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

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

library(admiraldev)
```

# Introduction

This article describes creating questionnaire ADaMs. Although questionnaire data
is collected in a single SDTM dataset (`QS`), usually it does not make sense to
create a single `ADQS` dataset for all questionnaire analyses. For example, a
univariate analysis of scores by visit requires different variables than a
time-to-event analysis. Therefore this vignette does not provide a programming
workflow for a complete dataset, but provides examples for deriving common types
of questionnaire parameters.

At the moment, `{admiral}` does not provide functions or metadata for specific
questionnaires nor functionality for handling the vast amount of questionnaires
and related parameters, e.g. a metadata structure for storing parameter
definitions and functions for reading such metadata. We plan to provide it in
future releases.

**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(dplyr)
library(tidyr)
library(tibble)
library(admiral)
```

## Example Data

In this vignette we use the example data from the CDISC ADaM Supplements
([Generalized Anxiety Disorder 7-Item Version 2
(GAD-7)](https://www.cdisc.org/standards/foundational/qrs/generalized-anxiety-disorder-7-item-version-2-0),
[Geriatric Depression Scale Short Form
(GDS-SF)](https://www.cdisc.org/standards/foundational/qrs/geriatric-depression-scale-short-form-0))[^1]:

[^1]: The example `QS` data (`example_qs`) is included in the admiral package.

```{r}
qs <- admiral::example_qs
```

```{r echo=FALSE}
dataset_vignette(qs)
```

```{r}
adsl <- tribble(
  ~STUDYID, ~USUBJID, ~SITEID, ~ITTFL, ~TRTSDT,                      ~DTHCAUS,
  "STUDYX",  "P0001",     13L,    "Y", lubridate::ymd("2012-11-16"), NA_character_,
  "STUDYX",  "P0002",     11L,    "Y", lubridate::ymd("2012-11-16"), "PROGRESSIVE DISEASE"
)
```
```{r echo=FALSE}
dataset_vignette(adsl)
```
# Original Items

The original items, i.e. the answers to the questionnaire questions, can be
handled in the same way as in a [BDS finding ADaM](bds_finding.html). For
example:

```{r eval=TRUE}
adqs <- qs %>%
  # Add ADSL variables
  derive_vars_merged(
    dataset_add = adsl,
    new_vars = exprs(TRTSDT, DTHCAUS),
    by_vars = exprs(STUDYID, USUBJID)
  ) %>%
  # Add analysis parameter variables
  mutate(
    PARAMCD = QSTESTCD,
    PARAM = QSTEST,
    PARCAT1 = QSCAT,
    AVALC = QSORRES,
    AVAL = QSSTRESN
  ) %>%
  # Add timing variables
  derive_vars_dt(new_vars_prefix = "A", dtc = QSDTC) %>%
  derive_vars_dy(reference_date = TRTSDT, source_vars = exprs(ADT)) %>%
  mutate(
    AVISIT = if_else(ADT <= TRTSDT, "BASELINE", VISIT),
    AVISITN = if_else(ADT <= TRTSDT, 0, VISITNUM)
  )
```

```{r echo=FALSE}
dataset_vignette(
  arrange(adqs, USUBJID, PARCAT1, ADY, PARAMCD),
  display_vars = exprs(USUBJID, PARAMCD, PARAM, PARCAT1, AVALC, AVAL, ADY, AVISIT)
)
```

We handle unscheduled visits as normal visits. For deriving visits based on
time-windows, see [Visit and Period Variables](visits_periods.html#visits). And
for flagging values to be used for analysis, see `derive_var_extreme_flag()`.

# Transformed Items

Please note that in the example data, the numeric values of the answers are
mapped in SDTM (`QSSTRESN`) such that they can be used for deriving scores.
Depending on the question, `QSORRES == "YES"` is mapped to `QSSTRESN = 0` or
`QSSTRESN = 1`. If the `QSSTRESN` values are not ready to be used for deriving
scores and require transformation, it is recommended that `QSSTRESN` is kept in
the ADaM dataset for traceability, and the transformed value is stored in
`AVAL`, since that's what will be used for the score calculation.

It may also be necessary to transform the range of the numeric values of the
original items. For example if a scale should be derived as the average but the
range of the contributing items varies. In this case the values could be
linearly transformed to a unified range like `[0, 100]`. The computation
function `transform_range()` can be used for the transformation.

