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Creating a Control of Eating Questionnaire ADaM

License

Note that University of Leeds are the copyright holders of the Control of Eating Questionnaire (CoEQ) and the test data included within {admiralmetabolic} as well as the ADCOEQ code are for not-for-profit use only within {admiralmetabolic} and pharmaverse-related examples/documentation. Any persons or companies wanting to use the CoEQ should request a license to do so from the following link.

Introduction

This article describes creating a Control of Eating Questionnaire ADaM for clinical trials.

We advise you first consult the {admiral} Creating Questionnaire ADaMs vignette. The programming workflow around creating the general set-up of an ADQS using {admiral} functions is the same. In this vignette, we focus on the Control of Eating Questionnaire and avoid repeating information and maintaining the same content in two places. As such, the code in this vignette is not completely executable; we recommend consulting the ADQS template script to view the full workflow.

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(admiral)
library(admiralmetabolic)
library(pharmaversesdtm)
library(dplyr)
library(stringr)

Programming Workflow

Read in Data
Original items
Derive the four Subscales
Remaining ADCOEQ Set-up

Read in Data

To start, all data frames needed for the creation of the ADaM dataset should be loaded into the global environment. Reading data will usually be a company specific process, however, for the purpose of this vignette, we will use example data from {pharmaversesdtm} and {admiral}. We will utilize DM, QS and ADSL.

In this vignette we use example data for the CoEQ. The example QS data (qs_metabolic) is included in the {admiralmetabolic} package.

dm_metabolic <- admiralmetabolic::dm_metabolic
qs_metabolic <- admiralmetabolic::qs_metabolic
admiral_adsl <- admiral::admiral_adsl

dm <- convert_blanks_to_na(dm_metabolic)
qs <- convert_blanks_to_na(qs_metabolic)
admiral_adsl <- convert_blanks_to_na(admiral_adsl)

Within this vignette, DM is used as the basis for ADSL:

# Retrieve required variables from admiral ADSL for this vignette that are not present in DM dataset
adsl <- dm %>%
  select(-DOMAIN) %>%
  mutate(TRT01P = ARM, TRT01A = ACTARM) %>%
  derive_vars_merged(
    dataset_add = admiral_adsl,
    by_vars = exprs(USUBJID),
    new_vars = exprs(TRTSDT, TRTEDT)
  )

Original Items

The original items, i.e. the answers to the questionnaire questions, can be handled in the same way as in an {admiral} BDS finding ADaM.

adcoeq1 <- qs %>%
  # Add ADSL variables
  derive_vars_merged(
    dataset_add = adsl,
    by_vars = exprs(STUDYID, USUBJID),
    new_vars = exprs(TRTSDT, TRTEDT, TRT01P, TRT01A)
  ) %>%
  # Add analysis parameter variables
  mutate(
    PARAMCD = QSTESTCD,
    PARAM = QSTEST,
    PARCAT1 = QSCAT
  ) %>%
  # Add timing variables
  derive_vars_dt(new_vars_prefix = "A", dtc = QSDTC) %>%
  derive_vars_dy(reference_date = TRTSDT, source_vars = exprs(ADT)) %>%
  mutate(
    AVISIT = case_when(
      is.na(VISIT) ~ NA_character_,
      str_detect(VISIT, "UNSCHED|RETRIEVAL|AMBUL") ~ NA_character_,
      TRUE ~ str_to_title(VISIT)
    ),
    AVISITN = case_when(
      AVISIT == "Baseline" ~ 0,
      str_detect(AVISIT, "Screen") ~ -1,
      str_detect(VISIT, "WEEK") ~ as.integer(str_extract(VISIT, "\\d+")),
      TRUE ~ NA_integer_
    )
  )

USUBJID	PARAMCD	PARAM	PARCAT1	QSSTRESN	ADY	AVISIT
01-701-1015	COEQ01	How hungry have you felt?	COEQ	17	-7	Screening 1
01-701-1015	COEQ02	How full have you felt?	COEQ	81	-7	Screening 1
01-701-1015	COEQ03	How strong was your desire to eat sweet foods?	COEQ	38	-7	Screening 1
01-701-1015	COEQ04	How strong was your desire to eat savoury foods?	COEQ	33	-7	Screening 1
01-701-1015	COEQ05	How happy have you felt?	COEQ	60	-7	Screening 1
01-701-1015	COEQ06	How anxious have you felt?	COEQ	60	-7	Screening 1
01-701-1015	COEQ07	How alert have you felt?	COEQ	12	-7	Screening 1
01-701-1015	COEQ08	How contented have you felt?	COEQ	29	-7	Screening 1
01-701-1015	COEQ09	During the last 7 days how often have you had food cravings?	COEQ	58	-7	Screening 1
01-701-1015	COEQ10	How strong have any food cravings been?	COEQ	63	-7	Screening 1

The analysis values (AVAL and AVALC) for most original items are set directly from QSSTRESN and QSORRES, respectively. However, CoEQ item 6 (COEQ06) requires a manual transformation, where we invert the original scores. This transformation is performed because CoEQ item 6 is used in calculating the subscale for “Positive Mood,” where its original scores indicate anxiety.

