AE Specification

library(metalite.ae)

Overview

The objective of this tutorial is to generate a production-ready AE specification analyses. It extends examples shown in the specific AE chapter of the R for Clinical Study Reports and Submission book.

The AE specification analysis entails the creation of tables that summarize details of different types of adverse events. To accomplish this using metalite.ae, three essential functions are required:

There are three optional functions to extend AE specification analysis.

An example output:

Example data

Within metalite.ae, we utilized the ADSL and ADAE datasets from the metalite package to create an illustrative dataset. The metadata structure remains consistent across all analysis examples within metalite.ae. Additional information can be accessed on the metalite package website.

meta <- meta_ae_example()
Click to show the output 
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'

Analysis preparation

The function prepare_ae_specific() is used to create a dataset for AE summary analysis by utilizing predefined keywords specified in the example data meta.

The resulting output of the function is an outdata object, which comprises a collection of raw datasets for analysis and reporting.

outdata <- prepare_ae_specific(
  meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel"
)
outdata
#> 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 = "apat", observation = "wk12",      parameter = "rel")

The resulting dataset contains frequently used statistics, with variables indexed according to the order specified in outdata$group.

outdata$group
#> [1] "Placebo"   "Low Dose"  "High Dose" "Total"

The row is indexed according to the order of outdata$name.

head(data.frame(outdata$order, outdata$name))
#>   outdata.order                                 outdata.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
outdata$n_pop
#>   n_1 n_2 n_3 n_4
#> 1  86  84  84 254
head(outdata$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
head(outdata$prop)
#>        prop_1    prop_2    prop_3    prop_4
#> 1          NA        NA        NA        NA
#> 2   51.162791 86.904762 83.333333 73.622047
#> 3   48.837209 13.095238 16.666667 26.377953
#> 4          NA        NA        NA        NA
#> 122  6.976744  8.333333  4.761905  6.692913
#> 25   1.162791  0.000000  2.380952  1.181102
head(outdata$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

Once the raw analysis results are obtained, the format_ae_specific() function can be employed to prepare the outdata, ensuring its compatibility with production-ready RTF tables.

tbl <- outdata |> format_ae_specific()
head(tbl$tbl)
#>                                             name n_1 prop_1 n_2 prop_2 n_3
#> 1                     Participants in population  86   <NA>  84   <NA>  84
#> 2   with one or more drug-related adverse events  44 (51.2)  73 (86.9)  70
#> 3            with no drug-related adverse events  42 (48.8)  11 (13.1)  14
#> 4                                                 NA   <NA>  NA   <NA>  NA
#> 122                            Cardiac disorders   6  (7.0)   7  (8.3)   4
#> 25                           Atrial fibrillation   1  (1.2)   0  (0.0)   2
#>     prop_3 n_4 prop_4
#> 1     <NA> 254   <NA>
#> 2   (83.3) 187 (73.6)
#> 3   (16.7)  67 (26.4)
#> 4     <NA>  NA   <NA>
#> 122  (4.8)  17  (6.7)
#> 25   (2.4)   3  (1.2)

Additional statistics

By using the display argument, we can choose specific statistics to include. For instance, we have the option to incorporate the risk difference.

tbl <- outdata |> format_ae_specific(display = c("n", "prop", "diff"))
head(tbl$tbl)
#>                                             name n_1 prop_1 n_2 prop_2 n_3
#> 1                     Participants in population  86   <NA>  84   <NA>  84
#> 2   with one or more drug-related adverse events  44 (51.2)  73 (86.9)  70
#> 3            with no drug-related adverse events  42 (48.8)  11 (13.1)  14
#> 4                                                 NA   <NA>  NA   <NA>  NA
#> 122                            Cardiac disorders   6  (7.0)   7  (8.3)   4
#> 25                           Atrial fibrillation   1  (1.2)   0  (0.0)   2
#>     prop_3 diff_2 diff_3
#> 1     <NA>   <NA>   <NA>
#> 2   (83.3)   35.7   32.2
#> 3   (16.7)  -35.7  -32.2
#> 4     <NA>   <NA>   <NA>
#> 122  (4.8)    1.4   -2.2
#> 25   (2.4)   -1.2    1.2

To perform advanced analysis, the extend_ae_specific_inference() function is utilized. For instance, we can incorporate a 95% confidence interval based on the Miettinen and Nurminen (M&N) method. Further information regarding the M&N method can be found in the rate compare vignette.

