![[Stable]](figures/lifecycle-stable.svg)
The tabulate_rsp_subgroups() function creates a layout element to tabulate binary response by subgroup, returning
statistics including response rate and odds ratio for each population subgroup. The table is created from df, a
list of data frames returned by extract_rsp_subgroups(), with the statistics to include specified via the vars
parameter.
A forest plot can be created from the resulting table using the g_forest() function.
tabulate_rsp_subgroups(
lyt,
df,
vars = c("n_tot", "n", "prop", "or", "ci"),
groups_lists = list(),
label_all = lifecycle::deprecated(),
riskdiff = NULL,
na_str = default_na_str(),
...,
.stat_names = NULL,
.formats = NULL,
.labels = NULL,
.indent_mods = NULL
)
a_response_subgroups(
df,
labelstr = "",
...,
.stats = NULL,
.stat_names = NULL,
.formats = NULL,
.labels = NULL,
.indent_mods = NULL
)Arguments
- lyt
(
PreDataTableLayouts)
layout that analyses will be added to.- df
(
list)
a list of data frames containing all analysis variables. List should be created usingextract_rsp_subgroups().- vars
(
character)
the names of statistics to be reported among:n: Total number of observations per group.n_rsp: Number of responders per group.prop: Proportion of responders.n_tot: Total number of observations.or: Odds ratio.ci: Confidence interval of odds ratio.pval: p-value of the effect. Note, the statisticsn_tot,or, andciare required.
- groups_lists
(named
listoflist)
optionally contains for eachsubgroupsvariable a list, which specifies the new group levels via the names and the levels that belong to it in the character vectors that are elements of the list.- label_all
(
string)
label for the total population analysis.- riskdiff
(
list)
if a risk (proportion) difference column should be added, a list of settings to apply within the column. Seecontrol_riskdiff()for details. IfNULL, no risk difference column will be added. Ifriskdiff$arm_xandriskdiff$arm_yareNULL, the first level ofdf$prop$armwill be used asarm_xand the second level asarm_y.- na_str
(
string)
string used to replace allNAor empty values in the output.- ...
additional arguments for the lower level functions.
- .stat_names
(
character)
names of the statistics that are passed directly to name single statistics (.stats). This option is visible when producingrtables::as_result_df()withmake_ard = TRUE.- .formats
(named
characterorlist)
formats for the statistics. See Details inanalyze_varsfor more information on the"auto"setting.- .labels
(named
character)
labels for the statistics (without indent).- .indent_mods
(named
integer)
indent modifiers for the labels. Defaults to 0, which corresponds to the unmodified default behavior. Can be negative.- labelstr
(
string)
label of the level of the parent split currently being summarized (must be present as second argument in Content Row Functions). Seertables::summarize_row_groups()for more information.- .stats
(
character)
statistics to select for the table.
Value
An rtables table summarizing binary response by subgroup.
a_response_subgroups()returns the corresponding list with formattedrtables::CellValue().
Details
These functions create a layout starting from a data frame which contains the required statistics. Tables typically used as part of forest plot.
Functions
tabulate_rsp_subgroups(): Table-creating function which creates a table summarizing binary response by subgroup. This function is a wrapper forrtables::analyze_colvars()andrtables::summarize_row_groups().a_response_subgroups(): Formatted analysis function which is used asafunintabulate_rsp_subgroups().
See also
Examples
library(dplyr)
library(forcats)
adrs <- tern_ex_adrs
adrs_labels <- formatters::var_labels(adrs)
adrs_f <- adrs %>%
filter(PARAMCD == "BESRSPI") %>%
filter(ARM %in% c("A: Drug X", "B: Placebo")) %>%
droplevels() %>%
mutate(
# Reorder levels of factor to make the placebo group the reference arm.
ARM = fct_relevel(ARM, "B: Placebo"),
rsp = AVALC == "CR"
)
formatters::var_labels(adrs_f) <- c(adrs_labels, "Response")
