Generating QC-Ready Result Data Frames (ARDs) from Tables
Davide Garolini
2024-12-20
Source:vignettes/ard_how_to.Rmd
ard_how_to.RmdDisclaimer
This vignette is a work in progress and subject to change.
Creating an Example Table
In order to generate an ARD (Analysis Results Dataset), we first need to create a table from which all the necessary information will be retrieved. We will borrow a simple table from this vignette about clinical trials.
library(rtables)
# Loading required package: formatters
#
# Attaching package: 'formatters'
# The following object is masked from 'package:base':
#
# %||%
# Loading required package: magrittr
#
# Attaching package: 'rtables'
# The following object is masked from 'package:utils':
#
# str
ADSL <- ex_adsl # Example ADSL dataset
# Very simple table
lyt <- basic_table() %>%
split_cols_by("ARM") %>%
analyze(c("AGE", "SEX"))
tbl <- build_table(lyt, ADSL)
tbl
# A: Drug X B: Placebo C: Combination
# ————————————————————————————————————————————————————————————
# AGE
# Mean 33.77 35.43 35.43
# SEX
# F 79 77 66
# M 51 55 60
# U 3 2 4
# UNDIFFERENTIATED 1 0 2Converting the Table to a Result Data Frame (ARD)
The as_result_df() function is used to convert a table
to a result data frame. The result data frame is a data frame that
contains the result of the summary table and is ready to be used for
quality control purposes. This may be customized according to different
standards.
Let’s see how we can produce different result
data.frames. The following outputs can be returned by
setting different parameters in the as_results_df()
function, and these results can be transformed back into a table using
the df_to_tt() function.
as_result_df(tbl)
# group1 group1_level avar_name row_name
# 1 <analysis_spl_tbl_name> ma_AGE_SEX AGE Mean
# 2 <analysis_spl_tbl_name> ma_AGE_SEX SEX F
# 3 <analysis_spl_tbl_name> ma_AGE_SEX SEX M
# 4 <analysis_spl_tbl_name> ma_AGE_SEX SEX U
# 5 <analysis_spl_tbl_name> ma_AGE_SEX SEX UNDIFFERENTIATED
# label_name row_num is_group_summary node_class A: Drug X B: Placebo
# 1 Mean 2 FALSE DataRow 33.76866 35.43284
# 2 F 4 FALSE DataRow 79 77
# 3 M 5 FALSE DataRow 51 55
# 4 U 6 FALSE DataRow 3 2
# 5 UNDIFFERENTIATED 7 FALSE DataRow 1 0
# C: Combination
# 1 35.43182
# 2 66
# 3 60
# 4 4
# 5 2
as_result_df(tbl, data_format = "strings")
# group1 group1_level avar_name row_name
# 1 <analysis_spl_tbl_name> ma_AGE_SEX AGE Mean
# 2 <analysis_spl_tbl_name> ma_AGE_SEX SEX F
# 3 <analysis_spl_tbl_name> ma_AGE_SEX SEX M
# 4 <analysis_spl_tbl_name> ma_AGE_SEX SEX U
# 5 <analysis_spl_tbl_name> ma_AGE_SEX SEX UNDIFFERENTIATED
# label_name row_num is_group_summary node_class A: Drug X B: Placebo
# 1 Mean 2 FALSE DataRow 33.77 35.43
# 2 F 4 FALSE DataRow 79 77
# 3 M 5 FALSE DataRow 51 55
# 4 U 6 FALSE DataRow 3 2
# 5 UNDIFFERENTIATED 7 FALSE DataRow 1 0
# C: Combination
# 1 35.43
# 2 66
# 3 60
# 4 4
# 5 2
as_result_df(tbl, simplify = TRUE)
# label_name A: Drug X B: Placebo C: Combination
# 1 Mean 33.76866 35.43284 35.43182
# 2 F 79 77 66
# 3 M 51 55 60
# 4 U 3 2 4
# 5 UNDIFFERENTIATED 1 0 2
as_result_df(tbl, simplify = TRUE, keep_label_rows = TRUE)
# label_name A: Drug X B: Placebo C: Combination
# 1 AGE NA NA NA
# 2 Mean 33.76866 35.43284 35.43182
# 3 SEX NA NA NA
# 4 F 79 77 66
# 5 M 51 55 60
# 6 U 3 2 4
# 7 UNDIFFERENTIATED 1 0 2
as_result_df(tbl, simplify = TRUE, keep_label_rows = TRUE, expand_colnames = TRUE)
# label_name A: Drug X B: Placebo C: Combination
# 1 <only_for_column_names> A: Drug X B: Placebo C: Combination
# 2 AGE NA NA NA
# 3 Mean 33.76866 35.43284 35.43182
# 4 SEX NA NA NA
# 5 F 79 77 66
# 6 M 51 55 60
# 7 U 3 2 4
# 8 UNDIFFERENTIATED 1 0 2Now let’s generate our final ARD output, which is ready to be used for quality control purposes.
