---
title: "Creating ADTR"
output: 
  rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Creating ADTR}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(admiraldev)
```

# Introduction

This article describes creating an `ADTR` (Tumor Results) ADaM with common
oncology parameters based on RECIST v1.1.

The main part in programming a tumor results dataset is the calculation of the
sum of diameters of all target lesions (lymph nodes & non-lymph nodes), the
calculation of nadir, change & percentage change from baseline, and the
analysis flags that could be required for reporting.  The tumor results data
could be set up for investigator and/or Independent review facility
(IRF)/Blinded Independent Central Review (BICR) data.  The below sample code
would need to be updated (for example, update the Evaluator `TR.TREVAL`,
`TU.TUEVAL`, and the applicable parameter details `PARAM`, `PARAMCD`, `PARCATy`)
in order to create the acquired data for Independent review facility
(IRF)/Blinded Independent Central Review (BICR).

The source dataset used will depend on each company, this could be solely the
`TR` domain or you may merge `TU` with `TR` (SDTM inputs) to get additional data variables or to ensure that you're processing the same lesions as collected.

Individual lesion diameters for each target lesion is required to calculate the sum of diameters for all target lesions, this data could be taken directly from `TR` or additional parameters could be created in `ADTR` (or similar) depending on the additional processing required (e.g. imputation of dates, re-labeling of visits) and your company specifications.

The majority of the functions used here exist from `{admiral}`.

**Note**: *All examples assume CDISC SDTM and/or ADaM format as input unless
otherwise specified.*

## Required Packages

The examples of this vignette require the following packages. 

```{r, warning=FALSE, message=FALSE}
library(admiral)
library(dplyr)
library(pharmaversesdtm)
library(pharmaverseadam)
library(lubridate)
library(stringr)
library(admiralonco)
```

# Programming Workflow

* [Read in Data](#readdata)
* [Merge `TR` with `TU` and Derive New Variables](#merge)
* [Pre-processing of Input Records](#input)
* [Derive Parameters for Lesion Diameters (`LDIAMn`, `NLDIAMn`)](#parameters)
* [Derive Parameter for Sum of Diameters (`SDIAM`) and `ANL01FL`](#sdiam)
* [Derive Baseline (`ABLFL`, `BASE`)](#base)
* [Derive NADIR](#nadir)
* [Derive Change from Baseline (`CHG`, `PCHG`)](#chg)
* [Derive Additional Flag Variables (e.g. `CRFL`, `CRNFL`, `PDFL`, `PDFL`](#addflag)
* [Derive Additional Analysis Flags (e.g. `ANLzzFL`](#analflag)
* [Derive Analysis Sequence Number (`ASEQ`)](#aseq)
* [Add `ADSL` Variables](#adslvars)

## Read in Data {#readdata}

To start, all data frames needed for the creation of `ADTR` should be read into the environment. This will be a company specific process.
Some of the data frames needed may be `ADSL`, `ADRS` ,`RS`, `TU`, `TR`, `SUPPTU` `SUPPTR`.

For example purposes, the SDTM and ADaM datasets (based on CDISC Pilot test
data)---which are included in `{pharmaversesdtm}` and `{pharmaverseadam}`---are
used. Also, see [Handling of Missing
Values](https://pharmaverse.github.io/admiral/articles/admiral.html#handling-of-missing-values)
explains why we need to use the `convert_blanks_to_na()` function.

On the `TR` domain we filter on where tumor assessment short name `TRTESTCD` is
`"LDIAM"` or `"LPERP"`. Depending on your data collection standards you may need
to update the code to filter on `TRTESTCD == "DIAMETER"`. If this is the case
then the template code where we derive parameters for lesions diameters should
be updated accordingly.

```{r}
data("adsl")
data("adrs_onco")
data("rs_onco_recist")
data("tu_onco_recist")
data("tr_onco_recist")
adrs <- adrs_onco
tu <- tu_onco_recist
tr <- tr_onco_recist
rs <- rs_onco_recist

tu <- convert_blanks_to_na(tu) %>%
  filter(TUEVAL == "INVESTIGATOR")
tr <- convert_blanks_to_na(tr) %>%
  filter(
    TREVAL == "INVESTIGATOR" & TRGRPID == "TARGET" & TRTESTCD %in% c("LDIAM", "LPERP")
  )
rs <- convert_blanks_to_na(rs)
```

At this step, it may be useful to join `ADSL` to your `TR` domain.  For efficiency, only the `ADSL` variables (in this example, Randomization date (`RANDDT`) used for derivations are selected at this step.  The rest of the relevant `ADSL` variables would be added later.

