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Creating a Metabolic ADLB ADaM

Introduction

This article describes creating a laboratory ADaM for metabolic clinical trials.

We advise you first consult the {admiral} Creating a BDS Finding ADaM vignette. The programming workflow around creating the general set-up of an ADLB using {admiral} functions is the same. In this vignette, we focus on common ADLB derivations in metabolic studies and avoid repeating information and maintaining the same content in two places.

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
Define Lookup Tables
Derive Core ADLB Variables
Derive Visit Information
Derive Metabolic Parameters
Remaining ADLB Set-up
Example Script

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 LB and ADSL data.

lb_metabolic <- pharmaversesdtm::lb_metabolic
admiralmetabolic_adsl <- admiralmetabolic::admiralmetabolic_adsl

lb <- convert_blanks_to_na(lb_metabolic)
adsl <- convert_blanks_to_na(admiralmetabolic_adsl)

Define Lookup Tables

Define parameter lookup table used to derive PARAMCD, PARAM, and PARAMN variables.

# Assign PARAMCD, PARAM, and PARAMN
param_lookup <- tibble::tribble(
  ~LBTESTCD, ~PARAMCD, ~PARAM, ~PARAMN,
  "ALB", "ALB", "Albumin (g/L)", 1,
  "ALP", "ALKPH", "Alkaline Phosphatase (U/L)", 2,
  "AST", "AST", "Aspartate Aminotransferase (U/L)", 3,
  "CHOL", "CHOLES", "Cholesterol (mmol/L)", 4,
  "GGT", "GGT", "Gamma Glutamyl Transferase (U/L)", 5,
  "GLUC", "GLUC", "Glucose (mmol/L)", 6,
  "HBA1CHGB", "HBA1CHGB", "Hemoglobin A1C/Hemoglobin (mmol/mol)", 7,
  "INSULIN", "INSULIN", "Insulin (mIU/L)", 8,
  "TRIG", "TRIG", "Triglycerides (mg/dL)", 9
)

Derive Core ADLB Variables

The basic parameters and timing variables can be derived similarly to other BDS finding ADaMs. For the derivation of Glucose and Insulin, only fasted results (where LBFAST = "Y") are considered. For all other laboratory tests, the fasting status does not affect their inclusion in the dataset at this stage.

# Define required ADSL variables for derivations
adsl_vars <- exprs(TRTSDT, TRTEDT, TRT01A, TRT01P)

adlb <- lb %>%
  # Remove non-fasted GLUC and INSULIN results
  filter(!(LBTESTCD %in% c("GLUC", "INSULIN") & LBFAST != "Y")) %>%
  # Join ADSL with LB (need TRTSDT for ADY derivation)
  derive_vars_merged(
    dataset_add = adsl,
    new_vars = adsl_vars,
    by_vars = get_admiral_option("subject_keys")
  ) %>%
  # Calculate ADT, ADY
  derive_vars_dt(
    new_vars_prefix = "A",
    dtc = LBDTC
  ) %>%
  derive_vars_dy(reference_date = TRTSDT, source_vars = exprs(ADT))

adlb <- adlb %>%
  # Add PARAMCD PARAM and PARAMN - from parameter lookup table
  derive_vars_merged_lookup(
    dataset_add = param_lookup,
    new_vars = exprs(PARAMCD, PARAM, PARAMN),
    by_vars = exprs(LBTESTCD)
  ) %>%
  ## Calculate PARCAT1 PARCAT2 AVAL AVALC ANRLO ANRHI
  slice_derivation(
    derivation = mutate,
    args = params(
      PARCAT1 = LBCAT,
    ),
    # Handle specific parameters requiring conventional units (CV)
    derivation_slice(
      filter = LBTESTCD %in% c("TRIG", "INSULIN"),
      args = params(
        PARCAT2 = "CV",
        AVAL = as.numeric(LBORRES),
        AVALC = NA_character_,
        ANRLO = as.numeric(LBORNRLO),
        ANRHI = as.numeric(LBORNRHI)
      )
    ),
    # Handle other parameters using standard units (SI)
    derivation_slice(
      filter = TRUE,
      args = params(
        PARCAT2 = "SI",
        AVAL = LBSTRESN,
        # Only populate AVALC if character value is non-redundant with AVAL
        AVALC = if_else(
          is.na(AVAL) | as.character(AVAL) != LBSTRESC,
          LBSTRESC,
          NA_character_
        ),
        ANRLO = LBSTNRLO,
        ANRHI = LBSTNRHI
      )
    )
  )
#> All `LBTESTCD` are mapped.

