The AE specific subgroup analysis aims to provide tables to summarize details of adverse events by subgroup. The development of AE specific subgroup analysis involves exported functions:
prepare_ae_specific_subgroup()
: prepare analysis raw
datasets.format_ae_specific_subgroup()
: prepare analysis (mock)
outdata with proper format.tlf_ae_specific_subgroup()
: transfer (mock) output
dataset to RTF table.The prepare_ae_specific_subgroup()
function is designed
to be used for multiple purposes. The input of the function is a
meta
object created by the metalite package.
The output of the function is an outdata
object
containing a list of analysis raw datasets. Key arguments are
subgroup_var
, subgroup_header
, and
display_subgroup_total
.
outdata <- prepare_ae_specific_subgroup(
meta,
population = "apat",
observation = "wk12",
parameter = "rel",
subgroup_var = "SEX",
subgroup_header = c("TRTA", "SEX"),
display_subgroup_total = TRUE
)
outdata
#> $components
#> [1] "soc" "par"
#>
#> $group
#> [1] "Placebo" "Low Dose" "High Dose"
#>
#> $subgroup
#> [1] "f" "m"
#>
#> $display_subgroup_total
#> [1] TRUE
#>
#> $meta
#> ADaM metadata:
#> .$data_population Population data with 254 subjects
#> .$data_observation Observation data with 1191 records
#> .$plan Analysis plan with 18 plans
#>
#>
#> Analysis population type:
#> name id group var subset label
#> 1 'apat' 'USUBJID' 'TRTA' SAFFL == 'Y' 'All Participants as Treated'
#>
#>
#> Analysis observation type:
#> name id group var subset label
#> 1 'wk12' 'USUBJID' 'TRTA' SAFFL == 'Y' 'Weeks 0 to 12'
#> 2 'wk24' 'USUBJID' 'TRTA' AOCC01FL == 'Y' 'Weeks 0 to 24'
#>
#>
#> Analysis parameter type:
#> name label
#> 1 'rel' 'drug-related adverse events'
#> 2 'aeosi' 'adverse events of special interest'
#> 3 'any' 'any adverse events'
#> 4 'ser' 'serious adverse events'
#> subset
#> 1 AEREL %in% c('POSSIBLE', 'PROBABLE')
#> 2 AEOSI == 'Y'
#> 3
#> 4 AESER == 'Y'
#>
#>
#> Analysis function:
#> name label
#> 1 'ae_summary' 'Table: adverse event summary'
#> 2 'ae_listing' 'Listing: adverse event'
#> 3 'ae_exp_adj' 'Exposure Adjusted Incident Rate'
#> 4 'ae_specific' 'Table: specific adverse event'
#>
#>
#> $population
#> [1] "apat"
#>
#> $observation
#> [1] "wk12"
#>
#> $parameter
#> [1] "rel"
#>
#> $out_all
#> $out_all$F
#> List of 15
#> $ meta :List of 7
#> $ population : chr "apat"
#> $ observation : chr "wk12"
#> $ parameter : chr "rel"
#> $ n :'data.frame': 138 obs. of 4 variables:
#> $ order : num [1:138] 1 100 200 900 1000 ...
#> $ group : chr [1:4] "Placebo" "Low Dose" "High Dose" "Total"
#> $ reference_group: num 1
#> $ prop :'data.frame': 138 obs. of 4 variables:
#> $ diff :'data.frame': 138 obs. of 2 variables:
#> $ n_pop :'data.frame': 1 obs. of 4 variables:
#> $ name : chr [1:138] "Participants in population" "with one or more drug-related adverse events" "with no drug-related adverse events" "" ...
#> $ soc_name : chr [1:138] NA NA NA NA ...
#> $ components : chr [1:2] "soc" "par"
#> $ prepare_call : language FUN(meta = X[[i]], population = ..1, observation = ..2, parameter = ..3, components = ..4)
#>
#> $out_all$M
#> List of 15
#> $ meta :List of 7
#> $ population : chr "apat"
#> $ observation : chr "wk12"
#> $ parameter : chr "rel"
#> $ n :'data.frame': 138 obs. of 4 variables:
#> $ order : num [1:138] 1 100 200 900 1000 ...
