Descriptive

Introduction

Objectives

Compute descriptive statistics on a (vigibase) dataset
Understand the structure of the link table

Prerequisite

Be familiar with data management on drugs and adrs, with the get_* and add_* functions (see vignette("basic_workflow"))

Demo data: counts of drugs, adrs, case characteristics

Step 0: Load packages

library(vigicaen)
library(rlang)
library(dplyr)

Step 1: Load datasets and add drug and adr columns

This vignette uses the preloaded datasets (and a spurious suspdup table).

demo     <- demo_
adr      <- adr_
drug     <- drug_
link     <- link_
out      <- out_
followup <- followup_

srce     <- srce_

thg      <- thg_
mp       <- mp_
meddra   <- meddra_
smq_list <- smq_list_
smq_content <- smq_content_

suspdup <- 
  data.table::data.table(
    UMCReportId = 1,
    SuspectedduplicateReportId = NA
  )

And preloaded drug and adr dictionaries.

d_drecno <- ex_$d_drecno

a_llt <- ex_$a_llt

demo <-
  demo |>
  add_drug(
    d_code = d_drecno,
    drug_data = drug
  )
#> ℹ `.data` detected as `demo` table.

demo <-
  demo |>
  add_adr(
    a_code = a_llt,
    adr_data = adr
  )
#> ℹ `.data` detected as `demo` table.

As we aim to describe drug and adr counts, but also other variables (age, sex, type of reporter), they will be added too.

You can still refer to

# Age, sex

demo <-
  demo |>
  mutate(
    age = cut(as.integer(AgeGroup),
              breaks = c(0,4,5,6,7,8),
              include.lowest = TRUE, right = TRUE,
              labels = c("<18", "18-45","45-64", "65-74", "75+")),

    sex = case_when(Gender == "1" ~ 1,
                    Gender == "2" ~ 2,
                    Gender %in% c("-","0","9") ~ NA_real_,
                    TRUE ~ NA_real_)
  )

# Death + outcome availability

demo <- 
  demo |> 
  mutate(death = 
           ifelse(UMCReportId %in% out$UMCReportId,
                  UMCReportId %in% 
                    (out |> 
                    filter(Seriousness == "1") |> 
                    pull(UMCReportId)
                    ),
                  NA)
         )

# follow-up, seriousness

demo <-
  demo |>
  mutate(
    fup = if_else(UMCReportId %in% followup$UMCReportId, 1, 0),
    serious = 
      ifelse(
        UMCReportId %in% out$UMCReportId,
        UMCReportId %in% 
          (out |> 
          filter(Serious == "Y") |> 
          pull(UMCReportId)
          ),
        NA)
  )

# year

demo <- 
  demo |> 
  mutate(
    year = as.numeric(substr(FirstDateDatabase, start = 1, stop = 4))
    )

# type of reporter

demo <-
  demo |>
  left_join(
    srce |> transmute(UMCReportId, type_reporter = Type),
    by = "UMCReportId")

`desc_facvar()`

desc_facvar() generates a summary of categorical variables with 2 or more levels.

Its .data argument is a dataset to describe. Described variables should be passed to vf, as a character vector.

Multi-level variables

Let’s take the demo dataset as an example, with variable “age”.

desc_facvar(
  .data = demo,
  vf = "age"
)
#> # A tibble: 5 × 4
#>   var   level value           n_avail
#>   <chr> <chr> <chr>             <int>
#> 1 age   <18   " 1/499 (0%) "      499
#> 2 age   18-45 "43/499 (9%) "      499
#> 3 age   45-64 "173/499 (35%)"     499
#> 4 age   65-74 "174/499 (35%)"     499
#> 5 age   75+   "108/499 (22%)"     499

The output format is a data.frame, of class tibble.

The first column, var, contains the name of the variable of interest. The second column, level, contains the level of the variable.

In this example, the first line shows the number of patients whose age variable (var) is “<18”, i.e. patients under 18 years old.

The percentage appears in the value column, after the count of cases and the total number of reports for which the information is available.