# Scales and Scores

Scales and Scores are often derived as the sum or the average across a subset of
the items. For the GAD-7 questionnaire, the total score is derived as the sum.
The `derive_summary_records()` function with `sum()` can be used to derive it as
a new parameter. For selecting the parameters to be summarized, regular
expressions like in the example below may be helpful. In the example we derive a
separate ADaM dataset for each questionnaire. Depending on the analysis needs, it
is also possible that an ADaM contains more than one questionnaire or all
questionnaires.
```{r eval=TRUE}
adgad7 <- adqs %>%
  # Select records to keep in the GAD-7 ADaM
  filter(PARCAT1 == "GAD-7 V2") %>%
  derive_summary_records(
    dataset = .,
    dataset_add = .,
    by_vars = exprs(STUDYID, USUBJID, AVISIT, ADT, ADY, TRTSDT, DTHCAUS),
    # Select records contributing to total score
    filter_add = str_detect(PARAMCD, "GAD020[1-7]"),
    set_values_to = exprs(
      AVAL = sum(AVAL, na.rm = TRUE),
      PARAMCD = "GAD02TS",
      PARAM = "GAD02-Total Score - Analysis"
    )
  )
```

```{r echo=FALSE}
dataset_vignette(
  arrange(adgad7, USUBJID, ADY, PARAMCD),
  display_vars = exprs(USUBJID, PARAMCD, PARAM, AVAL, ADY, AVISIT)
)
```

For the GDS-SF questionnaire, the total score is defined as the average of the
item values transformed to the range [0, 15] and rounded up to the next integer.
If more than five items are missing, the total score is considered as missing.
This parameter can be derived by `compute_scale()` and
`derive_summary_records()`:
```{r eval=TRUE}
adgdssf <- adqs %>%
  # Select records to keep in the GDS-SF ADaM
  filter(PARCAT1 == "GDS SHORT FORM") %>%
  derive_summary_records(
    dataset = .,
    dataset_add = .,
    by_vars = exprs(STUDYID, USUBJID, AVISIT, ADT, ADY, TRTSDT, DTHCAUS),
    # Select records contributing to total score
    filter_add = str_detect(PARAMCD, "GDS02[01][0-9]"),
    set_values_to = exprs(
      AVAL = compute_scale(
        AVAL,
        source_range = c(0, 1),
        target_range = c(0, 15),
        min_n = 10
      ) %>%
        ceiling(),
      PARAMCD = "GDS02TS",
      PARAM = "GDS02- Total Score - Analysis"
    )
  )
```

```{r echo=FALSE}
dataset_vignette(
  arrange(adgdssf, USUBJID, ADY, PARAMCD),
  display_vars = exprs(USUBJID, PARAMCD, PARAM, AVAL, ADY, AVISIT)
)
```

After deriving the scores by visit, the baseline and change from baseline
variables can be derived:
```{r eval=TRUE}
adgdssf <- adgdssf %>%
  # Flag baseline records (last before treatement start)
  restrict_derivation(
    derivation = derive_var_extreme_flag,
    args = params(
      by_vars = exprs(STUDYID, USUBJID, PARAMCD),
      order = exprs(ADT),
      new_var = ABLFL,
      mode = "last"
    ),
    filter = !is.na(AVAL) & ADT <= TRTSDT
  ) %>%
  # Derive baseline and change from baseline variables
  derive_var_base(
    by_vars = exprs(STUDYID, USUBJID, PARAMCD),
    source_var = AVAL,
    new_var = BASE
  ) %>%
  # Calculate CHG for post-baseline records
  # The decision on how to populate pre-baseline and baseline values of CHG is left to producer choice
  restrict_derivation(
    derivation = derive_var_chg,
    filter = AVISITN > 0
  ) %>%
  # Calculate PCHG for post-baseline records
  # The decision on how to populate pre-baseline and baseline values of PCHG is left to producer choice
  restrict_derivation(
    derivation = derive_var_pchg,
    filter = AVISITN > 0
  ) %>%
  # Derive sequence number (Optional Variable)
  derive_var_obs_number(
    by_vars = exprs(STUDYID, USUBJID),
    order = exprs(PARAMCD, ADT),
    check_type = "error"
  )
```

```{r echo=FALSE}
dataset_vignette(
  adgdssf,
  display_vars = exprs(USUBJID, PARAMCD, PARAM, AVISIT, AVAL, BASE, CHG, PCHG)
)
```