# Unstratified analysis.
df <- extract_rsp_subgroups(
variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
data = adrs_f
)
df
#> $prop
#> arm n n_rsp prop subgroup var
#> 1 B: Placebo 73 50 0.6849315 All Patients ALL
#> 2 A: Drug X 69 59 0.8550725 All Patients ALL
#> 3 B: Placebo 40 25 0.6250000 F SEX
#> 4 A: Drug X 38 36 0.9473684 F SEX
#> 5 B: Placebo 33 25 0.7575758 M SEX
#> 6 A: Drug X 31 23 0.7419355 M SEX
#> 7 B: Placebo 24 13 0.5416667 LOW BMRKR2
#> 8 A: Drug X 26 21 0.8076923 LOW BMRKR2
#> 9 B: Placebo 23 17 0.7391304 MEDIUM BMRKR2
#> 10 A: Drug X 26 23 0.8846154 MEDIUM BMRKR2
#> 11 B: Placebo 26 20 0.7692308 HIGH BMRKR2
#> 12 A: Drug X 17 15 0.8823529 HIGH BMRKR2
#> var_label row_type
#> 1 All Patients content
#> 2 All Patients content
#> 3 Sex analysis
#> 4 Sex analysis
#> 5 Sex analysis
#> 6 Sex analysis
#> 7 Continuous Level Biomarker 2 analysis
#> 8 Continuous Level Biomarker 2 analysis
#> 9 Continuous Level Biomarker 2 analysis
#> 10 Continuous Level Biomarker 2 analysis
#> 11 Continuous Level Biomarker 2 analysis
#> 12 Continuous Level Biomarker 2 analysis
#>
#> $or
#> arm n_tot or lcl ucl conf_level subgroup var
#> 1 142 2.714000 1.1804488 6.239827 0.95 All Patients ALL
#> 2 78 10.800000 2.2669576 51.452218 0.95 F SEX
#> 3 64 0.920000 0.2966470 2.853223 0.95 M SEX
#> 4 50 3.553846 1.0047370 12.570277 0.95 LOW BMRKR2
#> 5 49 2.705882 0.5911718 12.385232 0.95 MEDIUM BMRKR2
#> 6 43 2.250000 0.3970298 12.750933 0.95 HIGH BMRKR2
#> var_label row_type
#> 1 All Patients content
#> 2 Sex analysis
#> 3 Sex analysis
#> 4 Continuous Level Biomarker 2 analysis
#> 5 Continuous Level Biomarker 2 analysis
#> 6 Continuous Level Biomarker 2 analysis
#>
# Stratified analysis.
df_strat <- extract_rsp_subgroups(
variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2"), strata = "STRATA1"),
data = adrs_f
)