This number of reports with available information is recalled in the n_avail column.

Binary variables

What happens when the variable has only two levels, for example 1 and 0, as is often the case for the drug and adr variables?

desc_facvar(
  .data = demo,
  vf = "nivolumab"
)
#> # A tibble: 2 × 4
#>   var       level value         n_avail
#>   <chr>     <chr> <chr>           <int>
#> 1 nivolumab 0     525/750 (70%)     750
#> 2 nivolumab 1     225/750 (30%)     750

The output format is unchanged, with a data.frame as output.

The reading is unchanged: we get the count of cases of the variable nivolumab, by its two levels. There are thus 225 patients exposed to nivolumab, out of 750 reports in total, which represents 30% of patients.

Conversely, 525 reports do not mention nivolumab.

In general, when presenting the results, the level 0 of binary variables provides little information and can be omitted.

Logical variables

Let’s continue with another example on the “seriousness” status.

desc_facvar(
  .data = demo,
  vf = "serious"
)
#> # A tibble: 2 × 4
#>   var     level value         n_avail
#>   <chr>   <chr> <chr>           <int>
#> 1 serious FALSE 181/747 (24%)     747
#> 2 serious TRUE  566/747 (76%)     747

The “serious” variable takes the values TRUE/FALSE, and not 1/0, but it is interpreted in the same way (it is only an artifact of construction).

Thus, 566 cases are considered serious, out of 747 where the information is available.

Exporting raw values

You can export to run plotting or other formatting functions, with argument export_raw_values.

desc_facvar(
  .data = demo,
  vf = "nivolumab",
  export_raw_values = TRUE
)
#> # A tibble: 2 × 6
#>   var       level value         n_avail     n    pc
#>   <chr>     <chr> <chr>           <int> <int> <dbl>
#> 1 nivolumab 0     525/750 (70%)     750   525    70
#> 2 nivolumab 1     225/750 (30%)     750   225    30

Grouping several levels of a variable

What if the available categories do not match our final needs?

In the example on age, there is only one patient under 18 years old, and few patients under 45 years old. We would like to group all this data into a single line for a summary.

The solution is to create the variable with the desired levels upstream, in a data management step.

demo <-
  demo |>
  mutate(
    age2 = cut(as.integer(AgeGroup),
              breaks = c(0, 6, 7, 8),
              include.lowest = TRUE, right = TRUE,
              labels = c("<64", "65-74", "75+"))
  )


desc_facvar(
  demo,
  vf = "age2"
)
#> # A tibble: 3 × 4
#>   var   level value         n_avail
#>   <chr> <chr> <chr>           <int>
#> 1 age2  <64   217/499 (43%)     499
#> 2 age2  65-74 174/499 (35%)     499
#> 3 age2  75+   108/499 (22%)     499

The same is true for columns like “year”.

When studying the “year” column, it is common to get an error message

desc_facvar(
  .data = demo,
  vf = "year"
)
#> Error in `desc_facvar()`:
#> ! Too many levels detected in: year
#> ✖ Number of levels: 13 exceeded `ncat_max`(10)
#> ℹ Did you pass a continuous variable to `desc_facvar()`?
#> → Set `ncat_max` to suppress this error.

The error message “Too many levels detected in year” is intentional, to avoid passing continuous variables in the vf argument.

The maximum number of categories that can be taken by a variable treated by desc_facvar is controlled by the ncat_max argument.

If a variable has more than ncat_max different levels, the function stops.