as_result_df(tbl, make_ard = TRUE)
# group1 group1_level group2 group2_level variable
# 1 <analysis_spl_tbl_name> ma_AGE_SEX ARM A: Drug X AGE
# 2 <analysis_spl_tbl_name> ma_AGE_SEX ARM A: Drug X SEX
# 3 <analysis_spl_tbl_name> ma_AGE_SEX ARM A: Drug X SEX
# 4 <analysis_spl_tbl_name> ma_AGE_SEX ARM A: Drug X SEX
# 5 <analysis_spl_tbl_name> ma_AGE_SEX ARM A: Drug X SEX
# 6 <analysis_spl_tbl_name> ma_AGE_SEX ARM B: Placebo AGE
# 7 <analysis_spl_tbl_name> ma_AGE_SEX ARM B: Placebo SEX
# 8 <analysis_spl_tbl_name> ma_AGE_SEX ARM B: Placebo SEX
# 9 <analysis_spl_tbl_name> ma_AGE_SEX ARM B: Placebo SEX
# 10 <analysis_spl_tbl_name> ma_AGE_SEX ARM B: Placebo SEX
# 11 <analysis_spl_tbl_name> ma_AGE_SEX ARM C: Combination AGE
# 12 <analysis_spl_tbl_name> ma_AGE_SEX ARM C: Combination SEX
# 13 <analysis_spl_tbl_name> ma_AGE_SEX ARM C: Combination SEX
# 14 <analysis_spl_tbl_name> ma_AGE_SEX ARM C: Combination SEX
# 15 <analysis_spl_tbl_name> ma_AGE_SEX ARM C: Combination SEX
# variable_level variable_label stat_name stat
# 1 Mean Mean mean 33.76866
# 2 F F n 79.00000
# 3 M M n 51.00000
# 4 U U n 3.00000
# 5 UNDIFFERENTIATED UNDIFFERENTIATED n 1.00000
# 6 Mean Mean mean 35.43284
# 7 F F n 77.00000
# 8 M M n 55.00000
# 9 U U n 2.00000
# 10 UNDIFFERENTIATED UNDIFFERENTIATED n 0.00000
# 11 Mean Mean mean 35.43182
# 12 F F n 66.00000
# 13 M M n 60.00000
# 14 U U n 4.00000
# 15 UNDIFFERENTIATED UNDIFFERENTIATED n 2.00000Customizing the Output
as_result_df() and ARDs depend on the content of the
table, so it is possible to modify the table to customize the output.
For example, we can add some user-defined statistics with custom
names:
# rcell and in_rows are the core of any analysis function
rc <- rcell(c(1, 2), stat_names = c("Rand1", "Rand2"))
print(obj_stat_names(rc)) # c("Rand1", "Rand2")
# [1] "Rand1" "Rand2"
rc_row <- in_rows(
.list = list(a = c(NA, 1), b = c(1, NA)),
.formats = c("xx - xx", "xx.x - xx.x"),
.format_na_strs = list(c("asda", "lkjklj")),
.stat_names = list(c("A", "B"), c("B", "C"))
)
# Only a getter for this object
print(obj_stat_names(rc_row)) # list(a = c("A", "B"), b = c("B", "C"))
# $a
# [1] "A" "B"
#
# $b
# [1] "B" "C"
# if c("A", "B"), one for each row
# if single list, duplicated
rc_row <- in_rows(
.list = list(a = c(NA, 1), b = c(1, NA)),
.formats = c("xx - xx", "xx.x - xx.x"),
.format_na_strs = list(c("asda", "lkjklj")),
.stat_names = c("A", "B")
)
print(obj_stat_names(rc_row)) # c("A", "B") # one for each row
# $a
# [1] "A"
#
# $b
# [1] "B"
print(lapply(rc_row, obj_stat_names)) # identical to above + row names
# $a
# [1] "A"
#
# $b
# [1] "B"
rc_row <- in_rows(
.list = list(a = c(NA, 1), b = c(1, NA)),
.formats = c("xx - xx", "xx.x - xx.x"),
.format_na_strs = list(c("asda", "lkjklj")),
.stat_names = list(c("A", "B")) # It is duplicated, check it yourself!
