CDISC data standards

CDISC (Clinical Data Interchange Standards Consortium) defines the data formats required for regulatory submissions to the FDA and EMA. As a pharmacometrician, you will routinely work with SDTM and ADaM datasets — understanding their structure is essential for efficient data preparation and analysis.

The data pipeline

Data flows from the clinic through a series of transformations before reaching your analysis (Figure 1).

Figure 1: You’ll most often encounter SDTM- or ADaM-formatted data if you’re working with clinical data as a pharmacometrician.

Each standard has an Implementation Guide (IG) (like SDTMIG for SDTM) with version numbers that must be tracked and declared in submissions. CDISC also provides TAUGs (Therapeutic Area User Guides) that give domain-specific guidance (like for oncology, diabetes, CNS, etc.).

CDASH

CDASH (Clinical Data Acquisition Standards Harmonization) standardizes data entry on case report forms (CRFs) at clinical sites. It defines the fields that should be collected, ensuring consistency before data is ever entered into a database. As a pharmacometrician, you rarely work directly with CDASH — but it determines the quality and completeness of the raw data you eventually receive.

SDTM

SDTM (Study Data Tabulation Model) organizes raw clinical data into submission-ready datasets. It defines domains (separate datasets on specific topics, like lab values, vital signs, or adverse events), along with controlled terminology for naming variables.

Domains most relevant to pharmacometricians

Domain	Name	What it contains
PC	PK concentrations	Drug and metabolite concentrations over time — your primary dataset for popPK
PP	PK parameters	NCA-derived parameters (AUC, Cmax, t½) per subject per period
EX	Exposure	Dosing records — dose amount, route, start/end date and time
LB	Laboratory results	Lab values used as covariates (renal/hepatic function, biomarkers)
DM	Demographics	Age, sex, race, weight — the covariate backbone
VS	Vital signs	Height, weight, blood pressure — often used as time-varying covariates
AE	Adverse events	Safety data — relevant for E-R safety analyses

General Observation Classes

All SDTM domains are grouped into observation classes:

Table 1: General observation classes in SDTM.

Observation Class	Domain Name	Domain Abbreviation
Interventions	Procedure agents	AG
	Concomitant/prior medications	CM
	Exposure	EX
	Exposure as collected	EC
	Meal data	ML
	Procedures	PR
	Substance use	SU
Events	Adverse events	AE
	Biospecimen events	BE
	Clinical events	CE
	Disposition	DS
	Healthcare encounters	HO
	Medical history	MH
	Protocol deviations	DV
Findings	Product/drug accountability	DA
	Death details	DD
	ECG test results	EG
	Inclusion/Exclusion criteria not met	IE
	Biospecimen findings	BS
	Cell phenotype findings	CP
	Genomics findings	GF
	Immunogenicity specimen assessments	IS
	Laboratory test results	LB
	Microbiology specimen	MB
	Microbiology susceptibility	MS
	Microscopic findings	MI
	PK concentrations	PC
	PK parameters	PP
	Morphology	MO
	Cardiovascular system findings	CV
	Musculoskeletal system findings	MK
	Nervous system findings	NV
	Ophthalmic examinations	OE
	Reproductive system findings	RP
	Respiratory system findings	RE
	Urinary system findings	UR
	Physical examination	PE
	Functional tests	FT
	Questionnaires	QS
	Disease response and clinical classification	RS
	Subject characteristics	SC
	Subject status	SS
	Tumor/lesion identification	TU
	Tumor/lesion results	TR
Findings about	Vital signs	VS
	Findings about events or interventions	FA
	Skin response	SR
Special Purpose	Comments	CO
	Demographics	DM
	Subject elements	SE
	Subject disease milestones	SM
	Subject visits	SV
Trial design	Trial arms	TA
	Trial disease assessments	TD
	Trial elements	TE
	Trial inclusion/exclusion criteria	TI
	Trial disease milestones	TM
	Trial summary	TS
	Trial visits	TV
Relationship	Related records	RELREC
	Related specimens	RELSPEC
	Related subjects	RELSUB
	Supplemental qualifiers for [domain name]	SUPP--
Study reference	Non-host organism indentifiers	OI

PC domain — PK concentrations

The PC domain (along with the EX domain) is the most important SDTM domain for pharmacometricians. Each record is one concentration measurement for one subject at one time point.

Key variables:

Variable	Description
USUBJID	Unique subject identifier
PCTESTCD	Analyte code (e.g., DRUG, METABOLITE)
PCORRES	Result as collected (character, e.g., <0.5)
PCSTRESC	Standardized result (character; BLQ if below LLOQ)
PCSTRESN	Standardized result (numeric; blank if BLQ)
PCSTRESU	Standardized units (e.g., ng/mL)
PCLLOQ	Lower limit of quantitation
PCDTC	Date/time of collection (ISO 8601)

SDTM variable types

Variables in each domain are classified as required, expected, or permissible, and fall into four types:

Type	Purpose	Examples
Identifier	Links records to study/subject	STUDYID, USUBJID, --SEQ
Topic	The focus of the observation	--TESTCD, --TRT, --TERM
Qualifier	Additional detail about the topic	Result, grouping, synonym, record, variable qualifiers
Timing	When the observation occurred	--DTC, --DY, VISIT, --ELTM

ADaM

ADaM (Analysis Data Model) derives analysis-ready datasets from SDTM. Like SDTM, it is organized into different domains, each with its specific setup. Most of the time, an ADaM domain is derived from an SDTM domain (e.g., ADPC from PC) but with additional variables and transformations to facilitate analysis.

ADaM Implementation Guide

ADaM dataset structures

Structure	Name	Description
ADSL	Subject-Level Analysis Dataset	One record per subject — all baseline covariates and flags
BDS	Basic Data Structure	One or more records per subject/parameter/timepoint — used for ADPC, ADPP
OCCDS	Occurrence Data Structure	Designed for counting occurrences (adverse events, medications)

ADaM variable naming conventions

All ADaM variable names must be no more than 8 characters in length, start with a letter (not underscore), and be composed only of letters (A–Z), underscore (_), and numerals (0–9). ADaM adheres to a principle of harmonization known as “same name, same meaning, same values” across datasets.

In a pair of corresponding variables (e.g., TRTP and TRTPN), the primary or most commonly used variable does not have the suffix or extension (i.e., N for numeric or C for character). The relevant suffix is used only on the name of the secondary member of the variable pair.

Suffix	Meaning
N	Numeric version of a character variable (e.g., TRTPN)
C	Character version of a numeric variable
FL	Flag/indicator variable (Y/N, 1/0)
GRy, Gy, CATy	Grouping variable (y = grouping scheme)
DT	Numeric date
DTM	Numeric datetime
TM	Numeric time
DTF	Date imputation flag
TMF	Time imputation flag
DY	Relative study day (no day 0)
BL	Baseline
CHG	Change
FU	Follow-up
OT	On treatment
RU	Run-in
SC	Screening
TA	Taper
TI	Titer
U	Units
WA	Washout

SEND

SEND (Standard for Exchange of Nonclinical Data) is the nonclinical counterpart to SDTM — it applies the same domain-based structure to pre-clinical studies such as toxicology and pharmacokinetic studies in vivo.

SEND data can be a source for allometric scaling, preclinical PK/PD model development, and inter-species translation.