We can therefore solve this problem by adjusting the value of this parameter.

desc_facvar(
  .data = demo,
  vf = "year",
  ncat_max = 20
)
#> # A tibble: 13 × 4
#>    var   level value           n_avail
#>    <chr> <chr> <chr>             <int>
#>  1 year  2011  " 1/750 (0%) "      750
#>  2 year  2012  " 1/750 (0%) "      750
#>  3 year  2013  " 2/750 (0%) "      750
#>  4 year  2014  "10/750 (1%) "      750
#>  5 year  2015  " 8/750 (1%) "      750
#>  6 year  2016  "15/750 (2%) "      750
#>  7 year  2017  "116/750 (15%)"     750
#>  8 year  2018  "150/750 (20%)"     750
#>  9 year  2019  "116/750 (15%)"     750
#> 10 year  2020  "72/750 (10%)"      750
#> 11 year  2021  "99/750 (13%)"      750
#> 12 year  2022  "119/750 (16%)"     750
#> 13 year  2023  "41/750 (5%) "      750

This allows to review the main years, but will be less transposable in a final table of a manuscript. A categorization of the reporting years may be more informative.

Explicit categorical variables

Levels of some variables are indicated by numbers.

desc_facvar(
  .data = demo,
  vf = "Region"
)
#> # A tibble: 6 × 4
#>   var    level value           n_avail
#>   <chr>  <chr> <chr>             <int>
#> 1 Region 1     " 1/750 (0%) "      750
#> 2 Region 2     "389/750 (52%)"     750
#> 3 Region 3     "17/750 (2%) "      750
#> 4 Region 4     "276/750 (37%)"     750
#> 5 Region 5     " 6/750 (1%) "      750
#> 6 Region 6     "61/750 (8%) "      750

We know that 389 cases come from Region “2”, without being able to say which geographical area this region belongs to.

To obtain the correspondence, there are external tables, such as this one for the Region: (they can be found in the subsidiary tables of vigibase).

Code	Label
1	African Region
2	Region of the Americas
3	South-East Asia Region
4	European Region
5	Eastern Mediterranean Region
6	Western Pacific Region

Several options are possible to bring the information back directly into demo, the simplest is to use factors

demo <-
  demo |> 
  mutate(
    Region = factor(Region, levels = c("1", "2", "3", "4", "5", "6"))
  )

levels(demo$Region) <-
  c("African Region",                                    
    "Region of the Americas",                            
    "South-East Asia Region",                            
    "European Region",                                   
    "Eastern Mediterranean Region",                      
    "Western Pacific Region"  
  )

Note the transformation in two steps. The first to sort the levels of the variable, the second to assign the labels to its levels. This sequence is necessary to avoid a random sorting of levels.

This transformation has the effect of modifying the result of desc_facvar()

desc_facvar(
  .data = demo,
  vf = "Region"
)
#> # A tibble: 6 × 4
#>   var    level                        value           n_avail
#>   <chr>  <chr>                        <chr>             <int>
#> 1 Region African Region               " 1/750 (0%) "      750
#> 2 Region Region of the Americas       "389/750 (52%)"     750
#> 3 Region South-East Asia Region       "17/750 (2%) "      750
#> 4 Region European Region              "276/750 (37%)"     750
#> 5 Region Eastern Mediterranean Region " 6/750 (1%) "      750
#> 6 Region Western Pacific Region       "61/750 (8%) "      750

The two other variables mainly affected by this phenomenon are Type and type_reporter. The transformation code is found in vignette("template_main.R")

Other arguments of `desc_facvar()`

Three other arguments allow to control the output format of the results.

format is a character string that must necessarily contain the values n, N and pc.

This argument allows to customize the way the result is displayed. For example, if you want to put the percentage in brackets instead of parentheses

desc_facvar(
  .data = demo,
  vf = "nivolumab",
  format = "n_/N_ [pc_%]"
)
#> # A tibble: 2 × 4
#>   var       level value         n_avail
#>   <chr>     <chr> <chr>           <int>
#> 1 nivolumab 0     525/750 [70%]     750
#> 2 nivolumab 1     225/750 [30%]     750

You can also change all other elements of this argument.

pad_width allows to center the results in the middle of a character string. If you have particularly high numbers, you can increase the value of this parameter, so that your results remain well centered.
digits controls the number of digits after the decimal point for the percentage. Warning, it is not guaranteed that the sum will be exactly 100%.