# Time to Deterioration/Improvement {#timetodeterioration}

As time to event parameters require specific variables like `CNSR`, `STARTDT`,
and `EVNTDESC`, it makes sense to create a separate time to event dataset for
them. However, it might be useful to create flags or categorization variables in
`ADQS`. For example:
```{r}
# Create AVALCATy lookup table
avalcat_lookup <- exprs(
  ~PARAMCD, ~condition, ~AVALCAT1, ~AVALCA1N,
  "GDS02TS", AVAL <= 5, "Normal", 0L,
  "GDS02TS", AVAL <= 10 & AVAL > 5, "Possible Depression", 1L,
  "GDS02TS", AVAL > 10, "Likely Depression", 2L
)
# Create CHGCAT1 lookup table
chgcat_lookup <- exprs(
  ~condition, ~CHGCAT1,
  AVALCA1N > BASECA1N, "WORSENED",
  AVALCA1N == BASECA1N, "NO CHANGE",
  AVALCA1N < BASECA1N, "IMPROVED"
)

adgdssf <- adgdssf %>%
  derive_vars_cat(
    definition = avalcat_lookup,
    by_vars = exprs(PARAMCD)
  ) %>%
  derive_var_base(
    by_vars = exprs(STUDYID, USUBJID, PARAMCD),
    source_var = AVALCAT1,
    new_var = BASECAT1
  ) %>%
  derive_var_base(
    by_vars = exprs(STUDYID, USUBJID, PARAMCD),
    source_var = AVALCA1N,
    new_var = BASECA1N
  ) %>%
  derive_vars_cat(
    definition = chgcat_lookup
  )
```

```{r echo=FALSE}
dataset_vignette(
  arrange(adgdssf, USUBJID, desc(PARAMCD), ADY),
  display_vars = exprs(USUBJID, PARAMCD, PARAM, AVISIT, AVAL, AVALCAT1, CHGCAT1)
)
```

Then a time to deterioration parameter can be derived by:
```{r}
# Define event
deterioration_event <- event_source(
  dataset_name = "adqs",
  filter = PARAMCD == "GDS02TS" & CHGCAT1 == "WORSENED",
  date = ADT,
  set_values_to = exprs(
    EVNTDESC = "DEPRESSION WORSENED",
    SRCDOM = "ADQS",
    SRCVAR = "ADT",
    SRCSEQ = ASEQ
  )
)

# Define censoring at last assessment
last_valid_assessment <- censor_source(
  dataset_name = "adqs",
  filter = PARAMCD == "GDS02TS" & !is.na(CHGCAT1),
  date = ADT,
  set_values_to = exprs(
    EVNTDESC = "LAST ASSESSMENT",
    SRCDOM = "ADQS",
    SRCVAR = "ADT",
    SRCSEQ = ASEQ
  )
)

# Define censoring at treatment start (for subjects without assessment)
start <- censor_source(
  dataset_name = "adsl",
  date = TRTSDT,
  set_values_to = exprs(
    EVNTDESC = "TREATMENT START",
    SRCDOM = "ADSL",
    SRCVAR = "TRTSDT"
  )
)

adgdstte <- derive_param_tte(
  dataset_adsl = adsl,
  source_datasets = list(adsl = adsl, adqs = adgdssf),
  start_date = TRTSDT,
  event_conditions = list(deterioration_event),
  censor_conditions = list(last_valid_assessment, start),
  set_values_to = exprs(
    PARAMCD = "TTDEPR",
    PARAM = "Time to depression"
  )
) %>%
  derive_vars_duration(
    new_var = AVAL,
    start_date = STARTDT,
    end_date = ADT
  )
```

```{r echo=FALSE}
dataset_vignette(
  adgdstte,
  display_vars = exprs(USUBJID, PARAMCD, PARAM, AVAL, CNSR, EVNTDESC, SRCDOM, SRCVAR)
)
```