In cases where QSSTRESN values require transformation, it is recommended to keep the original QSSTRESN values in the ADaM dataset for traceability.

adcoeq2 <- adcoeq1 %>%
  # Add analysis value variables
  mutate(
    AVAL = if_else(PARAMCD == "COEQ06", 100 - QSSTRESN, QSSTRESN),
    AVALC = if_else(PARAMCD == "COEQ20", QSORRES, NA_character_)
  )

USUBJID	PARAMCD	PARAM	PARCAT1	QSSTRESN	ADY	AVISIT	AVALC	AVAL
01-701-1015	COEQ01	How hungry have you felt?	COEQ	17	-7	Screening 1	NA	17
01-701-1015	COEQ02	How full have you felt?	COEQ	81	-7	Screening 1	NA	81
01-701-1015	COEQ03	How strong was your desire to eat sweet foods?	COEQ	38	-7	Screening 1	NA	38
01-701-1015	COEQ04	How strong was your desire to eat savoury foods?	COEQ	33	-7	Screening 1	NA	33
01-701-1015	COEQ05	How happy have you felt?	COEQ	60	-7	Screening 1	NA	60
01-701-1015	COEQ06	How anxious have you felt?	COEQ	60	-7	Screening 1	NA	40
01-701-1015	COEQ07	How alert have you felt?	COEQ	12	-7	Screening 1	NA	12
01-701-1015	COEQ08	How contented have you felt?	COEQ	29	-7	Screening 1	NA	29
01-701-1015	COEQ09	During the last 7 days how often have you had food cravings?	COEQ	58	-7	Screening 1	NA	58
01-701-1015	COEQ20	Which one food makes it most difficult for you to control eating?	COEQ	NA	-7	Screening 1	Ice Cream	NA

For deriving visits based on time-windows, see {admiral} Visit and Period Variables.

Derive the four Subscales

For the Control of Eating Questionnaire, four subscales are derived. These subscales are derived as the mean across a subset of the various items/questions.

The subscales are defined as follows:

Craving Control: Calculate mean of items 9, 10, 11, 12 and 19.
Craving for Sweet: Calculate mean of items 3, 13, 14 and 15.
Craving for Savoury: Calculate mean of items 4, 16, 17 and 18.
Positive Mood: Calculate mean of items 5, 7, 8 and 6 (reversed).

These parameters can be derived by derive_summary_records():

adcoeq3 <- adcoeq2 %>%
  call_derivation(
    derivation = derive_summary_records,
    variable_params = list(
      params(
        filter_add = PARAMCD %in% c("COEQ09", "COEQ10", "COEQ11", "COEQ12", "COEQ19"),
        set_values_to = exprs(
          AVAL = mean(AVAL, na.rm = TRUE),
          PARAMCD = "COEQCRCO",
          PARAM = "COEQ - Craving Control"
        )
      ),
      params(
        filter_add = PARAMCD %in% c("COEQ03", "COEQ13", "COEQ14", "COEQ15"),
        set_values_to = exprs(
          AVAL = mean(AVAL, na.rm = TRUE),
          PARAMCD = "COEQCRSW",
          PARAM = "COEQ - Craving for Sweet"
        )
      ),
      params(
        filter_add = PARAMCD %in% c("COEQ04", "COEQ16", "COEQ17", "COEQ18"),
        set_values_to = exprs(
          AVAL = mean(AVAL, na.rm = TRUE),
          PARAMCD = "COEQCRSA",
          PARAM = "COEQ - Craving for Savoury"
        )
      ),
      params(
        filter_add = PARAMCD %in% c("COEQ05", "COEQ07", "COEQ08", "COEQ06"),
        set_values_to = exprs(
          AVAL = mean(AVAL, na.rm = TRUE),
          PARAMCD = "COEQPOMO",
          PARAM = "COEQ - Positive Mood"
        )
      )
    ),
    dataset_add = adcoeq2,
    by_vars = exprs(STUDYID, USUBJID, AVISIT, AVISITN, ADT, ADY, PARCAT1, TRTSDT, TRTEDT, TRT01P, TRT01A)
  )

USUBJID	PARAMCD	PARAM	AVAL	ADY	AVISIT
01-701-1015	COEQCRCO	COEQ - Craving Control	62.60	-7	Screening 1
01-701-1015	COEQCRSA	COEQ - Craving for Savoury	49.25	-7	Screening 1
01-701-1015	COEQCRSW	COEQ - Craving for Sweet	58.50	-7	Screening 1
01-701-1015	COEQPOMO	COEQ - Positive Mood	35.25	-7	Screening 1
01-701-1015	COEQCRCO	COEQ - Craving Control	54.80	-2	Screening 2
01-701-1015	COEQCRSA	COEQ - Craving for Savoury	65.75	-2	Screening 2
01-701-1015	COEQCRSW	COEQ - Craving for Sweet	43.50	-2	Screening 2
01-701-1015	COEQPOMO	COEQ - Positive Mood	53.00	-2	Screening 2
01-701-1015	COEQCRCO	COEQ - Craving Control	37.80	1	Baseline
01-701-1015	COEQCRSA	COEQ - Craving for Savoury	47.75	1	Baseline

Remaining ADCOEQ Set-up

The {admiral} Creating Questionnaire ADaMs vignette describes further steps, including, how to calculate the change from baseline variables, and how to add parameters for questionnaire completion.

Example Scripts

ADaM	Sample Code
ADCOEQ	ad_adcoeq.R

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.