#> $ group : chr [1:4] "Placebo" "Low Dose" "High Dose" "Total"
#> $ reference_group: num 1
#> $ prop :'data.frame': 138 obs. of 4 variables:
#> $ diff :'data.frame': 138 obs. of 2 variables:
#> $ n_pop :'data.frame': 1 obs. of 4 variables:
#> $ name : chr [1:138] "Participants in population" "with one or more drug-related adverse events" "with no drug-related adverse events" "" ...
#> $ soc_name : chr [1:138] NA NA NA NA ...
#> $ components : chr [1:2] "soc" "par"
#> $ prepare_call : language FUN(meta = X[[i]], population = ..1, observation = ..2, parameter = ..3, components = ..4)
#>
#> $out_all$Total
#> List of 15
#> $ meta :List of 7
#> $ population : chr "apat"
#> $ observation : chr "wk12"
#> $ parameter : chr "rel"
#> $ n :'data.frame': 138 obs. of 4 variables:
#> $ order : num [1:138] 1 100 200 900 1000 ...
#> $ group : chr [1:4] "Placebo" "Low Dose" "High Dose" "Total"
#> $ reference_group: num 1
#> $ prop :'data.frame': 138 obs. of 4 variables:
#> $ diff :'data.frame': 138 obs. of 2 variables:
#> $ n_pop :'data.frame': 1 obs. of 4 variables:
#> $ name : chr [1:138] "Participants in population" "with one or more drug-related adverse events" "with no drug-related adverse events" "" ...
#> $ soc_name : chr [1:138] NA NA NA NA ...
#> $ components : chr [1:2] "soc" "par"
#> $ prepare_call : language prepare_ae_specific(meta = meta, population = population, observation = observation, parameter = parameter, | __truncated__
The output dataset contains commonly used statistics within each
subgroup_var
.
outdata$out_all$F
#> List of 15
#> $ meta :List of 7
#> $ population : chr "apat"
#> $ observation : chr "wk12"
#> $ parameter : chr "rel"
#> $ n :'data.frame': 138 obs. of 4 variables:
#> $ order : num [1:138] 1 100 200 900 1000 ...
#> $ group : chr [1:4] "Placebo" "Low Dose" "High Dose" "Total"
#> $ reference_group: num 1
#> $ prop :'data.frame': 138 obs. of 4 variables:
#> $ diff :'data.frame': 138 obs. of 2 variables:
#> $ n_pop :'data.frame': 1 obs. of 4 variables:
#> $ name : chr [1:138] "Participants in population" "with one or more drug-related adverse events" "with no drug-related adverse events" "" ...
#> $ soc_name : chr [1:138] NA NA NA NA ...
#> $ components : chr [1:2] "soc" "par"
#> $ prepare_call : language FUN(meta = X[[i]], population = ..1, observation = ..2, parameter = ..3, components = ..4)
outdata$out_all$M
#> List of 15
#> $ meta :List of 7
#> $ population : chr "apat"
#> $ observation : chr "wk12"
#> $ parameter : chr "rel"
#> $ n :'data.frame': 138 obs. of 4 variables:
#> $ order : num [1:138] 1 100 200 900 1000 ...
#> $ group : chr [1:4] "Placebo" "Low Dose" "High Dose" "Total"
#> $ reference_group: num 1
#> $ prop :'data.frame': 138 obs. of 4 variables:
#> $ diff :'data.frame': 138 obs. of 2 variables:
#> $ n_pop :'data.frame': 1 obs. of 4 variables:
#> $ name : chr [1:138] "Participants in population" "with one or more drug-related adverse events" "with no drug-related adverse events" "" ...
#> $ soc_name : chr [1:138] NA NA NA NA ...
#> $ components : chr [1:2] "soc" "par"
#> $ prepare_call : language FUN(meta = X[[i]], population = ..1, observation = ..2, parameter = ..3, components = ..4)
outdata$out_all$Total
#> List of 15
#> $ meta :List of 7
#> $ population : chr "apat"
#> $ observation : chr "wk12"
#> $ parameter : chr "rel"
#> $ n :'data.frame': 138 obs. of 4 variables:
#> $ order : num [1:138] 1 100 200 900 1000 ...