# Time to Confirmed/Definitive Deterioration/Improvement 

The derivation of confirmed/definitive deterioration/improvement parameters is
very similar to the unconfirmed deterioration parameters except that the event
is not based on `CHGCATy`, but on a confirmation flag variable. This confirmation
flag can be derived by `derive_var_joined_exist_flag()`. For example, flagging
deteriorations, which are confirmed by a second assessment at least seven days
later:
```{r}
adgdssf <- adgdssf %>%
  derive_var_joined_exist_flag(
    dataset_add = adgdssf,
    by_vars = exprs(USUBJID, PARAMCD),
    order = exprs(ADT),
    new_var = CDETFL,
    join_vars = exprs(CHGCAT1, ADY),
    join_type = "after",
    filter_join = CHGCAT1 == "WORSENED" &
      CHGCAT1.join == "WORSENED" &
      ADY.join >= ADY + 7
  )
```

```{r echo=FALSE}
dataset_vignette(
  arrange(adgdssf, USUBJID, desc(PARAMCD), ADY),
  display_vars = exprs(USUBJID, PARAMCD, PARAM, ADY, CHGCAT1, CDETFL)
)
```

For flagging deteriorations at two consecutive assessments or considering death
due to progression at the last assessment as confirmation, the `tmp_obs_nr_var`
argument is helpful:
```{r}
# Flagging deterioration at two consecutive assessments
adgdssf <- adgdssf %>%
  derive_var_joined_exist_flag(
    dataset_add = adgdssf,
    by_vars = exprs(USUBJID, PARAMCD),
    order = exprs(ADT),
    new_var = CONDETFL,
    join_vars = exprs(CHGCAT1),
    join_type = "after",
    tmp_obs_nr_var = tmp_obs_nr,
    filter_join = CHGCAT1 == "WORSENED" &
      CHGCAT1.join == "WORSENED" &
      tmp_obs_nr.join == tmp_obs_nr + 1
  ) %>%
  # Flagging deterioration confirmed by
  # - a second deterioration at least 7 days later or
  # - deterioration at the last assessment and death due to progression
  derive_var_joined_exist_flag(
    .,
    dataset_add = .,
    by_vars = exprs(USUBJID, PARAMCD),
    order = exprs(ADT),
    new_var = CDTDTHFL,
    join_vars = exprs(CHGCAT1, ADY),
    join_type = "all",
    tmp_obs_nr_var = tmp_obs_nr,
    filter_join = CHGCAT1 == "WORSENED" & (
      CHGCAT1.join == "WORSENED" & ADY.join >= ADY + 7 |
        tmp_obs_nr == max(tmp_obs_nr.join) & DTHCAUS == "PROGRESSIVE DISEASE")
  )
```

```{r echo=FALSE}
dataset_vignette(
  arrange(adgdssf, USUBJID, desc(PARAMCD), ADY),
  display_vars = exprs(USUBJID, PARAMCD, PARAM, ADY, CHGCAT1, CONDETFL, CDTDTHFL)
)
```

For definitive deterioration (deterioration at all following assessments),
parameter summary functions like `all()` can be used in the filter condition:

```{r}
adgdssf <- adgdssf %>%
  derive_var_joined_exist_flag(
    dataset_add = adgdssf,
    by_vars = exprs(USUBJID, PARAMCD),
    order = exprs(ADT),
    new_var = DEFDETFL,
    join_vars = exprs(CHGCAT1),
    join_type = "after",
    filter_join = CHGCAT1 == "WORSENED" & all(CHGCAT1.join == "WORSENED")
  )
```

```{r echo=FALSE}
dataset_vignette(
  arrange(adgdssf, USUBJID, desc(PARAMCD), ADY),
  display_vars = exprs(USUBJID, PARAMCD, PARAM, ADY, CHGCAT1, DEFDETFL)
)
```

The time-to-event parameter can be derived in the same way as for the
unconfirmed parameters (see [Time to
Deterioration/Improvement](#timetodeterioration)).

