Subgroup Analysis for Specific AE

library(metalite.ae)

Overview

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.

Analysis preparation

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.

meta <- meta_ae_example()

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.

outdata$group
#> [1] "Placebo"   "Low Dose"  "High Dose"
outdata$subgroup
#> [1] "f" "m"

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.

head(outdata$out_all$Total$diff)
#>         diff_2     diff_3
#> 1           NA         NA
#> 2    35.741971  32.170543
#> 3   -35.741971 -32.170543
#> 4           NA         NA
#> 122   1.356589  -2.214839
#> 25   -1.162791   1.218162

Format output

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
#> 135 with one or more drug-related adverse events   28  (52.8)   41  (82.0)   32
#> 134          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>
#> 135  (80.0)   16  (48.5)   32  (94.1)   38  (86.4)       44      (51.2)
#> 134  (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
#> 135       73      (86.9)       70      (83.3)   100
#> 134       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
#> 135 with one or more drug-related adverse events   28  (52.8)   41  (82.0)   32
#> 134          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>
#> 135  (80.0)   16  (48.5)   32  (94.1)   38  (86.4)       44      (51.2)
#> 134  (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
#> 135       73      (86.9)       70      (83.3)   100
#> 134       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
#> 135 with one or more drug-related adverse events   28  (52.8)   41  (82.0)   32
#> 134          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>
#> 135  (80.0)    29.2    27.2   16  (48.5)   32  (94.1)   38  (86.4)    45.6
#> 134  (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>
#> 135    37.9       44      (51.2)       73      (86.9)       70      (83.3)
#> 134   -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
#> 135        35.7        32.2   100
#> 134       -35.7       -32.2   200
#> 1          <NA>        <NA>   900
#> 33          1.4        -2.2  1000
#> 22         -1.2         1.2  1021

Mock data preparation

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
#> 135 with one or more drug-related adverse events   28  (52.8)   41  (82.0)   32
#> 134          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>
#> 135  (80.0)   16  (48.5)   32  (94.1)   38  (86.4)       44      (51.2)
#> 134  (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
#> 135       73      (86.9)       70      (83.3)   100
#> 134       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

RTF tables

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]",
    analysis = "ae_specific", # Provide analysis type defined in meta$analysis
    path_outtable = "rtf/ae0specific0sub0gender1.rtf"
  )
#> The output is saved in/home/runner/work/metalite.ae/metalite.ae/vignettes/rtf/ae0specific0sub0gender1.rtf