```{r eval=TRUE}
adsl_vars <- exprs(RANDDT)
tr <- derive_vars_merged(
  tr,
  dataset_add = adsl,
  new_vars = adsl_vars,
  by_vars = get_admiral_option("subject_keys")
)
```

## Merge `TR` with `TU` and Derive New Variables {#merge}

Depending on your company specifications you may want to merge `TU` with `TR`. At this point if you require any new variables these could be added in this step. As an example, here we are keeping the tumor location (`TULOC`), and deriving a new variable for the tumor group site (`TULOCGR1`). 

```{r}
tr <- derive_vars_merged(
  tr,
  dataset_add = tu,
  new_vars = exprs(TULOC),
  by_vars = c(get_admiral_option("subject_keys"), exprs(TRLNKID = TULNKID))
) %>% mutate(
  TULOCGR1 = if_else(
    TULOC == "LYMPH NODE",
    "NODAL",
    "NON-NODAL"
  )
)
```

```{r, echo=FALSE}
dataset_vignette(tr, display_vars = exprs(USUBJID, VISIT, TRLNKID, TULOC, TULOCGR1))
```  

Furthermore, you could create additional new variables that are required
downstream when deriving the required parameters. For example, here we include
lesion ID expected (`LSEXP`) and lesion ID assessed (`LSASS`).

```{r}
tr <- mutate(
  tr,
  LSEXP = TRLNKID,
  LSASS = if_else(!is.na(TRSTRESN), TRLNKID, NA_character_)
)
```

```{r, echo=FALSE}
dataset_vignette(tr, display_vars = exprs(USUBJID, TRLNKID, VISIT, LSEXP, LSASS))
```  

## Pre-processing of Input Records {#input}

The next step involves company-specific pre-processing of records required downstream prior to the parameter derivations, to ensure that all the associated variable processing is done once before any new parameters are derived. 

In order to calculate the sum of diameter of lesions, we need to 
ensure that lesions are grouped correctly,  how this is done will be based on your company specifications but individual lesions sizes could be grouped either by using the analysis date `ADT` or visit `AVISIT`. This is to ensure that each lesion size is only counted once.  

Below are some considerations depending on your analysis.


### Partial Date Imputation and Deriving `ADT`, `ADTF`, `AVISIT` etc

If your data collection allows for partial dates, you could apply a
company-specific imputation rule when deriving the analysis date `ADT` (you could also add further code to handle missing `ADT`, if this is a consideration).  

In the example below, we impute the missing day to the earliest possible date to derive the analysis date `ADT`, and then re-derive the analysis day `ADY`.  

```{r}
tr <- derive_vars_dt(
  tr,
  dtc = TRDTC,
  new_vars_prefix = "A",
  highest_imputation = "D",
  date_imputation = "first"
) %>%
  derive_vars_dy(
    reference_date = RANDDT,
    source_vars = exprs(ADT)
  )
```

```{r, echo=FALSE}
dataset_vignette(tr, display_vars = exprs(USUBJID, RANDDT, TRLNKID, TRDTC, ADT, ADTF, ADY))
```  

### Unscheduled visits

If you have data collected at unscheduled visits, these could be identified as
uniquely in your SDTM.  However, if this is not the case and your grouping lesions by visit then you will need to amend the below code to apply company specific algorithm to re-label unscheduled visits as per your specifications.

```{r}
tr <- mutate(
  tr,
  AVISIT = if_else(
    VISIT == "SCREENING",
    "BASELINE",
    VISIT
  ),
  AVISITN = if_else(
    AVISIT == "BASELINE",
    0,
    VISITNUM
  )
)
```


## Derive Parameters for Lesion Diameters (`LDIAMn` & `NLDIAMn`){#parameters}

Depending on your company specifications you may either create parameters for
Target Lesion diameter for lymph nodes (`LDIAMn`) and non-lymph nodes
(`NLDIAMn`) in `ADTR` or similar dataset.

The below examples show the set up of the parameter details and the analysis
value & flag (`AVAL`/`ANL01FL`) variables.
      
```{r}
tr <- mutate(tr, tmp_lesion_nr = str_sub(TRLNKID, 3))
adtr <- bind_rows(
  tr %>%
    filter(TRTESTCD == "LDIAM") %>%
    mutate(
      PARAMCD = paste0("LDIAM", tmp_lesion_nr),
      PARAM = paste("Target Lesion", tmp_lesion_nr, "Analysis Diameter")
    ),
  tr %>%
    filter(TRTESTCD == "LPERP") %>%
    mutate(
      PARAMCD = paste0("NLDIAM", tmp_lesion_nr),
      PARAM = paste("Target Lesion", tmp_lesion_nr, "Analysis Perpendicular")
    )
) %>%
  mutate(
    PARCAT1 = "Target Lesion(s)",
    PARCAT2 = "Investigator",
    PARCAT3 = "RECIST 1.1",
    AVAL = TRSTRESN,
    ANL01FL = if_else(!is.na(AVAL), "Y", NA_character_)
  ) %>%
  select(-tmp_lesion_nr)
```