#> $ group : chr [1:4] "Placebo" "Low Dose" "High Dose" "Total"
#> $ reference_group: num 1
#> $ prop :'data.frame': 138 obs. of 4 variables:
#> $ diff :'data.frame': 138 obs. of 2 variables:
#> $ n_pop :'data.frame': 1 obs. of 4 variables:
#> $ name : chr [1:138] "Participants in population" "with one or more drug-related adverse events" "with no drug-related adverse events" "" ...
#> $ soc_name : chr [1:138] NA NA NA NA ...
#> $ components : chr [1:2] "soc" "par"
#> $ prepare_call : language prepare_ae_specific(meta = meta, population = population, observation = observation, parameter = parameter, | __truncated__
The variable is indexed by the order of outdata$group
and outdata$subgroup
within each
subgroup_var
.
The row is indexed by the order of name
within each
subgroup_var
analysis output.
head(data.frame(outdata$out_all$Total$order, outdata$out_all$Total$name))
#> outdata.out_all.Total.order outdata.out_all.Total.name
#> 1 1 Participants in population
#> 2 100 with one or more drug-related adverse events
#> 3 200 with no drug-related adverse events
#> 4 900
#> 5 1000 Cardiac disorders
#> 6 1021 Atrial fibrillation
n_pop
: participants in population within each
subgroup_var
.outdata$out_all$F$n_pop
#> n_1 n_2 n_3 n_4
#> 1 53 50 40 143
outdata$out_all$M$n_pop
#> n_1 n_2 n_3 n_4
#> 1 33 34 44 111
outdata$out_all$Total$n_pop
#> n_1 n_2 n_3 n_4
#> 1 86 84 84 254
n
: number of subjects with AE within each
subgroup_var
.head(outdata$out_all$F$n)
#> n_1 n_2 n_3 n_4
#> 1 53 50 40 143
#> 2 28 41 32 101
#> 3 25 9 8 42
#> 4 NA NA NA NA
#> 122 4 4 4 12
#> 25 1 0 2 3
head(outdata$out_all$M$n)
#> n_1 n_2 n_3 n_4
#> 1 33 34 44 111
#> 2 16 32 38 86
#> 3 17 2 6 25
#> 4 NA NA NA NA
#> 122 2 3 0 5
#> 26 0 1 0 1
head(outdata$out_all$Total$n)
#> n_1 n_2 n_3 n_4
#> 1 86 84 84 254
#> 2 44 73 70 187
#> 3 42 11 14 67
#> 4 NA NA NA NA
#> 122 6 7 4 17
#> 25 1 0 2 3
prop
: proportion of subjects with AE within each
subgroup_var
.head(outdata$out_all$F$prop)
#> prop_1 prop_2 prop_3 prop_4
#> 1 NA NA NA NA
#> 2 52.830189 82 80 70.629371
#> 3 47.169811 18 20 29.370629
#> 4 NA NA NA NA
#> 122 7.547170 8 10 8.391608
#> 25 1.886792 0 5 2.097902
head(outdata$out_all$M$prop)
#> prop_1 prop_2 prop_3 prop_4
#> 1 NA NA NA NA
#> 2 48.484848 94.117647 86.36364 77.4774775
#> 3 51.515152 5.882353 13.63636 22.5225225
#> 4 NA NA NA NA
#> 122 6.060606 8.823529 0.00000 4.5045045
#> 26 0.000000 2.941176 0.00000 0.9009009
head(outdata$out_all$Total$prop)
#> prop_1 prop_2 prop_3 prop_4
#> 1 NA NA NA NA
#> 2 51.162791 86.904762 83.333333 73.622047
#> 3 48.837209 13.095238 16.666667 26.377953
#> 4 NA NA NA NA
#> 122 6.976744 8.333333 4.761905 6.692913
#> 25 1.162791 0.000000 2.380952 1.181102
diff
: risk difference compared with the
reference_group
within each subgroup_var
.After we have the raw analysis results, we can use
format_ae_specific_subgroup()
to prepare the outdata to
create RTF tables.