)Let’s put it into practice:
mean_sd_custom <- function(x) {
mean <- mean(x, na.rm = FALSE)
sd <- sd(x, na.rm = FALSE)
rcell(c(mean, sd),
label = "Mean (SD)", format = "xx.x (xx.x)" # ,
# stat_names = c("Mean", "SD")
)
}
counts_percentage_custom <- function(x) {
cnts <- table(x)
out <- lapply(cnts, function(x) {
perc <- x / sum(cnts)
rcell(c(x, perc), format = "xx. (xx.%)")
})
in_rows(
.list = as.list(out), .labels = names(cnts),
.stat_names = list(c("Count", "Percentage"))
)
}
lyt <- basic_table(show_colcounts = TRUE, colcount_format = "N=xx") %>%
split_cols_by("ARM", split_fun = keep_split_levels(c("A: Drug X", "B: Placebo"))) %>%
analyze(vars = "AGE", afun = mean_sd_custom) %>%
analyze(vars = "SEX", afun = counts_percentage_custom)
tbl <- build_table(lyt, ex_adsl)
as_result_df(tbl, make_ard = TRUE)
# group1 group1_level group2 group2_level variable
# 1 <analysis_spl_tbl_name> ma_AGE_SEX ARM A: Drug X AGE
# 2 <analysis_spl_tbl_name> ma_AGE_SEX ARM A: Drug X AGE
# 3 <analysis_spl_tbl_name> ma_AGE_SEX ARM A: Drug X SEX
# 4 <analysis_spl_tbl_name> ma_AGE_SEX ARM A: Drug X SEX
# 5 <analysis_spl_tbl_name> ma_AGE_SEX ARM A: Drug X SEX
# 6 <analysis_spl_tbl_name> ma_AGE_SEX ARM A: Drug X SEX
# 7 <analysis_spl_tbl_name> ma_AGE_SEX ARM A: Drug X SEX
# 8 <analysis_spl_tbl_name> ma_AGE_SEX ARM A: Drug X SEX
# 9 <analysis_spl_tbl_name> ma_AGE_SEX ARM A: Drug X SEX
# 10 <analysis_spl_tbl_name> ma_AGE_SEX ARM A: Drug X SEX
# 11 <analysis_spl_tbl_name> ma_AGE_SEX ARM B: Placebo AGE
# 12 <analysis_spl_tbl_name> ma_AGE_SEX ARM B: Placebo AGE
# 13 <analysis_spl_tbl_name> ma_AGE_SEX ARM B: Placebo SEX
# 14 <analysis_spl_tbl_name> ma_AGE_SEX ARM B: Placebo SEX
# 15 <analysis_spl_tbl_name> ma_AGE_SEX ARM B: Placebo SEX
# 16 <analysis_spl_tbl_name> ma_AGE_SEX ARM B: Placebo SEX
# 17 <analysis_spl_tbl_name> ma_AGE_SEX ARM B: Placebo SEX
# 18 <analysis_spl_tbl_name> ma_AGE_SEX ARM B: Placebo SEX
# 19 <analysis_spl_tbl_name> ma_AGE_SEX ARM B: Placebo SEX
# 20 <analysis_spl_tbl_name> ma_AGE_SEX ARM B: Placebo SEX
# variable_level variable_label stat_name stat
# 1 Mean (SD) Mean (SD) <NA> 33.768656716
# 2 Mean (SD) Mean (SD) <NA> 6.553325712
# 3 F F Count 79.000000000
# 4 F F Percentage 0.589552239
# 5 M M Count 51.000000000
# 6 M M Percentage 0.380597015
# 7 U U Count 3.000000000
# 8 U U Percentage 0.022388060
# 9 UNDIFFERENTIATED UNDIFFERENTIATED Count 1.000000000
# 10 UNDIFFERENTIATED UNDIFFERENTIATED Percentage 0.007462687
# 11 Mean (SD) Mean (SD) <NA> 35.432835821
# 12 Mean (SD) Mean (SD) <NA> 7.895413879
# 13 F F Count 77.000000000
# 14 F F Percentage 0.574626866
# 15 M M Count 55.000000000
# 16 M M Percentage 0.410447761
# 17 U U Count 2.000000000
# 18 U U Percentage 0.014925373
# 19 UNDIFFERENTIATED UNDIFFERENTIATED Count 0.000000000
# 20 UNDIFFERENTIATED UNDIFFERENTIATED Percentage 0.000000000More Complex Outputs
Let’s add hierarchical row and column splits:
lyt <- basic_table() %>%
split_rows_by("STRATA2") %>%
summarize_row_groups() %>%
split_cols_by("ARM") %>%
split_cols_by("STRATA1") %>%
analyze(c("AGE", "SEX"))
tbl <- build_table(lyt, ex_adsl)
as_result_df(tbl, make_ard = TRUE)
# group1 group1_level group2 group2_level group3 group3_level variable
# 1 STRATA2 S1 ARM A: Drug X STRATA1 A STRATA2
# 2 STRATA2 S1 ARM A: Drug X STRATA1 A STRATA2
# 3 STRATA2 S1 ARM A: Drug X STRATA1 A AGE
# 4 STRATA2 S1 ARM A: Drug X STRATA1 A SEX
# 5 STRATA2 S1 ARM A: Drug X STRATA1 A SEX
# 6 STRATA2 S1 ARM A: Drug X STRATA1 A SEX
# 7 STRATA2 S1 ARM A: Drug X STRATA1 A SEX
# 8 STRATA2 S2 ARM A: Drug X STRATA1 A STRATA2
# 9 STRATA2 S2 ARM A: Drug X STRATA1 A STRATA2
# 10 STRATA2 S2 ARM A: Drug X STRATA1 A AGE
# 11 STRATA2 S2 ARM A: Drug X STRATA1 A SEX
# 12 STRATA2 S2 ARM A: Drug X STRATA1 A SEX
# 13 STRATA2 S2 ARM A: Drug X STRATA1 A SEX
# 14 STRATA2 S2 ARM A: Drug X STRATA1 A SEX
# 15 STRATA2 S1 ARM A: Drug X STRATA1 B STRATA2
# 16 STRATA2 S1 ARM A: Drug X STRATA1 B STRATA2
# 17 STRATA2 S1 ARM A: Drug X STRATA1 B AGE
# 18 STRATA2 S1 ARM A: Drug X STRATA1 B SEX
# 19 STRATA2 S1 ARM A: Drug X STRATA1 B SEX
# 20 STRATA2 S1 ARM A: Drug X STRATA1 B SEX
# 21 STRATA2 S1 ARM A: Drug X STRATA1 B SEX
# 22 STRATA2 S2 ARM A: Drug X STRATA1 B STRATA2
# 23 STRATA2 S2 ARM A: Drug X STRATA1 B STRATA2
# 24 STRATA2 S2 ARM A: Drug X STRATA1 B AGE
# 25 STRATA2 S2 ARM A: Drug X STRATA1 B SEX
# 26 STRATA2 S2 ARM A: Drug X STRATA1 B SEX
# 27 STRATA2 S2 ARM A: Drug X STRATA1 B SEX
# 28 STRATA2 S2 ARM A: Drug X STRATA1 B SEX
# 29 STRATA2 S1 ARM A: Drug X STRATA1 C STRATA2
# 30 STRATA2 S1 ARM A: Drug X STRATA1 C STRATA2
# 31 STRATA2 S1 ARM A: Drug X STRATA1 C AGE
# 32 STRATA2 S1 ARM A: Drug X STRATA1 C SEX
# 33 STRATA2 S1 ARM A: Drug X STRATA1 C SEX
# 34 STRATA2 S1 ARM A: Drug X STRATA1 C SEX
# 35 STRATA2 S1 ARM A: Drug X STRATA1 C SEX
# 36 STRATA2 S2 ARM A: Drug X STRATA1 C STRATA2
# 37 STRATA2 S2 ARM A: Drug X STRATA1 C STRATA2
# 38 STRATA2 S2 ARM A: Drug X STRATA1 C AGE
# 39 STRATA2 S2 ARM A: Drug X STRATA1 C SEX
# 40 STRATA2 S2 ARM A: Drug X STRATA1 C SEX
# 41 STRATA2 S2 ARM A: Drug X STRATA1 C SEX
# 42 STRATA2 S2 ARM A: Drug X STRATA1 C SEX
# 43 STRATA2 S1 ARM B: Placebo STRATA1 A STRATA2
# 44 STRATA2 