```{r, echo=FALSE}
dataset_vignette(
  arrange(adtr, USUBJID, TRLNKID, AVISITN),
  display_vars = exprs(USUBJID, TRLNKID, AVISIT, PARAMCD, PARAM, AVAL, ANL01FL)
)
```

## Derive Parameter for Sum of Diameter and Analysis Flag `ANL01FL` {#sdiam}

Sum of Target Lesions Diameters is the sum of the diameters of both lymph and
non-lymph nodes (sum of analysis values `AVAL` from source observations `LDIAMn` & `LNDIAMn`).   

In this vignette, the sum of the diameters is calculated across all the
available lesion measurements, and we use the analysis flag `ANL01FL` to select
the records that should be used as part of the analysis based on RECIST 1.1.
Subsequently we then build on this analysis flag (see [`ANLzzFL`](#analflag)) to
demonstrate additional analysis flags for example purposes only. The analysis
flags therefore should be setup according to your company specifications.


In the example below, the lesion sizes are counted and grouped according to the
by variables specified, in this case we group by the visit (`AVISIT` and
`AVISITN`) to ensure that the correct lesion measurements are summed together.
If there are different analysis day/dates (`ADY`/`ADT`) associated with a
particular visit, we take the minimum day/date.

```{r}
adtr_sum <- derive_summary_records(
  dataset_add = adtr,
  by_vars = c(get_admiral_option("subject_keys"), adsl_vars, exprs(AVISIT, AVISITN)),
  filter_add = (str_starts(PARAMCD, "LDIAM") & TULOCGR1 == "NON-NODAL") |
    (str_starts(PARAMCD, "NLDIAM") & TULOCGR1 == "NODAL"),
  set_values_to = exprs(
    AVAL = sum(AVAL, na.rm = TRUE),
    ADY = min(ADY, na.rm = TRUE),
    ADT = min(ADT, na.rm = TRUE),
    PARAMCD = "SDIAM",
    PARAM = "Target Lesions Sum of Diameters by Investigator",
    PARCAT1 = "Target Lesion(s)",
    PARCAT2 = "Investigator",
    PARCAT3 = "RECIST 1.1"
  )
)
```

```{r, echo=FALSE}
dataset_vignette(
  adtr_sum %>%
    arrange(USUBJID, AVISITN) %>%
    select(USUBJID, PARAMCD, PARAM, AVISIT, AVAL, ADT, ADY, everything()),
  display_vars = exprs(USUBJID, PARAMCD, PARAM, AVISIT, AVAL, ADT, ADY)
)
```

The analysis flag `ANL01FL` flags the sum of diameters where the number of lesions assessed at baseline and at post-baseline match.  To assess whether the number of lesions expected and assessed match, you could compare lesion identifiers (`TR.TRLNKID`) or use the previous variables set up earlier to compare lesion expected (`LSEXP`) vs lesion assessed (`LSASS`) or check for target lesion response (from `RS`) = `NE`.   A target lesion response of `NE` means all lesions from baseline are not measured at that post-baseline visit.   

Lesions can split or merge, if this is a consideration then this needs to be taken care of when deciding on which algorithm to use to check that all lesions are measured at post-baseline.

In the below example, we use `derive_var_merged_summary()` function multiple
times in order to process additional variables for sum of diameter parameter,
and calculate the analysis flag (`ANL01FL`).

In the first step we concatenate lesions IDs measured `LSEXP` at baseline, and
merge in with the second step where we concatenate the lesions IDs assessed
`LSASS` at that visit record.  You can then compare `LSEXP` vs `LSASS` at each
visit.

In the final step, the analysis flag is set `ANL01FL = "Y"` where lesions
assessed at post-baseline match baseline.

