# Creating Questionnaire ADaMs

# 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.

    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), Geriatric Depression Scale Short Form (GDS-SF))1:

    qs <- admiral::example_qs

STUDYID | USUBJID | QSSEQ | QSTESTCD | QSTEST | VISIT | VISITNUM | QSDTC | QSORRES | QSCAT | QSSTRESN
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
STUDYX | P0001 | 1 | GAD0201 | GAD02-Feeling Nervous Anxious or On Edge | VISIT 1 | 1 | 2012-11-16 | More than half the days | GAD-7 V2 | 2
STUDYX | P0001 | 2 | GAD0202 | GAD02-Not Able to Stop/Control Worrying | VISIT 1 | 1 | 2012-11-16 | More than half the days | GAD-7 V2 | 2
STUDYX | P0001 | 3 | GAD0203 | GAD02-Worrying Too Much About Things | VISIT 1 | 1 | 2012-11-16 | More than half the days | GAD-7 V2 | 2
STUDYX | P0001 | 4 | GAD0204 | GAD02-Trouble Relaxing | VISIT 1 | 1 | 2012-11-16 | More than half the days | GAD-7 V2 | 2
STUDYX | P0001 | 5 | GAD0205 | GAD02-Being Restless Hard to Sit Still | VISIT 1 | 1 | 2012-11-16 | Nearly every day | GAD-7 V2 | 3
STUDYX | P0001 | 6 | GAD0206 | GAD02-Becoming Easily Annoyed/Irritable | VISIT 1 | 1 | 2012-11-16 | Nearly every day | GAD-7 V2 | 3
STUDYX | P0001 | 7 | GAD0207 | GAD02-Feel Afraid/Something Awful Happen | VISIT 1 | 1 | 2012-11-16 | Several days | GAD-7 V2 | 1
STUDYX | P0001 | 8 | GAD0208 | GAD02-Total Score | VISIT 1 | 1 | 2012-11-16 | 15 | GAD-7 V2 | 15
STUDYX | P0001 | 9 | GAD0201 | GAD02-Feeling Nervous Anxious or On Edge | UNSCHEDULED VISIT 5.01 | 501 | 2013-04-15 | More than half the days | GAD-7 V2 | 2
STUDYX | P0001 | 10 | GAD0202 | GAD02-Not Able to Stop/Control Worrying | UNSCHEDULED VISIT 5.01 | 501 | 2013-04-15 | More than half the days | GAD-7 V2 | 2

    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"
    )

STUDYID | USUBJID | SITEID | ITTFL | TRTSDT | DTHCAUS
--- | --- | --- | --- | --- | ---
STUDYX | P0001 | 13 | Y | 2012-11-16 | NA
STUDYX | P0002 | 11 | Y | 2012-11-16 | PROGRESSIVE DISEASE

# 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. For example:

    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)
      )

USUBJID | PARAMCD | PARAM | PARCAT1 | AVALC | AVAL | ADY | AVISIT
--- | --- | --- | --- | --- | --- | --- | ---
P0001 | GAD0201 | GAD02-Feeling Nervous Anxious or On Edge | GAD-7 V2 | More than half the days | 2 | 1 | BASELINE
P0001 | GAD0202 | GAD02-Not Able to Stop/Control Worrying | GAD-7 V2 | More than half the days | 2 | 1 | BASELINE
P0001 | GAD0203 | GAD02-Worrying Too Much About Things | GAD-7 V2 | More than half the days | 2 | 1 | BASELINE
P0001 | GAD0204 | GAD02-Trouble Relaxing | GAD-7 V2 | More than half the days | 2 | 1 | BASELINE
P0001 | GAD0205 | GAD02-Being Restless Hard to Sit Still | GAD-7 V2 | Nearly every day | 3 | 1 | BASELINE
P0001 | GAD0206 | GAD02-Becoming Easily Annoyed/Irritable | GAD-7 V2 | Nearly every day | 3 | 1 | BASELINE
P0001 | GAD0207 | GAD02-Feel Afraid/Something Awful Happen | GAD-7 V2 | Several days | 1 | 1 | BASELINE
P0001 | GAD0208 | GAD02-Total Score | GAD-7 V2 | 15 | 15 | 1 | BASELINE
P0001 | GAD0201 | GAD02-Feeling Nervous Anxious or On Edge | GAD-7 V2 | More than half the days | 2 | 151 | UNSCHEDULED VISIT 5.01
P0001 | GAD0202 | GAD02-Not Able to Stop/Control Worrying | GAD-7 V2 | More than half the days | 2 | 151 | UNSCHEDULED VISIT 5.01

We handle unscheduled visits as normal visits. For deriving visits based on time-windows, see Visit and Period Variables. 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.