tbl <- outdata |> format_ae_specific_subgroup()
head(tbl$tbl)
#> name Fn_1 Fprop_1 Fn_2 Fprop_2 Fn_3
#> 96 Participants in population 53 <NA> 50 <NA> 40
#> 138 with one or more drug-related adverse events 28 (52.8) 41 (82.0) 32
#> 137 with no drug-related adverse events 25 (47.2) 9 (18.0) 8
#> 1 NA <NA> NA <NA> NA
#> 33 Cardiac disorders 4 (7.5) 4 (8.0) 4
#> 22 Atrial fibrillation 1 (1.9) 0 (0.0) 2
#> Fprop_3 Mn_1 Mprop_1 Mn_2 Mprop_2 Mn_3 Mprop_3 Totaln_1 Totalprop_1
#> 96 <NA> 33 <NA> 34 <NA> 44 <NA> 86 <NA>
#> 138 (80.0) 16 (48.5) 32 (94.1) 38 (86.4) 44 (51.2)
#> 137 (20.0) 17 (51.5) 2 (5.9) 6 (13.6) 42 (48.8)
#> 1 <NA> NA <NA> NA <NA> NA <NA> NA <NA>
#> 33 (10.0) 2 (6.1) 3 (8.8) 0 (0.0) 6 (7.0)
#> 22 (5.0) 0 (0.0) 0 (0.0) 0 (0.0) 1 (1.2)
#> Totaln_2 Totalprop_2 Totaln_3 Totalprop_3 order
#> 96 84 <NA> 84 <NA> 1
#> 138 73 (86.9) 70 (83.3) 100
#> 137 11 (13.1) 14 (16.7) 200
#> 1 NA <NA> NA <NA> 900
#> 33 7 (8.3) 4 (4.8) 1000
#> 22 0 (0.0) 2 (2.4) 1021
We can hide the total column:
tbl <- outdata |> format_ae_specific_subgroup(display = c("n", "prop"))
head(tbl$tbl)
#> name Fn_1 Fprop_1 Fn_2 Fprop_2 Fn_3
#> 96 Participants in population 53 <NA> 50 <NA> 40
#> 138 with one or more drug-related adverse events 28 (52.8) 41 (82.0) 32
#> 137 with no drug-related adverse events 25 (47.2) 9 (18.0) 8
#> 1 NA <NA> NA <NA> NA
#> 33 Cardiac disorders 4 (7.5) 4 (8.0) 4
#> 22 Atrial fibrillation 1 (1.9) 0 (0.0) 2
#> Fprop_3 Mn_1 Mprop_1 Mn_2 Mprop_2 Mn_3 Mprop_3 Totaln_1 Totalprop_1
#> 96 <NA> 33 <NA> 34 <NA> 44 <NA> 86 <NA>
#> 138 (80.0) 16 (48.5) 32 (94.1) 38 (86.4) 44 (51.2)
#> 137 (20.0) 17 (51.5) 2 (5.9) 6 (13.6) 42 (48.8)
#> 1 <NA> NA <NA> NA <NA> NA <NA> NA <NA>
#> 33 (10.0) 2 (6.1) 3 (8.8) 0 (0.0) 6 (7.0)
#> 22 (5.0) 0 (0.0) 0 (0.0) 0 (0.0) 1 (1.2)
#> Totaln_2 Totalprop_2 Totaln_3 Totalprop_3 order
#> 96 84 <NA> 84 <NA> 1
#> 138 73 (86.9) 70 (83.3) 100
#> 137 11 (13.1) 14 (16.7) 200
#> 1 NA <NA> NA <NA> 900
#> 33 7 (8.3) 4 (4.8) 1000
#> 22 0 (0.0) 2 (2.4) 1021
Adding risk difference:
tbl <- outdata |> format_ae_specific_subgroup(display = c("n", "prop", "diff"))
head(tbl$tbl)
#> name Fn_1 Fprop_1 Fn_2 Fprop_2 Fn_3
#> 96 Participants in population 53 <NA> 50 <NA> 40
#> 138 with one or more drug-related adverse events 28 (52.8) 41 (82.0) 32
#> 137 with no drug-related adverse events 25 (47.2) 9 (18.0) 8
#> 1 NA <NA> NA <NA> NA
#> 33 Cardiac disorders 4 (7.5) 4 (8.0) 4
#> 22 Atrial fibrillation 1 (1.9) 0 (0.0) 2
#> Fprop_3 Fdiff_2 Fdiff_3 Mn_1 Mprop_1 Mn_2 Mprop_2 Mn_3 Mprop_3 Mdiff_2
