The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
The objective of this tutorial is to generate a production-ready AE summary. It extends examples shown in the AE summary chapter of the R for Clinical Study Reports and Submission book.
The AE summary analysis entails the creation of tables that summarize adverse events information. To accomplish this using metalite.ae, three essential functions are required:
prepare_ae_summary()
: prepare analysis raw
datasets.format_ae_summary()
: prepare analysis (mock) outdata
with proper format.tlf_ae_summary()
: transfer (mock) output dataset to RTF
files.There is one optional function to extend AE summary analysis:
extend_ae_specific_inference()
: add risk difference
inference results based on M&N method.An example output:
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
#> 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'
The function prepare_ae_summary()
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_summary(
meta,
population = "apat",
observation = "wk12",
parameter = "any;rel;ser"
)
outdata
#> List of 13
#> $ meta :List of 7
#> $ population : chr "apat"
#> $ observation : chr "wk12"
#> $ parameter : chr "any;rel;ser"
#> $ n :'data.frame': 5 obs. of 4 variables:
#> $ order : num [1:5] 1 100 200 300 400
#> $ group : chr [1:4] "Placebo" "Low Dose" "High Dose" "Total"
#> $ reference_group: num 1
#> $ prop :'data.frame': 5 obs. of 4 variables:
#> $ diff :'data.frame': 5 obs. of 2 variables:
#> $ n_pop :'data.frame': 1 obs. of 4 variables:
#> $ name : chr [1:5] "Participants in population" "with one or more adverse events" "with no adverse events" "with drug-related{^a} adverse events" ...
#> $ prepare_call : language prepare_ae_summary(meta = meta, population = "apat", observation = "wk12", parameter = "any;rel;ser")
The resulting dataset contains frequently used statistics, with
variables indexed according to the order specified in
outdata$group
.
The row is indexed according to the order of
outdata$name
.
head(data.frame(outdata$order, outdata$name))
#> outdata.order outdata.name
#> 1 1 Participants in population
#> 2 100 with one or more adverse events
#> 3 200 with no adverse events
#> 4 300 with drug-related{^a} adverse events
#> 5 400 with serious adverse events
n_pop
: number of participants in population.n
: number of subjects with AE.head(outdata$n)
#> n_1 n_2 n_3 n_4
#> 1 86 84 84 254
#> 2 69 77 79 225
#> 3 17 7 5 29
#> 21 44 73 70 187
#> 22 0 1 2 3
prop
: proportion of subjects with AE.head(outdata$prop)
#> prop_1 prop_2 prop_3 prop_4
#> 1 NA NA NA NA
#> 2 80.23256 91.666667 94.047619 88.582677
#> 3 19.76744 8.333333 5.952381 11.417323
#> 21 51.16279 86.904762 83.333333 73.622047
#> 22 0.00000 1.190476 2.380952 1.181102
diff
: risk difference compared with the
reference_group
.Once the raw analysis results are obtained, the
format_ae_summary()
function can be employed to prepare the
outdata, ensuring its compatibility with production-ready RTF
tables.
tbl <- outdata |> format_ae_summary()
tbl$tbl
#> name n_1 prop_1 n_2 prop_2 n_3 prop_3 n_4
#> 1 Participants in population 86 <NA> 84 <NA> 84 <NA> 254
#> 2 with one or more adverse events 69 (80.2) 77 (91.7) 79 (94.0) 225
#> 3 with no adverse events 17 (19.8) 7 (8.3) 5 (6.0) 29
#> 21 with drug-related{^a} adverse events 44 (51.2) 73 (86.9) 70 (83.3) 187
#> 22 with serious adverse events 0 (0.0) 1 (1.2) 2 (2.4) 3
#> prop_4
#> 1 <NA>
#> 2 (88.6)
#> 3 (11.4)
#> 21 (73.6)
#> 22 (1.2)
By using the display
argument, we can choose specific
statistics to include. For instance, we have the option to incorporate
the risk difference.