desc_facvar(
  .data = demo,
  vf = "nivolumab",
  digits = 1
)
#> # A tibble: 2 × 4
#>   var       level value           n_avail
#>   <chr>     <chr> <chr>             <int>
#> 1 nivolumab 0     525/750 (70.0%)     750
#> 2 nivolumab 1     225/750 (30.0%)     750

Drug data: drug screening

Adr data: adr screening and evolution of adverse events

screen_drug() let you screen the most drugs reported in a drug dataset, sorted by frequency.

screen_drug(drug, mp_data = mp, top_n = 5)
#> # A tibble: 5 × 4
#>   `Drug name`      DrecNo     N percentage
#>   <chr>             <int> <int>      <dbl>
#> 1 pembrolizumab  20116296   298      39.7 
#> 2 nivolumab     111841511   225      30   
#> 3 ipilimumab    133138448    86      11.5 
#> 4 atezolizumab  112765189    69       9.2 
#> 5 durvalumab    125456180    68       9.07

Most of the time, you will have filtered the drug data upstream, with some add_* function, allowing to focus on a subset of cases (of a specific drug, adr, or any set of these)

For example, identify colitis cases and screen drugs under this reaction.

drug |> 
  add_adr(
    a_llt,
    adr_data = adr
  ) |> 
  filter(a_colitis == 1) |> 
  screen_drug(
    mp_data = mp, top_n = 5
  )
#> ℹ `.data` detected as `drug` table.
#> # A tibble: 5 × 4
#>   `Drug name`      DrecNo     N percentage
#>   <chr>             <int> <int>      <dbl>
#> 1 nivolumab     111841511    44       42.3
#> 2 pembrolizumab  20116296    40       38.5
#> 3 ipilimumab    133138448    20       19.2
#> 4 NA             73636724    14       13.5
#> 5 NA             34178924    13       12.5

Adr screening

screen_adr() let you screen the most frequent reactions reported in an adr dataset, sorted by frequency.

screen_adr(adr_, meddra = meddra_)
#>                                               term     n percentage
#>                                             <char> <int>      <num>
#> 1:                                            <NA>   678 90.4000000
#> 2: Respiratory, thoracic and mediastinal disorders   110 14.6666667
#> 3:                      Gastrointestinal disorders   104 13.8666667
#> 4:                              Vascular disorders     9  1.2000000
#> 5:                         Immune system disorders     6  0.8000000
#> 6:                         Hepatobiliary disorders     5  0.6666667
#> 7:          Skin and subcutaneous tissue disorders     1  0.1333333

Different term levels can be used, according to meddra, with argument term_level.

Most of the time, you will have filtered the adr data upstream, with some add_* function, allowing to focus on a subset of cases (of a specific drug, adr, or any set of these).

Outcome

The adr table contains information on the evolution of adverse events.

The possible outcomes (column Outcome) are

Recovered/resolved
Recovering/resolving
Recovered/resolved with sequelae
Not recovered/not resolved
Fatal
Died- unrelated to reaction
Died- reaction may be contributory

The adr structure is as follows

UMCReportId	Adr_Id	Outcome
1	a_1	1
1	a_2	2
2	a_3	3
2	a_4	1

A case, identified by its UMCReportId, may have several adverse events (Adr_Id) with different outcomes. Summarizing this information requires prioritization.

The logic is as follows: take the ” worst evolution” possible for each event of each case, in order to count each event only once for each case.

In order to filter cases according to a drug exposition, it is necessary to join the drug data to the adr table.

Step 1: Data management of adr with add_drug and add_adr

add_drug() and add_adr() can be used on adr data.


adr <-
  adr |>
  add_drug(
    d_code = d_drecno,
    drug_data = drug
  )
#> ℹ `.data` detected as `adr` table.

adr <-
  adr |>
  add_adr(
    a_code = a_llt,
    adr_data = adr
  )
#> ℹ `.data` detected as `adr` table.

This allows to identify drugs and adverse events of interest in the adr table.

Drugs are identified at the case level in this table.