S1 ARM B: Placebo STRATA1 A STRATA2
# 45 STRATA2 S1 ARM B: Placebo STRATA1 A AGE
# 46 STRATA2 S1 ARM B: Placebo STRATA1 A SEX
# 47 STRATA2 S1 ARM B: Placebo STRATA1 A SEX
# 48 STRATA2 S1 ARM B: Placebo STRATA1 A SEX
# 49 STRATA2 S1 ARM B: Placebo STRATA1 A SEX
# 50 STRATA2 S2 ARM B: Placebo STRATA1 A STRATA2
# 51 STRATA2 S2 ARM B: Placebo STRATA1 A STRATA2
# 52 STRATA2 S2 ARM B: Placebo STRATA1 A AGE
# 53 STRATA2 S2 ARM B: Placebo STRATA1 A SEX
# 54 STRATA2 S2 ARM B: Placebo STRATA1 A SEX
# 55 STRATA2 S2 ARM B: Placebo STRATA1 A SEX
# 56 STRATA2 S2 ARM B: Placebo STRATA1 A SEX
# 57 STRATA2 S1 ARM B: Placebo STRATA1 B STRATA2
# 58 STRATA2 S1 ARM B: Placebo STRATA1 B STRATA2
# 59 STRATA2 S1 ARM B: Placebo STRATA1 B AGE
# 60 STRATA2 S1 ARM B: Placebo STRATA1 B SEX
# 61 STRATA2 S1 ARM B: Placebo STRATA1 B SEX
# 62 STRATA2 S1 ARM B: Placebo STRATA1 B SEX
# 63 STRATA2 S1 ARM B: Placebo STRATA1 B SEX
# 64 STRATA2 S2 ARM B: Placebo STRATA1 B STRATA2
# 65 STRATA2 S2 ARM B: Placebo STRATA1 B STRATA2
# 66 STRATA2 S2 ARM B: Placebo STRATA1 B AGE
# 67 STRATA2 S2 ARM B: Placebo STRATA1 B SEX
# 68 STRATA2 S2 ARM B: Placebo STRATA1 B SEX
# 69 STRATA2 S2 ARM B: Placebo STRATA1 B SEX
# 70 STRATA2 S2 ARM B: Placebo STRATA1 B SEX
# 71 STRATA2 S1 ARM B: Placebo STRATA1 C STRATA2
# 72 STRATA2 S1 ARM B: Placebo STRATA1 C STRATA2
# 73 STRATA2 S1 ARM B: Placebo STRATA1 C AGE
# 74 STRATA2 S1 ARM B: Placebo STRATA1 C SEX
# 75 STRATA2 S1 ARM B: Placebo STRATA1 C SEX
# 76 STRATA2 S1 ARM B: Placebo STRATA1 C SEX
# 77 STRATA2 S1 ARM B: Placebo STRATA1 C SEX
# 78 STRATA2 S2 ARM B: Placebo STRATA1 C STRATA2
# 79 STRATA2 S2 ARM B: Placebo STRATA1 C STRATA2
# 80 STRATA2 S2 ARM B: Placebo STRATA1 C AGE
# 81 STRATA2 S2 ARM B: Placebo STRATA1 C SEX
# 82 STRATA2 S2 ARM B: Placebo STRATA1 C SEX
# 83 STRATA2 S2 ARM B: Placebo STRATA1 C SEX
# 84 STRATA2 S2 ARM B: Placebo STRATA1 C SEX
# 85 STRATA2 S1 ARM C: Combination STRATA1 A STRATA2
# 86 STRATA2 S1 ARM C: Combination STRATA1 A STRATA2
# 87 STRATA2 S1 ARM C: Combination STRATA1 A AGE
# 88 STRATA2 S1 ARM C: Combination STRATA1 A SEX
# 89 STRATA2 S1 ARM C: Combination STRATA1 A SEX
# 90 STRATA2 S1 ARM C: Combination STRATA1 A SEX
# 91 STRATA2 S1 ARM C: Combination STRATA1 A SEX
# 92 STRATA2 S2 ARM C: Combination STRATA1 A STRATA2
# 93 STRATA2 S2 ARM C: Combination STRATA1 A STRATA2
# 94 STRATA2 S2 ARM C: Combination STRATA1 A AGE
# 95 STRATA2 S2 ARM C: Combination STRATA1 A SEX
# 96 STRATA2 S2 ARM C: Combination STRATA1 A SEX
# 97 STRATA2 S2 ARM C: Combination STRATA1 A SEX
# 98 STRATA2 S2 ARM C: Combination