```{r}
adtr_sum <- adtr_sum %>%
  derive_var_merged_summary(
    dataset_add = adtr,
    by_vars = get_admiral_option("subject_keys"),
    filter_add = AVISIT == "BASELINE" &
      ((str_starts(PARAMCD, "LDIAM") & TULOCGR1 == "NON-NODAL") |
        (str_starts(PARAMCD, "NLDIAM") & TULOCGR1 == "NODAL")),
    new_vars = exprs(LSEXP = paste(sort(TRLNKID), collapse = ", "))
  ) %>%
  derive_var_merged_summary(
    dataset_add = adtr,
    by_vars = c(get_admiral_option("subject_keys"), exprs(AVISIT)),
    filter_add = ((str_starts(PARAMCD, "LDIAM") & TULOCGR1 == "NON-NODAL") |
      (str_starts(PARAMCD, "NLDIAM") & TULOCGR1 == "NODAL")) & ANL01FL == "Y",
    new_vars = exprs(LSASS = paste(sort(TRLNKID), collapse = ", "))
  ) %>%
  mutate(
    ANL01FL = if_else(LSEXP == LSASS, "Y", NA_character_)
  )
```

## Derive Baseline (`ABLFL`, `BASE`) {#base}

These functions are available in `admiral`, below examples show its usage for
`ADTR`. Here we create baseline record flag `ABLFL` by selecting the last record
prior to analysis day 1, and then derive the baseline value `BASE`. If in a
particular study/or subject, baseline is defined differently, you need to make sure to
update appropriate arguments.

```{r}
adtr_sum <- adtr_sum %>%
  restrict_derivation(
    derivation = derive_var_extreme_flag,
    args = params(
      by_vars = get_admiral_option("subject_keys"),
      order = exprs(ADY),
      new_var = ABLFL,
      mode = "last"
    ),
    filter = ADY <= 1
  ) %>%
  derive_var_base(
    by_vars = get_admiral_option("subject_keys")
  )
```

## Derive `NADIR` {#nadir}

Once we have Sum of Target Lesions Diameters parameter (`SDIAM`), the `NADIR` is
set to the lowest sum of diameters in Analysis Value (`AVAL`) before the current
observation, where all lesions were assessed (i.e. `ANL01F == "Y"`).  The first
observations after baseline (depending on how you have defined baseline earlier)
is set to the baseline value.

```{r}
adtr_sum <- adtr_sum %>%
  derive_vars_joined(
    dataset_add = adtr_sum,
    by_vars = get_admiral_option("subject_keys"),
    order = exprs(AVAL),
    new_vars = exprs(NADIR = AVAL),
    join_vars = exprs(ADY),
    join_type = "all",
    filter_add = ANL01FL == "Y",
    filter_join = ADY.join < ADY,
    mode = "first",
    check_type = "none"
  )
```

```{r, echo=FALSE}
dataset_vignette(
  adtr_sum %>%
    arrange(USUBJID, AVISITN) %>%
    select(USUBJID, PARAMCD, AVISIT, ADY, AVAL, NADIR, everything()),
  display_vars = exprs(USUBJID, PARAMCD, AVISIT, ADY, AVAL, NADIR)
)
```  

## Derive Change from Baseline (`CHG`, `PCHG`) {#chg}

These functions are available in admiral, below examples show its usage for
`ADTR`.  We calculate the change from baseline (`CHG`, `CHGNAD`) and percentage
change from baseline (`PCHG` , `PCHGNAD`) for the parameter `SDIAM` (using
`AVAL - BASE`) the variable `NADIR` (using `AVAL - NADIR`).

```{r}
adtr_sum <- adtr_sum %>%
  derive_var_chg() %>%
  derive_var_pchg() %>%
  mutate(
    CHGNAD = AVAL - NADIR,
    PCHGNAD = if_else(NADIR == 0, NA_real_, 100 * CHGNAD / NADIR)
  )
```

```{r, echo=FALSE}
dataset_vignette(
  arrange(adtr_sum, USUBJID, AVISITN),
  display_vars = exprs(USUBJID, AVISIT, AVAL, NADIR, BASE, CHG, PCHG, CHGNAD, PCHGNAD)
)
```  

## Derive Additional Flag Variables {#addflag}

Depending on the analysis flags you require, you may want to create additional
flag variables that will then aid setting up any additional analysis flag
variables defined as per your company specifications.

### Derive `PDFL`

In this example, we want to flag when a patient has had `PD`.  This could be done in a number of ways:

#### 1. Take the date of `PD` from `ADRS`