Step 2: `desc_outcome()` function

The desc_outcome function prioritizes data according to the rule:

Take the “worst evolution” possible for each event of each case, in order to count each event only once for each case.

adr |> 
  desc_outcome(
    drug_s = "nivolumab",
    adr_s = "a_colitis"
  )
#> # A tibble: 5 × 4
#>   drug_s    adr_s     n_cas out_label                 
#>   <chr>     <chr>     <int> <chr>                     
#> 1 nivolumab a_colitis    10 Unknown                   
#> 2 nivolumab a_colitis    25 Recovered/resolved        
#> 3 nivolumab a_colitis     6 Recovering/resolving      
#> 4 nivolumab a_colitis     1 Not recovered/not resolved
#> 5 nivolumab a_colitis     2 Fatal

In the case where adr was previously filtered to contain only data of a specific adverse drug reaction (for example, with tb_subset()), it is still preferable to recreate the drug column with add_drug (it will take the value 1 for all cases).

Link data: time to onset, dechallenge, rechallenge

The link table, as created with tb_vigibase(), contains additional information than the original link table.

It is augmented with

UMCReportId the case id
tto_mean the average of TimeToOnsetMin, and TimeToOnsetMax, in days
range the half-difference between TimeToOnsetMin and TimeToOnsetMax, in days

These additional variables are useful to compute the time from drug initiation to adverse drug reaction onset, and also to compute dechallenge and rechallenge data at case level.

Step 1: Load the datasets

link <- 
  link_

Step 2: Add drug and adr columns

The link table studies the relationship of each drug - adverse event pair, within the reports. There are therefore several lines in link for each line (case) in demo.

demo table example

UMCReportId	Other data (age, sexe…)
1	65-74, Man
2	65-74, Woman
3	45-64, Woman

The corresponding link table would be

UMCReportId	Drug_Id	Adr_Id	Time to onset
1	1_1	1_a	60
1	1_2	1_a	30
1	1_1	1_b	45
1	1_2	1_b	15
2
2
3
3
3

Learn the content of link

Let’s take a while to read data related to the case no 1, in the previous example.

It contains two different Drug_Id 1_1 and 1_2: this means that this case has two different drugs. Most of the time, it is two different drugs (let’s say, paracetamol and ibuprofen for this example). It can also be the same drug, with different administration modalities (paracetamol with two dosages, or at two different times).
It contains two different Adr_Id 1_a and 1_b: this means that this case has two different adverse events. Mostly, it refers to two different events (e.g. hepatitis and hemorrhage).
Information are available for each combination. The time to onset, i.e. the delay between drug initiation and event onset is displayed for each combination

The reading is as follows:

The hepatitis (1_a) occurred 60 days after the introduction of paracetamol (1_1), and 30 days after the introduction of ibuprofen (1_2).
The hemorrhage (1_b) occurred 45 days after the introduction of paracetamol (1_1), and 15 days after the introduction of ibuprofen (1_2).

In this relatively simple example, everything is coherent: we observe that paracetamol and ibuprofen were introduced 30 days apart from each other.

The reality is often more complex: as previously announced, there may be several lines in linkfor the same drug, with different time to onset.

In this case, it is important to decide how to handle this multiple information.

For example, we could have a time to onset at 30 days for paracetamol taken at 500mg daily, and a time to onset at 15 days for paracetamol taken at 1000mg daily.

Identify drugs and adverse events

As for the demo and adr tables, the link table must be completed with drug and adr columns, using the add_* family functions.

link <-
  link |> 
   add_drug(
    d_code = d_drecno,
    drug_data = drug
  )
#> ℹ `.data` detected as `link` table.

link <-
  link |>
  add_adr(
    a_code = a_llt,
    adr_data = adr
  )
#> ℹ `.data` detected as `link` table.