STUDYID	USUBJID	ADT	ADY	PARAMCD	PARAM	PARAMN	PARCAT1	PARCAT2	AVAL	AVALC	ANRLO	ANRHI
CDISCPILOT01	01-701-1015	2013-12-26	-7	ALB	Albumin (g/L)	1	CHEMISTRY	SI	38.00000	NA	33.00000	49.00000
CDISCPILOT01	01-701-1015	2013-12-26	-7	ALKPH	Alkaline Phosphatase (U/L)	2	CHEMISTRY	SI	34.00000	NA	35.00000	115.00000
CDISCPILOT01	01-701-1015	2013-12-26	-7	AST	Aspartate Aminotransferase (U/L)	3	CHEMISTRY	SI	40.00000	NA	9.00000	34.00000
CDISCPILOT01	01-701-1015	2013-12-26	-7	CHOLES	Cholesterol (mmol/L)	4	CHEMISTRY	SI	5.94780	NA	4.03000	7.76000
CDISCPILOT01	01-701-1015	2013-12-26	-7	GGT	Gamma Glutamyl Transferase (U/L)	5	CHEMISTRY	SI	15.00000	NA	5.00000	50.00000
CDISCPILOT01	01-701-1015	2013-12-26	-7	GLUC	Glucose (mmol/L)	6	CHEMISTRY	SI	4.71835	NA	2.80000	13.90000
CDISCPILOT01	01-701-1015	2013-12-26	-7	HBA1CHGB	Hemoglobin A1C/Hemoglobin (mmol/mol)	7	CHEMISTRY	SI	62.84175	NA	20.21865	38.79795
CDISCPILOT01	01-701-1015	2013-12-26	-7	INSULIN	Insulin (mIU/L)	8	CHEMISTRY	CV	14.40000	NA	2.00000	25.00000
CDISCPILOT01	01-701-1015	2013-12-26	-7	TRIG	Triglycerides (mg/dL)	9	CHEMISTRY	CV	111.10000	NA	0.00000	150.00000
CDISCPILOT01	01-701-1015	2014-01-16	15	ALB	Albumin (g/L)	1	CHEMISTRY	SI	39.00000	NA	33.00000	49.00000

Derive Visit Information

Derive Analysis Visit (AVISIT) and Analysis Visit Number (AVISITN).

adlb <- adlb %>%
  mutate(
    AVISIT = case_when(
      str_detect(VISIT, "SCREEN") ~ "Baseline",
      !is.na(VISIT) ~ str_to_title(VISIT),
      TRUE ~ NA_character_
    ),
    AVISITN = case_when(
      AVISIT == "Baseline" ~ 0,
      str_detect(VISIT, "WEEK") ~ as.integer(str_extract(VISIT, "\\d+")),
      TRUE ~ NA_integer_
    )
  )

STUDYID	USUBJID	PARAMCD	PARAM	PARCAT1	AVAL	ADY	AVISIT	AVISITN
CDISCPILOT01	01-701-1015	ALB	Albumin (g/L)	CHEMISTRY	38.00000	-7	Baseline	0
CDISCPILOT01	01-701-1015	ALKPH	Alkaline Phosphatase (U/L)	CHEMISTRY	34.00000	-7	Baseline	0
CDISCPILOT01	01-701-1015	AST	Aspartate Aminotransferase (U/L)	CHEMISTRY	40.00000	-7	Baseline	0
CDISCPILOT01	01-701-1015	CHOLES	Cholesterol (mmol/L)	CHEMISTRY	5.94780	-7	Baseline	0
CDISCPILOT01	01-701-1015	GGT	Gamma Glutamyl Transferase (U/L)	CHEMISTRY	15.00000	-7	Baseline	0
CDISCPILOT01	01-701-1015	GLUC	Glucose (mmol/L)	CHEMISTRY	4.71835	-7	Baseline	0
CDISCPILOT01	01-701-1015	HBA1CHGB	Hemoglobin A1C/Hemoglobin (mmol/mol)	CHEMISTRY	62.84175	-7	Baseline	0
CDISCPILOT01	01-701-1015	INSULIN	Insulin (mIU/L)	CHEMISTRY	14.40000	-7	Baseline	0
CDISCPILOT01	01-701-1015	TRIG	Triglycerides (mg/dL)	CHEMISTRY	111.10000	-7	Baseline	0
CDISCPILOT01	01-701-1015	ALB	Albumin (g/L)	CHEMISTRY	39.00000	15	Week 2	2

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

Derive Metabolic Parameters

In addition to the core ADLB variables, several metabolic parameters and scores will be derived based on laboratory data and vital signs. These derivations include:

Fatty Liver Index (FLI): Derived from triglycerides (mg/dL), GGT (U/L), BMI (kg/m2), and waist circumference (cm). \[ FLI = \left[\frac{\exp(\lambda)}{1 + \exp(\lambda)}\right] \times 100 \] where \[ \lambda = 0.953 \times \ln (\text{triglycerides}) + 0.139 \times BMI + 0.718 \times \ln (GGT) + 0.053 \times \text{waist circumference} – 15.745. \] FLI is interpreted as a score between 0 and 100, where higher values indicate a worse condition.
Homeostasis Model Assessment – Insulin Resistance (HOMA-IR): Calculated from fasting plasma glucose (mmol/L) and fasting plasma insulin (mIU/L). \[ \text{HOMA-IR} = \frac{FPI \times FPG}{22.5}. \] An alternative formula is used when fasting plasma glucose is assessed in mg/dL: \[ \text{HOMA-IR} = \frac{FPI \times FPG}{405}. \]

To derive these parameters and scores, the following variables will be needed:

From ADLB: TRIG (Triglycerides), GGT, GLUC (fasted), INSULIN (fasted).
From ADVS: BMI, WSTCIR (Waist Circumference).

Add variables required for derivation

In this section, we will merge relevant variables needed to derive metabolic parameter from the ADVS (Analysis Dataset for Vital Signs) dataset into the ADLB dataset. This is necessary to include variables such as BMI and Waist Circumference, which are required for the derivation of FLI score parameter in subsequent section.

# Load ADVS dataset (assuming it has been created by ad_advs.R)
admiralmetabolic_advs <- admiralmetabolic::admiralmetabolic_advs
advs <- convert_blanks_to_na(admiralmetabolic_advs)

# Merge BMI and WSTCIR from ADVS to ADLB based on subject and date
adlb <- adlb %>%
  derive_vars_transposed(
    advs,
    by_vars = exprs(!!!get_admiral_option("subject_keys"), ADT),
    key_var = PARAMCD,
    value_var = AVAL,
    filter = PARAMCD %in% c("BMI", "WSTCIR")
  )

Please note that in this example we merge on ADT, but it is also possible to merge on AVISIT or other windowing algorithms specified in the statistical analysis plan.

STUDYID	USUBJID	PARAMCD	PARAM	AVAL	ADT	ADY	AVISIT	AVISITN	BMI	WSTCIR
CDISCPILOT01	01-701-1015	ALB	Albumin (g/L)	38.00000	2013-12-26	-7	Baseline	0	38.10500	99
CDISCPILOT01	01-701-1015	ALKPH	Alkaline Phosphatase (U/L)	34.00000	2013-12-26	-7	Baseline	0	38.10500	99
CDISCPILOT01	01-701-1015	AST	Aspartate Aminotransferase (U/L)	40.00000	2013-12-26	-7	Baseline	0	38.10500	99
CDISCPILOT01	01-701-1015	CHOLES	Cholesterol (mmol/L)	5.94780	2013-12-26	-7	Baseline	0	38.10500	99
CDISCPILOT01	01-701-1015	GGT	Gamma Glutamyl Transferase (U/L)	15.00000	2013-12-26	-7	Baseline	0	38.10500	99
CDISCPILOT01	01-701-1015	GLUC	Glucose (mmol/L)	4.71835	2013-12-26	-7	Baseline	0	38.10500	99
CDISCPILOT01	01-701-1015	HBA1CHGB	Hemoglobin A1C/Hemoglobin (mmol/mol)	62.84175	2013-12-26	-7	Baseline	0	38.10500	99
CDISCPILOT01	01-701-1015	INSULIN	Insulin (mIU/L)	14.40000	2013-12-26	-7	Baseline	0	38.10500	99
CDISCPILOT01	01-701-1015	TRIG	Triglycerides (mg/dL)	111.10000	2013-12-26	-7	Baseline	0	38.10500	99
CDISCPILOT01	01-701-1015	ALB	Albumin (g/L)	39.00000	2014-01-16	15	Week 2	2	38.53219	100

Derive HOMA-IR index

This part describes how to calculate the Homeostasis Model Assessment – Insulin Resistance (HOMA-IR) index from fasting plasma glucose and fasting plasma insulin. This derivation does not require merging data from other datasets.

