
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 3
+
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%)
+
+
Adverse Event Table
+
+
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 1934
+
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.
+
+
Statistics Functions
+
+
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)"
+
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 122 123 120 365
+
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 74 208
+
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%)
+
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%)
+
+
+
Response Table
+
+
A typical response table for a binary clinical trial endpoint may be
+composed of several different analyses:
+
+- Proportion of responders in each treatment group
+- Difference between proportion of responders in comparison groups
+vs. control group
+- Chi-Square test for difference in response rates between comparison
+groups vs. control group
+
+
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"))
+
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)
+
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)
+#> B: Placebo A: Drug X C: Combination
+#> (N=134) (N=134) (N=132)
+#> —————————————————————————————————————————————————————————————————————————————
+#> Responders 90 (67.2%) 114 (85.1%) 120 (90.9%)
+#> 95% CI (Wald, with correction) (58.8, 75.5) (78.7, 91.5) (85.6, 96.2)
+
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)
+#> B: Placebo A: Drug X C: Combination
+#> (N=134) (N=134) (N=132)
+#> ———————————————————————————————————————————————————————————————————————
+#> Responders 90 (67.2%) 114 (85.1%) 120 (90.9%)
+#> 90% CI (Clopper-Pearson) (59.9, 73.9) (79.1, 89.9) (85.7, 94.7)
+
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)
+#> B: Placebo A: Drug X 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)
+
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:
+
+
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)
+#> B: Placebo A: Drug X C: Combination
+#> (N=134) (N=134) (N=132)
+#> ———————————————————————————————————————————————————————————————————————————————————————————————
+#> p-value (Chi-Squared Test with Schouten Correction) 0.0008 <0.0001
+
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)
+#> B: Placebo A: Drug X C: Combination
+#> (N=134) (N=134) (N=132)
+#> ————————————————————————————————————————————————————————————————————————————————————————————————————
+#> Responders 90 (67.2%) 114 (85.1%) 120 (90.9%)
+#> 90% CI (Clopper-Pearson) (59.9, 73.9) (79.1, 89.9) (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 <0.0001
+