tbl <- outdata |>
  extend_ae_specific_inference() |>
  format_ae_specific(display = c("n", "prop", "diff", "diff_ci"))
head(tbl$tbl)
#>                                             name n_1 prop_1 n_2 prop_2 n_3
#> 1                     Participants in population  86   <NA>  84   <NA>  84
#> 2   with one or more drug-related adverse events  44 (51.2)  73 (86.9)  70
#> 3            with no drug-related adverse events  42 (48.8)  11 (13.1)  14
#> 4                                                 NA   <NA>  NA   <NA>  NA
#> 122                            Cardiac disorders   6  (7.0)   7  (8.3)   4
#> 25                           Atrial fibrillation   1  (1.2)   0  (0.0)   2
#>     prop_3 diff_2           ci_2 diff_3           ci_3
#> 1     <NA>   <NA>   (-4.4,  0.0)   <NA>   (-4.4,  0.0)
#> 2   (83.3)   35.7   (22.4, 48.0)   32.2   (18.4, 44.8)
#> 3   (16.7)  -35.7 (-48.0, -22.4)  -32.2 (-44.8, -18.4)
#> 4     <NA>   <NA>           <NA>   <NA>           <NA>
#> 122  (4.8)    1.4   (-7.3, 10.2)   -2.2  (-10.3,  5.6)
#> 25   (2.4)   -1.2   (-6.3,  3.3)    1.2   (-4.2,  7.3)

We can use extend_ae_specific_duration() to add average duration of AE.

tbl <- outdata |>
  extend_ae_specific_duration(duration_var = "ADURN") |>
  format_ae_specific(display = c("n", "prop", "dur"))

head(tbl$tbl)
#>                                             name n_1 prop_1        dur_1 n_2
#> 1                     Participants in population  86   <NA>         <NA>  84
#> 2   with one or more drug-related adverse events  44 (51.2)  29.0 ( 3.5)  73
#> 3            with no drug-related adverse events  42 (48.8)         <NA>  11
#> 4                                                 NA   <NA>         <NA>  NA
#> 122                            Cardiac disorders   6  (7.0)  27.1 ( 5.9)   7
#> 25                           Atrial fibrillation   1  (1.2)          6.0   0
#>     prop_2        dur_2 n_3 prop_3        dur_3
#> 1     <NA>         <NA>  84   <NA>         <NA>
#> 2   (86.9)  27.2 ( 3.2)  70 (83.3)  30.6 ( 2.2)
#> 3   (13.1)         <NA>  14 (16.7)         <NA>
#> 4     <NA>         <NA>  NA   <NA>         <NA>
#> 122  (8.3)  16.1 ( 3.5)   4  (4.8)   1.5 ( 0.4)
#> 25   (0.0)         <NA>   2  (2.4)   1.7 ( 0.7)

We can use extend_ae_specific_events() to add number of AE and/or average of it per subject.

tbl <- outdata |>
  extend_ae_specific_events() |>
  format_ae_specific(display = c("n", "prop", "events_count", "events_avg"))

head(tbl$tbl)
#>                                             name n_1 prop_1  eventsavg_1
#> 1                     Participants in population  86   <NA>         <NA>
#> 2   with one or more drug-related adverse events  44 (51.2)   0.7 ( 0.1)
#> 3            with no drug-related adverse events  42 (48.8)         <NA>
#> 4                                                 NA   <NA>         <NA>
#> 122                            Cardiac disorders   6  (7.0)   2.3 ( 0.6)
#> 25                           Atrial fibrillation   1  (1.2)          1.0
#>     eventscount_1 n_2 prop_2  eventsavg_2 eventscount_2 n_3 prop_3  eventsavg_3
#> 1              NA  84   <NA>         <NA>            NA  84   <NA>         <NA>
#> 2             133  73 (86.9)   1.6 ( 0.2)           292  70 (83.3)   1.5 ( 0.2)
#> 3              NA  11 (13.1)         <NA>            NA  14 (16.7)         <NA>
#> 4              NA  NA   <NA>         <NA>            NA  NA   <NA>         <NA>
#> 122            14   7  (8.3)   1.9 ( 0.4)            13   4  (4.8)   1.2 ( 0.2)
#> 25              1   0  (0.0)         <NA>             0   2  (2.4)   1.5 ( 0.5)
#>     eventscount_3
#> 1              NA
#> 2             279
#> 3              NA
#> 4              NA
#> 122             5
#> 25              3

We can use filter_method and filter_criteria parameters to filter information based on the specified criteria:

tbl <- outdata |>
  extend_ae_specific_events() |>
  format_ae_specific(
    display = c("n", "prop", "events_count", "events_avg"),
    filter_method = "percent",
    filter_criteria = 6
  )

head(tbl$tbl)
#>                                             name n_1 prop_1  eventsavg_1
#> 1                     Participants in population  86   <NA>         <NA>
#> 2   with one or more drug-related adverse events  44 (51.2)   0.7 ( 0.1)
#> 3            with no drug-related adverse events  42 (48.8)         <NA>
#> 4                                                 NA   <NA>         <NA>
#> 122                            Cardiac disorders   6  (7.0)   2.3 ( 0.6)
#> 126                   Gastrointestinal disorders   4  (4.7)   1.8 ( 0.5)
#>     eventscount_1 n_2 prop_2  eventsavg_2 eventscount_2 n_3 prop_3  eventsavg_3
#> 1              NA  84   <NA>         <NA>            NA  84   <NA>         <NA>
#> 2             133  73 (86.9)   1.6 ( 0.2)           292  70 (83.3)   1.5 ( 0.2)
#> 3              NA  11 (13.1)         <NA>            NA  14 (16.7)         <NA>
#> 4              NA  NA   <NA>         <NA>            NA  NA   <NA>         <NA>
#> 122            14   7  (8.3)   1.9 ( 0.4)            13   4  (4.8)   1.2 ( 0.2)
#> 126             7   8  (9.5)   1.9 ( 0.4)            15  10 (11.9)   2.1 ( 0.5)
#>     eventscount_3
#> 1              NA
#> 2             279
#> 3              NA
#> 4              NA
#> 122             5
#> 126            21

In results above, rows having any one of “prop_x” values are greater than 6 get kept in the output.