# Derive HOMA-IR using derive_param_computed
adlb <- adlb %>%
  derive_param_computed(
    by_vars = exprs(!!!get_admiral_option("subject_keys"), AVISIT, AVISITN, ADT, ADY, !!!adsl_vars),
    parameters = c("INSULIN", "GLUC"),
    set_values_to = exprs(
      AVAL = AVAL.INSULIN * AVAL.GLUC / 22.5,
      PARAMCD = "HOMAIR",
      PARAM = "Homeostasis Model Assessment - Insulin Resistance",
      PARAMN = 10
    )
  )

STUDYID	USUBJID	PARAMCD	PARAM	AVAL	ADY	AVISIT	AVISITN
CDISCPILOT01	01-701-1015	HOMAIR	Homeostasis Model Assessment - Insulin Resistance	3.019744	-7	Baseline	0
CDISCPILOT01	01-701-1015	HOMAIR	Homeostasis Model Assessment - Insulin Resistance	8.289493	15	Week 2	2
CDISCPILOT01	01-701-1015	HOMAIR	Homeostasis Model Assessment - Insulin Resistance	5.606263	42	Week 6	6
CDISCPILOT01	01-701-1015	HOMAIR	Homeostasis Model Assessment - Insulin Resistance	4.369747	63	Week 8	8
CDISCPILOT01	01-701-1015	HOMAIR	Homeostasis Model Assessment - Insulin Resistance	6.675262	84	Week 12	12
CDISCPILOT01	01-701-1015	HOMAIR	Homeostasis Model Assessment - Insulin Resistance	8.635382	126	Week 16	16
CDISCPILOT01	01-701-1015	HOMAIR	Homeostasis Model Assessment - Insulin Resistance	3.495403	140	Week 20	20
CDISCPILOT01	01-701-1015	HOMAIR	Homeostasis Model Assessment - Insulin Resistance	6.354785	168	Week 24	24
CDISCPILOT01	01-701-1015	HOMAIR	Homeostasis Model Assessment - Insulin Resistance	6.854622	182	Week 26	26
CDISCPILOT01	01-701-1023	HOMAIR	Homeostasis Model Assessment - Insulin Resistance	6.513173	-14	Baseline	0

Derive FLI score

To obtain the FLI score parameter, we’ll utilize triglycerides, GGT, BMI, and waist circumference. Note that this derivation requires merging data from the ADVS dataset (BMI and Waist Circumference), as demonstrated in the previous section. FLI is provided here as an example; users can derive other metabolic scores like HSI, NAFLD, and others using the same logic and required input variables.

# Derive FLI using derive_param_computed
adlb <- adlb %>%
  derive_param_computed(
    by_vars = exprs(!!!get_admiral_option("subject_keys"), AVISIT, AVISITN, ADT, ADY, BMI, WSTCIR, !!!adsl_vars),
    parameters = c("TRIG", "GGT"),
    set_values_to = exprs(
      AVAL = {
        lambda <- 0.953 * log(AVAL.TRIG) + 0.139 * BMI + 0.718 * log(AVAL.GGT) + 0.053 * WSTCIR - 15.745
        (exp(lambda) / (1 + exp(lambda))) * 100
      },
      PARAMCD = "FLI",
      PARAM = "Fatty Liver Index",
      PARAMN = 11
    )
  )

adlb <- adlb %>%
  arrange(!!!get_admiral_option("subject_keys"), ADT, PARAMN) # Arrange for consistency

STUDYID	USUBJID	PARAMCD	PARAM	AVAL	ADY	AVISIT	AVISITN
CDISCPILOT01	01-701-1015	FLI	Fatty Liver Index	77.41712	-7	Baseline	0
CDISCPILOT01	01-701-1015	FLI	Fatty Liver Index	52.97118	15	Week 2	2
CDISCPILOT01	01-701-1015	FLI	Fatty Liver Index	83.46849	42	Week 6	6
CDISCPILOT01	01-701-1015	FLI	Fatty Liver Index	81.29596	63	Week 8	8
CDISCPILOT01	01-701-1015	FLI	Fatty Liver Index	69.50608	84	Week 12	12
CDISCPILOT01	01-701-1015	FLI	Fatty Liver Index	51.45346	126	Week 16	16
CDISCPILOT01	01-701-1015	FLI	Fatty Liver Index	91.01194	140	Week 20	20
CDISCPILOT01	01-701-1015	FLI	Fatty Liver Index	93.15697	168	Week 24	24
CDISCPILOT01	01-701-1015	FLI	Fatty Liver Index	88.77355	182	Week 26	26
CDISCPILOT01	01-701-1023	FLI	Fatty Liver Index	73.64499	-14	Baseline	0

Remaining ADLB Set-up

The {admiral} Creating a BDS Finding ADaM vignette describes further steps, including how to calculate baseline and change from baseline variables, add analysis flags (e.g., ANL01FL), handle reference ranges, categorizations, and other common ADLB requirements.

Example Scripts

ADaM	Sample Code
ADLB	ad_adlb.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.
Health stats visible at Monitor.