Skip to contents

Process data for Event Analysis

Source: R/event_analysis.R
process_event_analysis.Rd

Process data for Event Analysis

Usage

process_event_analysis(
  datain,
  a_subset = NA_character_,
  summary_by = "Events",
  hterm,
  ht_val,
  ht_scope,
  lterm,
  lt_val,
  lt_scope
)

Arguments

datain

Input dataset can be any ADaM data.

a_subset

Analysis subset condition.

summary_by

Set to 'Events' or 'Patients'.

hterm

MedDRA queries or high level variables; valid values : "FMQ_NAM", "AEBODSYS"

ht_val

Queries/term name captured in the hl_var variable, e.g."Erythema"

ht_scope

Scope of the MedDRA queries; Valid values : "Narrow" or "Broad"

lterm

MedDRA variable; Valid value : "AEDECOD"

lt_val

MedDRA value, e.g., "Erythema"

lt_scope

Scope of the MedDRA queries; Valid values : "Narrow" or "Broad"

Value

List of data frames summarized by hterm and lterm

Examples

data(adae)
data(FMQ_Consolidated_List)

## process `ADAE` with `ae_pre_processor()`
prep_ae <- adae |>
  ae_pre_processor(
    ae_filter = "ANY",
    obs_residual = 0,
    fmq_data = FMQ_Consolidated_List
  )
prep_entry <- prep_ae[["data"]] |>
  mentry(
    trtvar = "TRTA",
    trtsort = "TRTAN",
    trttotalyn = "N",
    byvar = "FMQ_NAM"
  )
## prepare data for plot
prep_event_analysis <- prep_entry |>
  process_event_analysis(
    a_subset = glue::glue("AOCCPFL == 'Y' & {prep_ae$a_subset}"),
    summary_by = "Events",
    hterm = "FMQ_NAM",
    ht_val = "ABDOMINAL PAIN",
    ht_scope = "Narrow",
    lterm = "AEDECOD",
    lt_val = "ABDOMINAL DISCOMFORT",
    lt_scope = "Narrow"
  )
#> mcatstat success

prep_event_analysis[["pt_df"]]
#> # A tibble: 3 × 16
#>   BYVAR1    TRTVAR DPTVAR DPTVAL CVALUE DENOMN  FREQ DPTVALN BYVAR1N   PCT CPCT 
#>   <chr>     <fct>  <chr>  <chr>  <chr>   <int> <int>   <dbl>   <dbl> <dbl> <chr>
#> 1 Abdomina… "Plac… AEDEC… ABDOM… 0         213     0       1       2 0     0.00 
#> 2 Abdomina… "Xano… AEDEC… ABDOM… 0         313     0       1       2 0     0.00 
#> 3 Abdomina… "Xano… AEDEC… ABDOM… 1 (0.…    318     1       1       2 0.314 0.31 
#> # ℹ 5 more variables: XVAR <chr>, DPTVARN <dbl>, CN <chr>, PCT_N <dbl>,
#> #   Percent <chr>
prep_event_analysis[["query_df"]]
#> # A tibble: 9 × 20
#>   BYVAR1    TRTVAR DPTVAR DPTVAL CVALUE DENOMN  FREQ DPTVALN BYVAR1N   PCT CPCT 
#>   <chr>     <fct>  <chr>  <chr>  <chr>   <int> <int>   <dbl>   <dbl> <dbl> <chr>
#> 1 Abdomina… "Plac… AEDEC… ABDOM… 1 (0.…    213     1       2       1 0.469 0.47 
#> 2 Abdomina… "Plac… AEDEC… ABDOM… 0         213     0       1       2 0     0.00 
#> 3 Abdomina… "Plac… AEDEC… STOMA… 0         213     0     130       1 0     0.00 
#> 4 Abdomina… "Xano… AEDEC… ABDOM… 3 (0.…    313     3       2       1 0.958 0.96 
#> 5 Abdomina… "Xano… AEDEC… ABDOM… 0         313     0       1       2 0     0.00 
#> 6 Abdomina… "Xano… AEDEC… STOMA… 0         313     0     130       1 0     0.00 
#> 7 Abdomina… "Xano… AEDEC… ABDOM… 1 (0.…    318     1       2       1 0.314 0.31 
#> 8 Abdomina… "Xano… AEDEC… STOMA… 1 (0.…    318     1     130       1 0.314 0.31 
#> 9 Abdomina… "Xano… AEDEC… ABDOM… 1 (0.…    318     1       1       2 0.314 0.31 
#> # ℹ 9 more variables: XVAR <chr>, DPTVARN <dbl>, CN <chr>, HTERM <chr>,
#> #   HVAL <chr>, LVAL <chr>, Percent <chr>, PCT_N <dbl>, DECODh <dbl>