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

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 {admiralmetabolic}. We will utilize DM, QS and ADSL.

dm_metabolic <- pharmaversesdtm::dm_metabolic
qs_metabolic <- pharmaversesdtm::qs_metabolic
admiralmetabolic_adsl <- admiralmetabolic::admiralmetabolic_adsl

dm <- convert_blanks_to_na(dm_metabolic)
qs <- convert_blanks_to_na(qs_metabolic)
adsl <- convert_blanks_to_na(admiralmetabolic_adsl)

Derive Core ADVS Variables

The following steps are to merge ADSL variables with the source data and derive the usual ADCOEQ analysis variables. Note that only the sections required for this vignette are covered in the following steps. To get a detailed guidance on all the steps, refer the {admiral} Creating a BDS Finding ADaM vignette.

adsl_vars <- exprs(TRTSDT, TRTEDT, TRT01P, TRT01A)

adcoeq <- derive_vars_merged(
  qs,
  dataset_add = adsl,
  new_vars = adsl_vars,
  by_vars = exprs(STUDYID, USUBJID)
)

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.

adcoeq <- adcoeq %>%
  # 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.

adcoeq <- adcoeq %>%
  # 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:

These parameters can be derived by derive_summary_records():

adcoeq <- adcoeq %>%
  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 = adcoeq,
    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