```{r eval=TRUE}
adtr_sum <- adtr_sum %>%
  derive_var_merged_exist_flag(
    dataset_add = adrs,
    filter_add = PARAMCD == "PD",
    by_vars = c(get_admiral_option("subject_keys"), exprs(ADT)),
    new_var = PDFL,
    condition = AVALC == "Y"
  )
```

```{r, echo=FALSE}
dataset_vignette(
  arrange(adtr_sum, USUBJID, AVISITN),
  display_vars = exprs(USUBJID, AVISIT, AVAL, NADIR, CHGNAD, PCHGNAD, PDFL)
)

adtr_sum <- select(adtr_sum, -PDFL)
```

#### 2. Take the first date when the overall response is `PD` from `RS` 

```{r eval=TRUE}
adtr_sum <- adtr_sum %>%
  derive_var_merged_exist_flag(
    dataset_add = derive_vars_dt(
      rs,
      dtc = RSDTC,
      new_vars_prefix = "A",
      highest_imputation = "D",
      flag_imputation = "none"
    ),
    filter_add = RSTESTCD == "OVRLRESP" & RSEVAL == "INVESTIGATOR",
    by_vars = c(get_admiral_option("subject_keys"), exprs(ADT)),
    new_var = PDFL,
    condition = RSSTRESC == "PD"
  )
```

```{r, echo=FALSE}
dataset_vignette(
  arrange(adtr_sum, USUBJID, AVISITN),
  display_vars = exprs(USUBJID, AVISIT, AVAL, NADIR, CHGNAD, PCHGNAD, PDFL)
)

adtr_sum <- select(adtr_sum, -PDFL)
```

#### 3. Target lesion response of no `CR` at the current observation visit and but a `CR` response at the `NADIR` observation

```{r}
adtr_sum <- adtr_sum %>% mutate(
  CRFL = if_else(AVAL == 0 & ANL01FL == "Y", "Y", NA_character_),
  CRNFL = if_else(NADIR == 0, "Y", NA_character_),
  PDFL = if_else(is.na(CRFL) & CRNFL == "Y", "Y", NA_character_)
)
```

```{r, echo=FALSE}
dataset_vignette(
  arrange(adtr_sum, USUBJID, AVISITN),
  display_vars = exprs(USUBJID, AVISIT, AVAL, NADIR, CHGNAD, PCHGNAD, PDFL)
)

adtr_sum <- select(adtr_sum, -PDFL, -CRFL, -CRNFL)
```


#### 4. Calculate from source data

The derivation for `PD` for target lesions is based on the calculated sum of
diameters of the assessed lesions results in a calculated Percent Change from
NADIR >=20% (`PCHGNAD`) and a calculated Change from NADIR >=5mm (`CHGNAD`).

In this example, `CRFL` flags where the patient has had Complete Response, and
`CRNFL`, flags where Complete response contributed to the `NADIR` calculation.
    
```{r}
adtr_sum <- adtr_sum %>% mutate(
  CRFL = if_else(AVAL == 0 & ANL01FL == "Y", "Y", NA_character_),
  CRNFL = if_else(NADIR == 0, "Y", NA_character_)
)
```    

```{r}
adtr_sum <- adtr_sum %>%
  mutate(
    PDFL = if_else(
      PCHGNAD >= 20 & CHGNAD >= 5 | is.na(CRFL) & CRNFL == "Y",
      "Y",
      NA_character_
    )
  )
```

```{r, echo=FALSE}
dataset_vignette(
  arrange(adtr_sum, USUBJID, AVISITN),
  display_vars = exprs(USUBJID, AVISIT, AVAL, NADIR, CHGNAD, PCHGNAD, PDFL)
)
```
  

## Derive Analysis Flags `ANLzzFL` {#analflag}

Based on your specifications, additional analysis flags (`ANLzzFL`) could be set up in `ADTR`. Below are some examples which use the `ANL01FL` flag derived earlier for the sum of diameter parameter (`SDIAM`) as a basis.

Analysis 02 Flag (`ANL02FL`) only includes visits (where analysis date is e.g after randomization date (`RANDDT`)) with the lowest `NADIR` (lowest sum of diameter) so maximum tumor shrinkage by using the first non-missing lowest percentage change from baseline (`PCHG`).