# Worst/Best Answer

This class of parameters can be used when the worst answer of a set of yes/no
answers should be selected. For example, if yes/no answers for "No sleep",
"Waking up more than three times", "More than 30 minutes to fall asleep" are
collected, a parameter for the worst sleeping problems could be derived. In the
example, "no sleeping problems" is assumed if all questions were answered with
"no".
```{r}
adsp <- adqs %>%
  filter(PARCAT1 == "SLEEPING PROBLEMS") %>%
  derive_extreme_event(
    by_vars = exprs(USUBJID, AVISIT),
    tmp_event_nr_var = event_nr,
    order = exprs(event_nr, ADY, QSSEQ),
    mode = "first",
    events = list(
      event(
        condition = PARAMCD == "SP0101" & AVALC == "YES",
        set_values_to = exprs(
          AVALC = "No sleep",
          AVAL = 1
        )
      ),
      event(
        condition = PARAMCD == "SP0102" & AVALC == "YES",
        set_values_to = exprs(
          AVALC = "Waking up more than three times",
          AVAL = 2
        )
      ),
      event(
        condition = PARAMCD == "SP0103" & AVALC == "YES",
        set_values_to = exprs(
          AVALC = "More than 30 mins to fall asleep",
          AVAL = 3
        )
      ),
      event(
        condition = all(AVALC == "NO"),
        set_values_to = exprs(
          AVALC = "No sleeping problems",
          AVAL = 4
        )
      ),
      event(
        condition = TRUE,
        set_values_to = exprs(
          AVALC = "Missing",
          AVAL = 99
        )
      )
    ),
    set_values_to = exprs(
      PARAMCD = "SP01WSP",
      PARAM = "Worst Sleeping Problems"
    )
  )
```

```{r echo=FALSE}
dataset_vignette(
  arrange(adsp, USUBJID, ADY, PARAMCD),
  display_vars = exprs(USUBJID, PARAMCD, PARAM, AVISIT, AVALC)
)
```

# Completion

Parameters for completion, like "at least 90% of the questions were answered", can
be derived by `derive_summary_records()`.
```{r}
adgdssf <- adgdssf %>%
  derive_summary_records(
    dataset_add = adgdssf,
    filter_add = str_detect(PARAMCD, "GDS02[01][0-9]"),
    by_vars = exprs(USUBJID, AVISIT),
    set_values_to = exprs(
      AVAL = sum(!is.na(AVAL)) / 15 >= 0.9,
      PARAMCD = "COMPL90P",
      PARAM = "Completed at least 90% of questions?",
      AVALC = if_else(AVAL == 1, "YES", "NO")
    )
  )
```

```{r echo=FALSE}
dataset_vignette(
  arrange(adgdssf, USUBJID, PARAMCD, ADY),
  display_vars = exprs(USUBJID, PARAMCD, PARAM, AVISIT, AVALC)
)
```

Please note that the denominator may depend on the answers of some of the
questions. For example, a given questionnaire might direct someone to go from
question #4 directly to question #8 based on their response to question #4,
because questions #5, #6 and #7 would not apply in that case.

If missed visits need to be taken into account, the expected records can be
added to the input dataset by calling `derive_expected_records()`:

```{r}
# Create dataset with expected visits and parameters (GDS0201 - GDS0215)
parm_visit_ref <- crossing(
  tribble(
    ~AVISIT,    ~AVISITN,
    "BASELINE",        0,
    "VISIT 2",         2,
    "VISIT 3",         3,
    "VISIT 4",         4,
    "VISIT 5",         5
  ),
  tibble(PARAMCD = sprintf("GDS02%02d", seq(1, 15)))
)

adgdssf <- adgdssf %>%
  derive_expected_records(
    dataset_ref = parm_visit_ref,
    by_vars = exprs(USUBJID),
    set_values_to = exprs(
      filled_in = 1
    )
  ) %>%
  derive_summary_records(
    dataset = .,
    dataset_add = .,
    filter_add = str_detect(PARAMCD, "GDS02[01][0-9]"),
    by_vars = exprs(USUBJID, AVISIT),
    set_values_to = exprs(
      AVAL = all(!is.na(AVAL)),
      PARAMCD = "COMPLALL",
      PARAM = "Completed all questions?",
      AVALC = if_else(AVAL == 1, "YES", "NO")
    )
  ) %>%
  filter(is.na(filled_in)) %>%
  select(-filled_in)
```

```{r echo=FALSE}
dataset_vignette(
  arrange(adgdssf, USUBJID, PARAMCD, ADY),
  display_vars = exprs(USUBJID, PARAMCD, PARAM, AVISIT, AVALC)
)
```