    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"
        )
      )

USUBJID | PARAMCD | PARAM | AVAL | ADY | AVISIT
--- | --- | --- | --- | --- | ---
P0001 | GAD0201 | GAD02-Feeling Nervous Anxious or On Edge | 2 | 1 | BASELINE
P0001 | GAD0202 | GAD02-Not Able to Stop/Control Worrying | 2 | 1 | BASELINE
P0001 | GAD0203 | GAD02-Worrying Too Much About Things | 2 | 1 | BASELINE
P0001 | GAD0204 | GAD02-Trouble Relaxing | 2 | 1 | BASELINE
P0001 | GAD0205 | GAD02-Being Restless Hard to Sit Still | 3 | 1 | BASELINE
P0001 | GAD0206 | GAD02-Becoming Easily Annoyed/Irritable | 3 | 1 | BASELINE
P0001 | GAD0207 | GAD02-Feel Afraid/Something Awful Happen | 1 | 1 | BASELINE
P0001 | GAD0208 | GAD02-Total Score | 15 | 1 | BASELINE
P0001 | GAD02TS | GAD02-Total Score - Analysis | 15 | 1 | BASELINE
P0001 | GAD0201 | GAD02-Feeling Nervous Anxious or On Edge | 2 | 151 | UNSCHEDULED VISIT 5.01

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()`:

    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"
        )
      )

USUBJID | PARAMCD | PARAM | AVAL | ADY | AVISIT
--- | --- | --- | --- | --- | ---
P0001 | GDS0201 | GDS02-Satisfied With Life | 0 | 1 | BASELINE
P0001 | GDS0202 | GDS02-Dropped Activities and Interests | 1 | 1 | BASELINE
P0001 | GDS0203 | GDS02-Life Is Empty | 0 | 1 | BASELINE
P0001 | GDS0204 | GDS02-Bored Often | 1 | 1 | BASELINE
P0001 | GDS0205 | GDS02-Good Spirits Most of Time | 1 | 1 | BASELINE
P0001 | GDS0206 | GDS02-Afraid of Something Bad Happening | 1 | 1 | BASELINE
P0001 | GDS0207 | GDS02-Feel Happy Most of Time | 1 | 1 | BASELINE
P0001 | GDS0208 | GDS02-Often Feel Helpless | 1 | 1 | BASELINE
P0001 | GDS0209 | GDS02-Prefer to Stay Home | 0 | 1 | BASELINE
P0001 | GDS0210 | GDS02-Memory Problems | 1 | 1 | BASELINE

After deriving the scores by visit, the baseline and change from baseline variables can be derived:

    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
      derive_var_obs_number(
        by_vars = exprs(STUDYID, USUBJID),
        order = exprs(PARAMCD, ADT),
        check_type = "error"
      )

USUBJID | PARAMCD | PARAM | AVISIT | AVAL | BASE | CHG | PCHG
--- | --- | --- | --- | --- | --- | --- | ---
P0001 | GDS0201 | GDS02-Satisfied With Life | BASELINE | 0 | 0 | NA | NA
P0001 | GDS0201 | GDS02-Satisfied With Life | VISIT 2 | 0 | 0 | 0 | NA
P0001 | GDS0201 | GDS02-Satisfied With Life | UNSCHEDULED 2.01 | 0 | 0 | 0 | NA
P0001 | GDS0201 | GDS02-Satisfied With Life | VISIT 3 | NA | 0 | NA | NA
P0001 | GDS0201 | GDS02-Satisfied With Life | VISIT 4 | 0 | 0 | 0 | NA
P0001 | GDS0202 | GDS02-Dropped Activities and Interests | BASELINE | 1 | 1 | NA | NA
P0001 | GDS0202 | GDS02-Dropped Activities and Interests | VISIT 2 | 0 | 1 | -1 | -100
P0001 | GDS0202 | GDS02-Dropped Activities and Interests | UNSCHEDULED 2.01 | 0 | 1 | -1 | -100
P0001 | GDS0202 | GDS02-Dropped Activities and Interests | VISIT 3 | NA | 1 | NA | NA
P0001 | GDS0202 | GDS02-Dropped Activities and Interests | VISIT 4 | 0 | 1 | -1 | -100

# Time to Deterioration/Improvement

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:

    # Create AVALCATy lookup table
    avalcat_lookup <- exprs(
      ~PARAMCD, ~condition, ~AVALCAT1, ~AVALCAT1N,
      "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,
      AVALCAT1N > BASECA1N, "WORSENED",
      AVALCAT1N == BASECA1N, "NO CHANGE",
      AVALCAT1N < 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 = AVALCAT1N,
        new_var = BASECA1N
      ) %>%
      derive_vars_cat(
        definition = chgcat_lookup
      )

USUBJID | PARAMCD | PARAM | AVISIT | AVAL | AVALCAT1 | CHGCAT1
--- | --- | --- | --- | --- | --- | ---
P0001 | GDS02TS | GDS02- Total Score - Analysis | BASELINE | 10 | Possible Depression | NO CHANGE
P0001 | GDS02TS | GDS02- Total Score - Analysis | VISIT 2 | 8 | Possible Depression | NO CHANGE
P0001 | GDS02TS | GDS02- Total Score - Analysis | UNSCHEDULED 2.01 | 8 | Possible Depression | NO CHANGE
P0001 | GDS02TS | GDS02- Total Score - Analysis | VISIT 3 | 7 | Possible Depression | NO CHANGE
P0001 | GDS02TS | GDS02- Total Score - Analysis | VISIT 4 | 3 | Normal | IMPROVED
P0001 | GDS0215 | GDS02-Most People Better Off Than You | BASELINE | 0 | NA | NA
P0001 | GDS0215 | GDS02-Most People Better Off Than You | VISIT 2 | 0 | NA | NA
P0001 | GDS0215 | GDS02-Most People Better Off Than You | UNSCHEDULED 2.01 | 0 | NA | NA
P0001 | GDS0215 | GDS02-Most People Better Off Than You | VISIT 3 | 0 | NA | NA
P0001 | GDS0215 | GDS02-Most People Better Off Than You | VISIT 4 | 0 | NA | NA

Then a time to deterioration parameter can be derived by:

    # 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
      )

USUBJID | PARAMCD | PARAM | AVAL | CNSR | EVNTDESC | SRCDOM | SRCVAR
--- | --- | --- | --- | --- | --- | --- | ---
P0001 | TTDEPR | Time to depression | 90 | 1 | LAST ASSESSMENT | ADQS | ADT
P0002 | TTDEPR | Time to depression | 30 | 0 | DEPRESSION WORSENED | ADQS | ADT

# 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:

    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
      )

USUBJID | PARAMCD | PARAM | ADY | CHGCAT1 | CDETFL
--- | --- | --- | --- | --- | ---
P0001 | GDS02TS | GDS02- Total Score - Analysis | 1 | NO CHANGE | NA
P0001 | GDS02TS | GDS02- Total Score - Analysis | 30 | NO CHANGE | NA
P0001 | GDS02TS | GDS02- Total Score - Analysis | 43 | NO CHANGE | NA
P0001 | GDS02TS | GDS02- Total Score - Analysis | 58 | NO CHANGE | NA
P0001 | GDS02TS | GDS02- Total Score - Analysis | 90 | IMPROVED | NA
P0001 | GDS0215 | GDS02-Most People Better Off Than You | 1 | NA | NA
P0001 | GDS0215 | GDS02-Most People Better Off Than You | 30 | NA | NA
P0001 | GDS0215 | GDS02-Most People Better Off Than You | 43 | NA | NA
P0001 | GDS0215 | GDS02-Most People Better Off Than You | 58 | NA | NA
P0001 | GDS0215 | GDS02-Most People Better Off Than You | 90 | NA | NA

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:

    # 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")
      )

USUBJID | PARAMCD | PARAM | ADY | CHGCAT1 | CONDETFL | CDTDTHFL
--- | --- | --- | --- | --- | --- | ---
P0001 | GDS02TS | GDS02- Total Score - Analysis | 1 | NO CHANGE | NA | NA
P0001 | GDS02TS | GDS02- Total Score - Analysis | 30 | NO CHANGE | NA | NA
P0001 | GDS02TS | GDS02- Total Score - Analysis | 43 | NO CHANGE | NA | NA
P0001 | GDS02TS | GDS02- Total Score - Analysis | 58 | NO CHANGE | NA | NA
P0001 | GDS02TS | GDS02- Total Score - Analysis | 90 | IMPROVED | NA | NA
P0001 | GDS0215 | GDS02-Most People Better Off Than You | 1 | NA | NA | NA
P0001 | GDS0215 | GDS02-Most People Better Off Than You | 30 | NA | NA | NA
P0001 | GDS0215 | GDS02-Most People Better Off Than You | 43 | NA | NA | NA
P0001 | GDS0215 | GDS02-Most People Better Off Than You | 58 | NA | NA | NA
P0001 | GDS0215 | GDS02-Most People Better Off Than You | 90 | NA | NA | NA