We can use sort_order and sort_column parameters to sort results based on the specified criteria:

tbl <- outdata |>
  extend_ae_specific_events() |>
  format_ae_specific(
    display = c("n", "prop", "events_count", "events_avg"),
    sort_order = c("count_des"),
    sort_column = c("Placebo")
  )

head(tbl$tbl)
#>                                             name n_1 prop_1  eventsavg_1
#> 1                     Participants in population  86   <NA>         <NA>
#> 2   with one or more drug-related adverse events  44 (51.2)   0.7 ( 0.1)
#> 3            with no drug-related adverse events  42 (48.8)         <NA>
#> 4                                                 NA   <NA>         <NA>
#> 122                            Cardiac disorders   6  (7.0)   2.3 ( 0.6)
#> 79                         Myocardial infarction   2  (2.3)   1.0 ( 0.0)
#>     eventscount_1 n_2 prop_2  eventsavg_2 eventscount_2 n_3 prop_3  eventsavg_3
#> 1              NA  84   <NA>         <NA>            NA  84   <NA>         <NA>
#> 2             133  73 (86.9)   1.6 ( 0.2)           292  70 (83.3)   1.5 ( 0.2)
#> 3              NA  11 (13.1)         <NA>            NA  14 (16.7)         <NA>
#> 4              NA  NA   <NA>         <NA>            NA  NA   <NA>         <NA>
#> 122            14   7  (8.3)   1.9 ( 0.4)            13   4  (4.8)   1.2 ( 0.2)
#> 79              2   1  (1.2)          2.0             2   1  (1.2)          1.0
#>     eventscount_3
#> 1              NA
#> 2             279
#> 3              NA
#> 4              NA
#> 122             5
#> 79              1

Mock data preparation

The mock argument facilitates the creation of a mock table with ease.

Please note that the intention of the mock argument is not to provide an all-encompassing mock table template. Instead, it serves as a convenient method to assist users in generating a mock table that closely resembles the desired output layout. To develop a more versatile mock table generation tool, further efforts are necessary. This could potentially involve the creation of a dedicated mock table generation package or similar solutions.

tbl <- outdata |> format_ae_specific(mock = TRUE)
head(tbl$tbl)
#>                                           name  n_1 prop_1  n_2 prop_2  n_3
#> 1                   Participants in population   xx   <NA>   xx   <NA>   xx
#> 2 with one or more drug-related adverse events   xx (xx.x)   xx (xx.x)   xx
#> 3          with no drug-related adverse events   xx (xx.x)   xx (xx.x)   xx
#> 4                                              <NA>   <NA> <NA>   <NA> <NA>
#> 5                            Cardiac disorders    x  (x.x)    x  (x.x)    x
#> 6                          Atrial fibrillation    x  (x.x)    x  (x.x)    x
#>   prop_3  n_4 prop_4
#> 1   <NA>  xxx   <NA>
#> 2 (xx.x)  xxx (xx.x)
#> 3 (xx.x)   xx (xx.x)
#> 4   <NA> <NA>   <NA>
#> 5  (x.x)   xx  (x.x)
#> 6  (x.x)    x  (x.x)

RTF tables

The last step is to prepare the RTF table using tlf_ae_summary().

outdata |>
  format_ae_specific() |>
  tlf_ae_specific(
    meddra_version = "24.0",
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    path_outtable = "rtf/ae0specific1.rtf"
  )
#> The output is saved in/rtmp/RtmpGZp85u/Rbuild2a613d72ebcea3/metalite.ae/vignettes/rtf/ae0specific1.rtf

The tlf_ae_specific() function also provides some commonly used arguments to customize the table.

outdata |>
  format_ae_specific() |>
  tlf_ae_specific(
    meddra_version = "24.0",
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    col_rel_width = c(6, rep(1, 8)),
    text_font_size = 8,
    orientation = "landscape",
    path_outtable = "rtf/ae0specific2.rtf"
  )
#> The output is saved in/rtmp/RtmpGZp85u/Rbuild2a613d72ebcea3/metalite.ae/vignettes/rtf/ae0specific2.rtf

The mock table can also be generated.

outdata |>
  format_ae_specific(mock = TRUE) |>
  tlf_ae_specific(
    meddra_version = "24.0",
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    path_outtable = "rtf/mock_ae0specific1.rtf"
  )
#> The output is saved in/rtmp/RtmpGZp85u/Rbuild2a613d72ebcea3/metalite.ae/vignettes/rtf/mock_ae0specific1.rtf