tbl <- outdata |> format_ae_summary(display = c("n", "prop", "diff"))
tbl$tbl
#> name n_1 prop_1 n_2 prop_2 n_3 prop_3 diff_2
#> 1 Participants in population 86 <NA> 84 <NA> 84 <NA> NA
#> 2 with one or more adverse events 69 (80.2) 77 (91.7) 79 (94.0) 11.4
#> 3 with no adverse events 17 (19.8) 7 (8.3) 5 (6.0) 35.7
#> 21 with drug-related{^a} adverse events 44 (51.2) 73 (86.9) 70 (83.3) 1.2
#> 22 with serious adverse events 0 (0.0) 1 (1.2) 2 (2.4) -11.4
#> diff_3
#> 1 NA
#> 2 13.8
#> 3 32.2
#> 21 2.4
#> 22 -13.8
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_summary(display = c("n", "prop", "diff", "diff_ci"))
tbl$tbl
#> name n_1 prop_1 n_2 prop_2 n_3 prop_3 diff_2
#> 1 Participants in population 86 <NA> 84 <NA> 84 <NA> NA
#> 2 with one or more adverse events 69 (80.2) 77 (91.7) 79 (94.0) 11.4
#> 3 with no adverse events 17 (19.8) 7 (8.3) 5 (6.0) 35.7
#> 21 with drug-related{^a} adverse events 44 (51.2) 73 (86.9) 70 (83.3) 1.2
#> 22 with serious adverse events 0 (0.0) 1 (1.2) 2 (2.4) -11.4
#> ci_2 diff_3 ci_3
#> 1 (-4.4, 0.0) NA (-4.4, 0.0)
#> 2 ( 1.0, 22.2) 13.8 ( 4.0, 24.3)
#> 3 (-22.2, -1.0) 32.2 (-24.3, -4.0)
#> 21 (22.4, 48.0) 2.4 (18.4, 44.8)
#> 22 (-3.1, 6.5) -13.8 (-2.0, 8.3)
The mock
argument facilitates the creation of a mock
table with ease.
Please note that the intention of the mock
argument is
not to provide an all-encompassing mock table template. Instead, it
serves as a convenient method to assist users in generating a mock table
that closely resembles the desired output layout. To develop a more
versatile mock table generation tool, further efforts are necessary.
This could potentially involve the creation of a dedicated mock table
generation package or similar solutions.
tbl <- outdata |> format_ae_summary(mock = TRUE)
tbl$tbl
#> name n_1 prop_1 n_2 prop_2 n_3 prop_3 n_4
#> 1 Participants in population xx <NA> xx <NA> xx <NA> xxx
#> 2 with one or more adverse events xx (xx.x) xx (xx.x) xx (xx.x) xxx
#> 3 with no adverse events xx (xx.x) x (x.x) x (x.x) xx
#> 4 with drug-related{^a} adverse events xx (xx.x) xx (xx.x) xx (xx.x) xxx
#> 5 with serious adverse events x (x.x) x (x.x) x (x.x) x
#> prop_4
#> 1 <NA>
#> 2 (xx.x)
#> 3 (xx.x)
#> 4 (xx.x)
#> 5 (x.x)
The last step is to prepare the RTF table using
tlf_ae_summary()
.
outdata |>
format_ae_summary() |>
tlf_ae_summary(
source = "Source: [CDISCpilot: adam-adsl; adae]",
path_outtable = "rtf/ae0summary1.rtf"
)
#> The output is saved in/rtmp/Rtmp5KcMff/Rbuild3e664b2f7731/metalite.ae/vignettes/rtf/ae0summary1.rtf
The tlf_ae_summary()
function also provides some
commonly used argument to customize the table.
outdata |>
format_ae_summary() |>
tlf_ae_summary(
source = "Source: [CDISCpilot: adam-adsl; adae]",
col_rel_width = c(6, rep(1, 8)),
text_font_size = 8,
orientation = "landscape",
path_outtable = "rtf/ae0summary2.rtf"
)
#> The output is saved in/rtmp/Rtmp5KcMff/Rbuild3e664b2f7731/metalite.ae/vignettes/rtf/ae0summary2.rtf
The mock table can also be generated.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.