```{r}
adtr_sum <- adtr_sum %>%
  derive_var_relative_flag(
    by_vars = get_admiral_option("subject_keys"),
    order = exprs(ADT),
    new_var = POSTRNDFL,
    condition = ADT > RANDDT,
    mode = "first",
    selection = "after",
    inclusive = TRUE,
    flag_no_ref_groups = FALSE
  ) %>%
  restrict_derivation(
    derivation = derive_var_extreme_flag,
    args = params(
      by_vars = get_admiral_option("subject_keys"),
      new_var = ANL02FL,
      order = exprs(PCHG),
      mode = "first",
      check_type = "none"
    ),
    filter = ANL01FL == "Y" & POSTRNDFL == "Y"
  ) %>%
  select(-POSTRNDFL)
```

```{r, echo=FALSE}
dataset_vignette(
  adtr_sum %>%
    arrange(USUBJID, AVISITN) %>%
    select(USUBJID, AVISIT, AVAL, NADIR, PCHG, ANL01FL, ANL02FL, everything()),
  display_vars = exprs(USUBJID, AVISIT, AVAL, NADIR, PCHG, ANL01FL, ANL02FL)
)
```  

Analysis 03 flag (`ANL03FL`) includes all sum of diameters where all lesions
were assessed (where `ANL01FL == "Y"`) until the patient has `PD`, indicated by
using the `PDFL` variable.

```{r}
adtr_sum <- adtr_sum %>%
  restrict_derivation(
    derivation = derive_var_relative_flag,
    args = params(
      by_vars = get_admiral_option("subject_keys"),
      new_var = ANL03FL,
      condition = PDFL == "Y",
      order = exprs(ADY),
      mode = "first",
      selection = "before",
      inclusive = FALSE
    ),
    filter = ANL01FL == "Y" | PDFL == "Y"
  )
```

```{r, echo=FALSE}
dataset_vignette(
  adtr_sum %>%
    arrange(USUBJID, AVISITN) %>%
    select(USUBJID, AVISIT, AVAL, NADIR, PDFL, ANL01FL, ANL02FL, ANL03FL, everything()),
  display_vars = exprs(USUBJID, AVISIT, AVAL, NADIR, PDFL, ANL01FL, ANL03FL)
)
```  

Analysis 04 flag (`ANL04FL`) includes all sum of diameters where all lesions
were assessed (where `ANL01FL == "Y"`) and additionally those indicating `PD`
using the `PDFL` variable.
  
```{r}
adtr_sum <- adtr_sum %>%
  mutate(
    ANL04FL = if_else(ANL01FL == "Y" | PDFL == "Y", "Y", NA_character_)
  )
```

```{r, echo=FALSE}
dataset_vignette(
  adtr_sum %>%
    arrange(USUBJID, AVISITN) %>%
    select(USUBJID, AVISIT, AVAL, NADIR, PDFL, ANL01FL, ANL02FL, ANL03FL, ANL04FL, everything()),
  display_vars = exprs(USUBJID, AVISIT, AVAL, NADIR, PDFL, ANL01FL, ANL04FL)
)
```  


```{r}
adtr <- bind_rows(adtr, adtr_sum)
```

## Derive Analysis Sequence Number (`ASEQ`) {#aseq}

The `{admiral}` function `admiral::derive_var_obs_number()` can be used to
derive `ASEQ`:

```{r eval=TRUE}
adtr <- adtr %>%
  derive_var_obs_number(
    by_vars = get_admiral_option("subject_keys"),
    order = exprs(PARAMCD, AVISITN, TRSEQ),
    check_type = "error"
  )
```

```{r, echo=FALSE}
dataset_vignette(
  arrange(adtr, USUBJID, ASEQ),
  display_vars = exprs(USUBJID, PARAMCD, AVISIT, ASEQ, AVAL)
)
```  


## Add `ADSL` Variables {#adslvars}
  
If needed, the other `ADSL` variables can now be added. List of `ADSL`
variables already merged held in vector `adsl_vars`.

```{r eval=TRUE}
adtr <- adtr %>%
  derive_vars_merged(
    dataset_add = select(adsl, !!!negate_vars(adsl_vars)),
    by_vars = get_admiral_option("subject_keys")
  )
```

```{r, eval=TRUE, echo=FALSE}
dataset_vignette(
  adtr,
  display_vars = exprs(USUBJID, RFSTDTC, RFENDTC, DTHDTC, DTHFL, AGE, AGEU)
)
```


# Example Script {#example}

ADaM | Sample Code
---- | --------------
`ADTR` | `admiral::use_ad_template("ADTR", package = "admiralonco")`