df_strat
#> $prop
#> arm n n_rsp prop subgroup var
#> 1 B: Placebo 73 50 0.6849315 All Patients ALL
#> 2 A: Drug X 69 59 0.8550725 All Patients ALL
#> 3 B: Placebo 40 25 0.6250000 F SEX
#> 4 A: Drug X 38 36 0.9473684 F SEX
#> 5 B: Placebo 33 25 0.7575758 M SEX
#> 6 A: Drug X 31 23 0.7419355 M SEX
#> 7 B: Placebo 24 13 0.5416667 LOW BMRKR2
#> 8 A: Drug X 26 21 0.8076923 LOW BMRKR2
#> 9 B: Placebo 23 17 0.7391304 MEDIUM BMRKR2
#> 10 A: Drug X 26 23 0.8846154 MEDIUM BMRKR2
#> 11 B: Placebo 26 20 0.7692308 HIGH BMRKR2
#> 12 A: Drug X 17 15 0.8823529 HIGH BMRKR2
#> var_label row_type
#> 1 All Patients content
#> 2 All Patients content
#> 3 Sex analysis
#> 4 Sex analysis
#> 5 Sex analysis
#> 6 Sex analysis
#> 7 Continuous Level Biomarker 2 analysis
#> 8 Continuous Level Biomarker 2 analysis
#> 9 Continuous Level Biomarker 2 analysis
#> 10 Continuous Level Biomarker 2 analysis
#> 11 Continuous Level Biomarker 2 analysis
#> 12 Continuous Level Biomarker 2 analysis
#>
#> $or
#> arm n_tot or lcl ucl conf_level subgroup var
#> 1 142 2.6343899 1.1537821 6.015009 0.95 All Patients ALL
#> 2 78 9.5946605 2.0379337 45.171985 0.95 F SEX
#> 3 64 0.8947158 0.2936803 2.725809 0.95 M SEX
#> 4 50 3.5976656 1.0101319 12.813374 0.95 LOW BMRKR2
#> 5 49 2.6242168 0.5162572 13.339308 0.95 MEDIUM BMRKR2
#> 6 43 2.2816865 0.4116391 12.647228 0.95 HIGH BMRKR2
#> var_label row_type
#> 1 All Patients content
#> 2 Sex analysis
#> 3 Sex analysis
#> 4 Continuous Level Biomarker 2 analysis
#> 5 Continuous Level Biomarker 2 analysis
#> 6 Continuous Level Biomarker 2 analysis
#>
# Grouping of the BMRKR2 levels.
df_grouped <- extract_rsp_subgroups(
variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
data = adrs_f,
groups_lists = list(
BMRKR2 = list(
"low" = "LOW",
"low/medium" = c("LOW", "MEDIUM"),
"low/medium/high" = c("LOW", "MEDIUM", "HIGH")
)
)
)
df_grouped
#> $prop
#> arm n n_rsp prop subgroup var
#> 1 B: Placebo 73 50 0.6849315 All Patients ALL
#> 2 A: Drug X 69 59 0.8550725 All Patients ALL
#> 3 B: Placebo 40 25 0.6250000 F SEX
#> 4 A: Drug X 38 36 0.9473684 F SEX
#> 5 B: Placebo 33 25 0.7575758 M SEX
#> 6 A: Drug X 31 23 0.7419355 M SEX
#> 7 B: Placebo 24 13 0.5416667 low BMRKR2
#> 8 A: Drug X 26 21 0.8076923 low BMRKR2
#> 9 B: Placebo 47 30 0.6382979 low/medium BMRKR2
#> 10 A: Drug X 52 44 0.8461538 low/medium BMRKR2
#> 11 B: Placebo 73 50 0.6849315 low/medium/high BMRKR2
#> 12 A: Drug X 69 59 0.8550725 low/medium/high BMRKR2
#> var_label row_type
#> 1 All Patients content
#> 2 All Patients content
#> 3 Sex analysis
#> 4 Sex analysis
#> 5 Sex analysis
#> 6 Sex analysis
#> 7 Continuous Level Biomarker 2 analysis
#> 8 Continuous Level Biomarker 2 analysis
#> 9 Continuous Level Biomarker 2 analysis
#> 10 Continuous Level Biomarker 2 analysis
#> 11 Continuous Level Biomarker 2 analysis
#> 12 Continuous Level Biomarker 2 analysis
#>
#> $or
#> arm n_tot or lcl ucl conf_level subgroup var
#> 1 142 2.714000 1.180449 6.239827 0.95 All Patients ALL
#> 2 78 10.800000 2.266958 51.452218 0.95 F SEX
#> 3 64 0.920000 0.296647 2.853223 0.95 M SEX
#> 4 50 3.553846 1.004737 12.570277 0.95 low BMRKR2
#> 5 99 3.116667 1.193409 8.139385 0.95 low/medium BMRKR2
#> 6 142 2.714000 1.180449 6.239827 0.95 low/medium/high BMRKR2
#> var_label row_type
#> 1 All Patients content
#> 2 Sex analysis
#> 3 Sex analysis
#> 4 Continuous Level Biomarker 2 analysis
#> 5 Continuous Level Biomarker 2 analysis
#> 6 Continuous Level Biomarker 2 analysis
#>
# Table with default columns
basic_table() %>%
tabulate_rsp_subgroups(df)
#> B: Placebo A: Drug X
#> Baseline Risk Factors Total n n Response (%) n Response (%) Odds Ratio 95% CI
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————
#> All Patients 142 73 68.5% 69 85.5% 2.71 (1.18, 6.24)
#> Sex
#> F 78 40 62.5% 38 94.7% 10.80 (2.27, 51.45)
#> M 64 33 75.8% 31 74.2% 0.92 (0.30, 2.85)
#> Continuous Level Biomarker 2
#> LOW 50 24 54.2% 26 80.8% 3.55 (1.00, 12.57)
#> MEDIUM 49 23 73.9% 26 88.5% 2.71 (0.59, 12.39)
#> HIGH 43 26 76.9% 17 88.2% 2.25 (0.40, 12.75)
# Table with selected columns
basic_table() %>%
tabulate_rsp_subgroups(
df = df,
vars = c("n_tot", "n", "n_rsp", "prop", "or", "ci")
)
#> B: Placebo A: Drug X
#> Baseline Risk Factors Total n n Responders Response (%) n Responders Response (%) Odds Ratio 95% CI
#> —————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> All Patients 142 73 50 68.5% 69 59 85.5% 2.71 (1.18, 6.24)
#> Sex
#> F 78 40 25 62.5% 38 36 94.7% 10.80 (2.27, 51.45)
#> M 64 33 25 75.8% 31 23 74.2% 0.92 (0.30, 2.85)
#> Continuous Level Biomarker 2
#> LOW 50 24 13 54.2% 26 21 80.8% 3.55 (1.00, 12.57)
#> MEDIUM 49 23 17 73.9% 26 23 88.5% 2.71 (0.59, 12.39)
#> HIGH 43 26 20 76.9% 17 15 88.2% 2.25 (0.40, 12.75)
# Table with risk difference column added
basic_table() %>%
tabulate_rsp_subgroups(
df,
riskdiff = control_riskdiff(
arm_x = levels(df$prop$arm)[1],
arm_y = levels(df$prop$arm)[2]
)
)
#> B: Placebo A: Drug X
#> Baseline Risk Factors Total n n Response (%) n Response (%) Odds Ratio 95% CI Risk Difference (%) (95% CI)
#> ——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> All Patients 142 73 68.5% 69 85.5% 2.71 (1.18, 6.24) -17.0 (-30.5 - -3.5)
#> Sex
#> F 78 40 62.5% 38 94.7% 10.80 (2.27, 51.45) -32.2 (-48.8 - -15.6)
#> M 64 33 75.8% 31 74.2% 0.92 (0.30, 2.85) 1.6 (-19.7 - 22.8)
#> Continuous Level Biomarker 2
#> LOW 50 24 54.2% 26 80.8% 3.55 (1.00, 12.57) -26.6 (-51.6 - -1.6)
#> MEDIUM 49 23 73.9% 26 88.5% 2.71 (0.59, 12.39) -14.5 (-36.3 - 7.2)
#> HIGH 43 26 76.9% 17 88.2% 2.25 (0.40, 12.75) -11.3 (-33.6 - 11.0)