STRATA1 A SEX
# 99 STRATA2 S1 ARM C: Combination STRATA1 B STRATA2
# 100 STRATA2 S1 ARM C: Combination STRATA1 B STRATA2
# 101 STRATA2 S1 ARM C: Combination STRATA1 B AGE
# 102 STRATA2 S1 ARM C: Combination STRATA1 B SEX
# 103 STRATA2 S1 ARM C: Combination STRATA1 B SEX
# 104 STRATA2 S1 ARM C: Combination STRATA1 B SEX
# 105 STRATA2 S1 ARM C: Combination STRATA1 B SEX
# 106 STRATA2 S2 ARM C: Combination STRATA1 B STRATA2
# 107 STRATA2 S2 ARM C: Combination STRATA1 B STRATA2
# 108 STRATA2 S2 ARM C: Combination STRATA1 B AGE
# 109 STRATA2 S2 ARM C: Combination STRATA1 B SEX
# 110 STRATA2 S2 ARM C: Combination STRATA1 B SEX
# 111 STRATA2 S2 ARM C: Combination STRATA1 B SEX
# 112 STRATA2 S2 ARM C: Combination STRATA1 B SEX
# 113 STRATA2 S1 ARM C: Combination STRATA1 C STRATA2
# 114 STRATA2 S1 ARM C: Combination STRATA1 C STRATA2
# 115 STRATA2 S1 ARM C: Combination STRATA1 C AGE
# 116 STRATA2 S1 ARM C: Combination STRATA1 C SEX
# 117 STRATA2 S1 ARM C: Combination STRATA1 C SEX
# 118 STRATA2 S1 ARM C: Combination STRATA1 C SEX
# 119 STRATA2 S1 ARM C: Combination STRATA1 C SEX
# 120 STRATA2 S2 ARM C: Combination STRATA1 C STRATA2
# 121 STRATA2 S2 ARM C: Combination STRATA1 C STRATA2
# 122 STRATA2 S2 ARM C: Combination STRATA1 C AGE
# 123 STRATA2 S2 ARM C: Combination STRATA1 C SEX
# 124 STRATA2 S2 ARM C: Combination STRATA1 C SEX
# 125 STRATA2 S2 ARM C: Combination STRATA1 C SEX
# 126 STRATA2 S2 ARM C: Combination STRATA1 C SEX
# variable_level variable_label stat_name stat
# 1 S1 S1 n 18.0000000
# 2 S1 S1 p 0.4736842
# 3 Mean Mean mean 31.6111111
# 4 F F n 12.0000000
# 5 M M n 5.0000000
# 6 U U n 1.0000000
# 7 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000
# 8 S2 S2 n 20.0000000
# 9 S2 S2 p 0.5263158
# 10 Mean Mean mean 34.4000000
# 11 F F n 9.0000000
# 12 M M n 11.0000000
# 13 U U n 0.0000000
# 14 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000
# 15 S1 S1 n 28.0000000
# 16 S1 S1 p 0.5957447
# 17 Mean Mean mean 34.5714286
# 18 F F n 14.0000000
# 19 M M n 13.0000000
# 20 U U n 1.0000000
# 21 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000
# 22 S2 S2 n 19.0000000
# 23 S2 S2 p 0.4042553
# 24 Mean Mean mean 32.7894737
# 25 F F n 11.0000000
# 26 M M n 8.0000000
# 27 U U n 0.0000000
# 28 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000
# 29 S1 S1 n 27.0000000
# 30 S1 S1 p 0.5510204
# 31 Mean Mean mean 35.2592593
# 32 F F n 17.0000000
# 33 M M n 8.0000000
# 34 U U n 1.0000000
# 35 UNDIFFERENTIATED UNDIFFERENTIATED n 1.0000000
# 36 S2 S2 n 22.0000000
# 37 S2 S2 p 0.4489796
# 38 Mean Mean mean 32.9545455
# 