#> 96 <NA> NA NA 33 <NA> 34 <NA> 44 <NA> NA
#> 138 (80.0) 29.2 27.2 16 (48.5) 32 (94.1) 38 (86.4) 45.6
#> 137 (20.0) -29.2 -27.2 17 (51.5) 2 (5.9) 6 (13.6) -45.6
#> 1 <NA> NA NA NA <NA> NA <NA> NA <NA> NA
#> 33 (10.0) 0.5 2.5 2 (6.1) 3 (8.8) 0 (0.0) 2.8
#> 22 (5.0) -1.9 3.1 0 (0.0) 0 (0.0) 0 (0.0) 0.0
#> Mdiff_3 Totaln_1 Totalprop_1 Totaln_2 Totalprop_2 Totaln_3 Totalprop_3
#> 96 NA 86 <NA> 84 <NA> 84 <NA>
#> 138 37.9 44 (51.2) 73 (86.9) 70 (83.3)
#> 137 -37.9 42 (48.8) 11 (13.1) 14 (16.7)
#> 1 NA NA <NA> NA <NA> NA <NA>
#> 33 -6.1 6 (7.0) 7 (8.3) 4 (4.8)
#> 22 0.0 1 (1.2) 0 (0.0) 2 (2.4)
#> Totaldiff_2 Totaldiff_3 order
#> 96 NA NA 1
#> 138 35.7 32.2 100
#> 137 -35.7 -32.2 200
#> 1 NA NA 900
#> 33 1.4 -2.2 1000
#> 22 -1.2 1.2 1021
We can also use format_ae_specific_subgroup()
to create
mock output data.
The purpose of the mock
argument is not to create a
comprehensive mock table template, but a handy way to help users create
a mock table that mimics the exact output layout.
Additional work is required to develop a flexible mock table generation tool (for example, a dedicated mock table generation package).
tbl <- outdata |> format_ae_specific_subgroup(mock = FALSE)
head(tbl$tbl)
#> name Fn_1 Fprop_1 Fn_2 Fprop_2 Fn_3
#> 96 Participants in population 53 <NA> 50 <NA> 40
#> 138 with one or more drug-related adverse events 28 (52.8) 41 (82.0) 32
#> 137 with no drug-related adverse events 25 (47.2) 9 (18.0) 8
#> 1 NA <NA> NA <NA> NA
#> 33 Cardiac disorders 4 (7.5) 4 (8.0) 4
#> 22 Atrial fibrillation 1 (1.9) 0 (0.0) 2
#> Fprop_3 Mn_1 Mprop_1 Mn_2 Mprop_2 Mn_3 Mprop_3 Totaln_1 Totalprop_1
#> 96 <NA> 33 <NA> 34 <NA> 44 <NA> 86 <NA>
#> 138 (80.0) 16 (48.5) 32 (94.1) 38 (86.4) 44 (51.2)
#> 137 (20.0) 17 (51.5) 2 (5.9) 6 (13.6) 42 (48.8)
#> 1 <NA> NA <NA> NA <NA> NA <NA> NA <NA>
#> 33 (10.0) 2 (6.1) 3 (8.8) 0 (0.0) 6 (7.0)
#> 22 (5.0) 0 (0.0) 0 (0.0) 0 (0.0) 1 (1.2)
#> Totaln_2 Totalprop_2 Totaln_3 Totalprop_3 order
#> 96 84 <NA> 84 <NA> 1
#> 138 73 (86.9) 70 (83.3) 100
#> 137 11 (13.1) 14 (16.7) 200
#> 1 NA <NA> NA <NA> 900
#> 33 7 (8.3) 4 (4.8) 1000
#> 22 0 (0.0) 2 (2.4) 1021
By using tlf_ae_specific_subgroup()
, we can transfer the
output from format_ae_specific_subgroup()
to an RTF or PDF
table.
outdata |>
format_ae_specific_subgroup() |>
tlf_ae_specific_subgroup(
meddra_version = "24.0",
source = "Source: [CDISCpilot: adam-adsl; adae]",
path_outtable = "rtf/ae0specific0sub0gender1.rtf"
)
#> The output is saved in/rtmp/Rtmp5KcMff/Rbuild3e664b2f7731/metalite.ae/vignettes/rtf/ae0specific0sub0gender1.rtf