Counts check

link |> 
   check_dm(
     cols = c(names(d_drecno), names(a_llt))
     )
#>               [,1]
#> ipilimumab     267
#> atezolizumab   189
#> durvalumab     166
#> nivolumab     1347
#> pembrolizumab 1819
#> avelumab        83
#> cemiplimab      38
#> tremelimumab    35
#> a_embolism      38
#> a_colitis      314
#> a_pneumonitis  238

!! Warning!!, counts correspond to the number of lines for each drug and each effect. It is not the number of reports containing each drug or each effect. If you want to obtain this information, you must query the demo table.

Time to onset

The time to onset information is contained in two variables in the link table: TimeToOnsetMin and TimeToOnsetMax. These two variables reflect the minimum and maximum delay of the adverse event occurrence compared to the drug intake, taking into account the uncertainty of the input data.

UMCReportId	Drug_Id	Adr_Id	TimeToOnsetMin	TimeToOnsetMax
1	1_1	1_a	45	75

Here, hepatitis occurred between 45 and 75 days after first paracetamol intake.

This structure is inherited of the incertitude from the source reporter or the case. This case would correspond to data like: “Hepatitis occurred 2months after paracetamol introduction”.

This sentence contains an imprecision on the exact delay of occurrence: what was the exact day of the month? Was it 1 month and 15 days? Or 2 months and 15 days? More? It is impossible to decide.

By convention, we consider that the true time to onset is +/- 15 days from the indicated date (here, between 60 - 15 = 45 days, and 60 + 15 = 75 days).

Two parameters are derived from this information: the mean time to onset tto_mean and the range. The calculation is as follows:

tto_mean = (TimeToOnsetMax + TimeToOnsetMin) / 2

range = (TimeToOnsetMax + TimeToOnsetMin) / 2 - TimeToOnsetMin

UMCReportId	Drug_Id	Adr_Id	TimeToOnsetMin	TimeToOnsetMax	tto_mean	range
1	1_1	1_a	45	75	60	30

The tto_mean is intuitive: it is the average delay between the two available values. In our example, we find 60 days, which is the delay indicated by the reporter.

The range gives the uncertainty: 30 days in our example, meaning that we cannot be more precise than 30 days.

The Uppsala Monitoring Centre recommendation is to use only the time to onset whose range is <= 1, i.e. the cases where the date is known to the day.

Note: the information on hours and minutes is also present in the time to onset, if known.

If we keep on the example of hepatitis, we could have a time to onset at 30 days for paracetamol taken at 500mg daily, and a time to onset at 15 days for paracetamol taken at 1000mg daily.

In this case, it is important to decide how to handle this multiple information. Otherwise, we would have two different tto_mean values for the paracetamol - hepatitis pair.

There is a need for an arbitrary rule to synthetize these data. Our habit is to take the longest delay between the drug introduction and the event occurrence (i.e. the delay between the first drug intake and the event). Admittedly, this may not meet all needs.

This information, that we call tto_max, is obtained with extract_tto().

extract_tto(
  .data = link,
  drug_s = "nivolumab",
  adr_s  = "a_colitis"
)
#>    UMCReportId tto_max     adr_s    drug_s
#> 1    108846594     205 a_colitis nivolumab
#> 2     73027866     175 a_colitis nivolumab
#> 3     87966085      36 a_colitis nivolumab
#> 4     88371745     740 a_colitis nivolumab
#> 5    138643678     379 a_colitis nivolumab
#> 6     39936658      99 a_colitis nivolumab
#> 7    140765885       5 a_colitis nivolumab
#> 8     11372968      62 a_colitis nivolumab
#> 9     63102481      57 a_colitis nivolumab
#> 10    34209616       8 a_colitis nivolumab
#> 11    21293261      59 a_colitis nivolumab
#> 12    58329610     541 a_colitis nivolumab
#> 13    38269558     393 a_colitis nivolumab
#> 14    85691581     490 a_colitis nivolumab
#> 15   112663221       7 a_colitis nivolumab
#> 16   109716692      37 a_colitis nivolumab
#> 17    56986472      47 a_colitis nivolumab
#> 18    76401465     390 a_colitis nivolumab
#> 19   125056616      18 a_colitis nivolumab
#> 20    33942691      36 a_colitis nivolumab
#> 21   133088642      23 a_colitis nivolumab
#> 22   143881598     113 a_colitis nivolumab
#> 23    79620779      85 a_colitis nivolumab
#> 24    95759941      59 a_colitis nivolumab

The tto_max is the longest delay between the drug introduction and the event occurrence. There is only one line for each drug - adr pair.