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

    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")
      )

USUBJID | PARAMCD | PARAM | ADY | CHGCAT1 | DEFDETFL
--- | --- | --- | --- | --- | ---
P0001 | GDS02TS | GDS02- Total Score - Analysis | 1 | NO CHANGE | NA
P0001 | GDS02TS | GDS02- Total Score - Analysis | 30 | NO CHANGE | NA
P0001 | GDS02TS | GDS02- Total Score - Analysis | 43 | NO CHANGE | NA
P0001 | GDS02TS | GDS02- Total Score - Analysis | 58 | NO CHANGE | NA
P0001 | GDS02TS | GDS02- Total Score - Analysis | 90 | IMPROVED | NA
P0001 | GDS0215 | GDS02-Most People Better Off Than You | 1 | NA | NA
P0001 | GDS0215 | GDS02-Most People Better Off Than You | 30 | NA | NA
P0001 | GDS0215 | GDS02-Most People Better Off Than You | 43 | NA | NA
P0001 | GDS0215 | GDS02-Most People Better Off Than You | 58 | NA | NA
P0001 | GDS0215 | GDS02-Most People Better Off Than You | 90 | NA | NA

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

# 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”.

    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"
        )
      )

USUBJID | PARAMCD | PARAM | AVISIT | AVALC
--- | --- | --- | --- | ---
P0001 | SP0101 | SP01-No sleep at all | BASELINE | NO
P0001 | SP0102 | SP01-Waking up more than three times | BASELINE | NO
P0001 | SP0103 | SP01-More than 30 mins to fall asleep | BASELINE | YES
P0001 | SP01WSP | Worst Sleeping Problems | BASELINE | More than 30 mins to fall asleep
P0001 | SP0101 | SP01-No sleep at all | VISIT 2 | NO
P0001 | SP0102 | SP01-Waking up more than three times | VISIT 2 | NO
P0001 | SP0103 | SP01-More than 30 mins to fall asleep | VISIT 2 | NO
P0001 | SP01WSP | Worst Sleeping Problems | VISIT 2 | No sleeping problems
P0001 | SP0101 | SP01-No sleep at all | VISIT 4 | NO
P0001 | SP0102 | SP01-Waking up more than three times | VISIT 4 | YES

# Completion

Parameters for completion, like “at least 90% of the questions were answered”, can be derived by `derive_summary_records()`.

    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")
        )
      )

USUBJID | PARAMCD | PARAM | AVISIT | AVALC
--- | --- | --- | --- | ---
P0001 | COMPL90P | Completed at least 90% of questions? | BASELINE | YES
P0001 | COMPL90P | Completed at least 90% of questions? | UNSCHEDULED 2.01 | YES
P0001 | COMPL90P | Completed at least 90% of questions? | VISIT 2 | YES
P0001 | COMPL90P | Completed at least 90% of questions? | VISIT 3 | NO
P0001 | COMPL90P | Completed at least 90% of questions? | VISIT 4 | YES
P0001 | GDS0201 | GDS02-Satisfied With Life | BASELINE | YES
P0001 | GDS0201 | GDS02-Satisfied With Life | VISIT 2 | YES
P0001 | GDS0201 | GDS02-Satisfied With Life | UNSCHEDULED 2.01 | YES
P0001 | GDS0201 | GDS02-Satisfied With Life | VISIT 3 | NA
P0001 | GDS0201 | GDS02-Satisfied With Life | VISIT 4 | YES

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()`:

    # 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)

USUBJID | PARAMCD | PARAM | AVISIT | AVALC
--- | --- | --- | --- | ---
P0001 | COMPL90P | Completed at least 90% of questions? | BASELINE | YES
P0001 | COMPL90P | Completed at least 90% of questions? | UNSCHEDULED 2.01 | YES
P0001 | COMPL90P | Completed at least 90% of questions? | VISIT 2 | YES
P0001 | COMPL90P | Completed at least 90% of questions? | VISIT 3 | NO
P0001 | COMPL90P | Completed at least 90% of questions? | VISIT 4 | YES
P0001 | COMPLALL | Completed all questions? | BASELINE | YES
P0001 | COMPLALL | Completed all questions? | UNSCHEDULED 2.01 | YES
P0001 | COMPLALL | Completed all questions? | VISIT 2 | YES
P0001 | COMPLALL | Completed all questions? | VISIT 3 | NO
P0001 | COMPLALL | Completed all questions? | VISIT 4 | YES

1. The example `QS` data (`example_qs`) is included in the admiral package.↩︎