39 F F n 16.0000000
# 40 M M n 6.0000000
# 41 U U n 0.0000000
# 42 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000
# 43 S1 S1 n 22.0000000
# 44 S1 S1 p 0.5000000
# 45 Mean Mean mean 36.6818182
# 46 F F n 11.0000000
# 47 M M n 10.0000000
# 48 U U n 1.0000000
# 49 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000
# 50 S2 S2 n 22.0000000
# 51 S2 S2 p 0.5000000
# 52 Mean Mean mean 33.5454545
# 53 F F n 13.0000000
# 54 M M n 9.0000000
# 55 U U n 0.0000000
# 56 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000
# 57 S1 S1 n 19.0000000
# 58 S1 S1 p 0.4222222
# 59 Mean Mean mean 37.6842105
# 60 F F n 12.0000000
# 61 M M n 7.0000000
# 62 U U n 0.0000000
# 63 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000
# 64 S2 S2 n 26.0000000
# 65 S2 S2 p 0.5777778
# 66 Mean Mean mean 34.7692308
# 67 F F n 15.0000000
# 68 M M n 10.0000000
# 69 U U n 1.0000000
# 70 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000
# 71 S1 S1 n 26.0000000
# 72 S1 S1 p 0.5777778
# 73 Mean Mean mean 35.3846154
# 74 F F n 13.0000000
# 75 M M n 13.0000000
# 76 U U n 0.0000000
# 77 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000
# 78 S2 S2 n 19.0000000
# 79 S2 S2 p 0.4222222
# 80 Mean Mean mean 34.8947368
# 81 F F n 13.0000000
# 82 M M n 6.0000000
# 83 U U n 0.0000000
# 84 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000
# 85 S1 S1 n 14.0000000
# 86 S1 S1 p 0.3500000
# 87 Mean Mean mean 34.0000000
# 88 F F n 7.0000000
# 89 M M n 6.0000000
# 90 U U n 1.0000000
# 91 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000
# 92 S2 S2 n 26.0000000
# 93 S2 S2 p 0.6500000
# 94 Mean Mean mean 34.3461538
# 95 F F n 11.0000000
# 96 M M n 14.0000000
# 97 U U n 0.0000000
# 98 UNDIFFERENTIATED UNDIFFERENTIATED n 1.0000000
# 99 S1 S1 n 18.0000000
# 100 S1 S1 p 0.4186047
# 101 Mean Mean mean 35.8333333
# 102 F F n 9.0000000
# 103 M M n 9.0000000
# 104 U U n 0.0000000
# 105 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000
# 106 S2 S2 n 25.0000000
# 107 S2 S2 p 0.5813953
# 108 Mean Mean mean 36.6800000
# 109 F F n 12.0000000
# 110 M M n 12.0000000
# 111 U U n 1.0000000
# 112 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000
# 113 S1 S1 n 24.0000000
# 114 S1 S1 p 0.4897959
# 115 Mean Mean mean 36.5833333
# 116 F F n 14.0000000
# 117 M M n 8.0000000
# 118 U U n 1.0000000
# 119 UNDIFFERENTIATED UNDIFFERENTIATED n 1.0000000
# 120 S2 S2 n 25.0000000
# 121 S2 S2 p 0.5102041
# 122 Mean Mean mean 34.7200000
# 123 F F n 13.0000000
# 124 M M n 11.0000000
# 125 U U n 1.0000000
# 126 UNDIFFERENTIATED UNDIFFERENTIATED n 0.0000000