This information can be used for a graphical representation, or to derive an average, a range… The second option is possible in many ways, notably with desc_tto().

desc_tto(
  .data = link,
  drug_s = "nivolumab",
  adr_s  = "a_colitis"
)
#>      drug_s     adr_s     var level                         value n_avail
#> 1 nivolumab a_colitis tto_max  <NA> 60.5 (36.0-248.5) [5.0-740.0]      24

Several drugs and reactions can be queried in these two functions.

desc_tto(
  .data = link,
  drug_s = c("nivolumab", "pembrolizumab"),
  adr_s  = c("a_colitis", "a_pneumonitis")
)
#>          drug_s         adr_s     var level                           value
#> 1     nivolumab     a_colitis tto_max  <NA>   60.5 (36.0-248.5) [5.0-740.0]
#> 2 pembrolizumab     a_colitis tto_max  <NA> 44.0 (18.5-112.5) [1.0-1,207.0]
#> 3     nivolumab a_pneumonitis tto_max  <NA>  75.0 (49.8-167.0) [18.0-602.0]
#> 4 pembrolizumab a_pneumonitis tto_max  <NA> 52.0 (21.0-101.0) [0.0-1,050.0]
#>   n_avail
#> 1      24
#> 2      15
#> 3      22
#> 4      17

Dechallenge

desc_dch() synthesizes the number of positive dechallenges:

A positive dechallenge occurs when the drug has been stopped or its dosage has been reduced, and the reaction has abatted.

desc_dch(
  link,
  drug_s = "nivolumab",
  adr_s  = "a_colitis"
)
#>      drug_s     adr_s pos_dch
#> 1 nivolumab a_colitis      27

desc_dch(
  link,
  drug_s = c("nivolumab", "pembrolizumab"),
  adr_s  = c("a_colitis", "a_pneumonitis")
)
#>          drug_s         adr_s pos_dch
#> 1     nivolumab     a_colitis      27
#> 2 pembrolizumab     a_colitis      19
#> 3     nivolumab a_pneumonitis      22
#> 4 pembrolizumab a_pneumonitis      26

Rechallenge

Description span from rechallenge cases to informative rechallenge cases (those cases where the outcome is known). Drug and Adr identifiers refer to DrecNo and MedDRA_Id, respectively. Terminology

overall as opposed to rch for rechallenged (rch + no_rch = overall).
Among rch, inf (informative) as opposed to non_inf (inf + non_inf = rch)
Among inf, rec (recurring) as opposed to non_rec (rec + non_rec = inf)

desc_rch(
  link,
  demo_data = demo,
  drug_s = "nivolumab",
  adr_s  = "a_colitis"
)
#>       drug_s     adr_s n_overall n_rch n_inf n_rec
#>       <char>    <char>     <int> <int> <int> <int>
#> 1: nivolumab a_colitis        44    26    19    12

The number of cases is counted at the case level in demo. This explains why the demo_data argument is required here.

As with desc_tto() and desc_dch(), you can query several drug - adr pairs at once.

Columns passed to arguments drug_s and adr_s can correspond to sets of drugs or events, or even identify all cases present in your dataset.

Let’s say we want to know the number of positive rechallenge cases for our entire dataset

We must create a variable that takes the value 1 for all cases.

link <-
  link |> 
  mutate(
    all_cases = 1
  )

We a particular syntax, we can access the information

desc_rch(
  link,
  demo_data = demo,
  drug_s = "all_cases",
  adr_s  = "all_cases"
)
#>       drug_s     adr_s n_overall n_rch n_inf n_rec
#>       <char>    <char>     <int> <int> <int> <int>
#> 1: all_cases all_cases       707   297   106    28