clinical-biostatistics-cdisc-data

name	clinical-biostatistics-cdisc-data
description	Reads, validates, and prepares CDISC SDTM and ADaM clinical trial data for analysis. Covers SDTM domain joins (DM, AE, EX, VS, LB, DS), ADaM architecture (ADSL, BDS, OCCDS, ADTTE) with traceability, treatment-emergent AE conventions, baseline derivation, SUPPQUAL/NSV handling, Define-XML 2.1, and Pinnacle 21 / CORE validation. Use when working with clinical trial datasets in CDISC SDTM/ADaM format, preparing analysis-ready data, or validating for regulatory submission.
tool_type	python
primary_tool	pyreadstat
goal_approach_exempt	true

Version Compatibility

Reference examples tested with: pyreadstat 1.2+, pandas 2.1+, numpy 1.26+. CDISC standards referenced: SDTM 2.0 / SDTMIG 3.4 (SDTM 3.0 / SDTMIG 4.0 in public review through April 2026); ADaMIG v1.3 (2021); OCCDS v1.1 (Nov 2021); BDS-for-TTE v1.0; Define-XML 2.1 (FDA-recommended for studies starting on/after March 15, 2023); Dataset-JSON v1.1 (Dec 2025; FDA Federal Register notice April 2025); Pinnacle 21 Community 4.0+; CORE (CDISC Open Rules Engine, 2024).

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R packages cited (essential for ADaM derivation): admiral (Roche/openpharma), metacore, metatools, xportr

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

CDISC SDTM and ADaM Data Handling

"Load clinical trial data" -> Parse CDISC SDTM domain files; build or consume ADaM analysis-ready datasets; preserve subject-level and event-level structure; respect traceability and validation expectations for regulatory submission.

• Python: pyreadstat.read_xport(), pd.read_sas(), pd.merge()
• R: haven::read_xpt(), admiral for ADaM derivation, Pinnacle21 or CORE for validation

Aggregation Strategy Taxonomy -- Choose the Right Question

Strategy	Scientific question answered	Example endpoint	Fails when
Any event (binary)	Does treatment change probability of experiencing the event at all?	Had any serious AE: Yes/No	Treatment changes event burden but not anyone-event probability
Event count	Does treatment change burden of events per patient?	Total AE count per subject	Subjects with 1 vs 10 events treated equivalently
Maximum severity	Does treatment shift patients toward more severe manifestations?	Worst AESEV per subject	Confounded with event count (more events -> higher chance of severe)
First event + time	Does treatment delay onset of the event?	Time to first serious AE (TTE)	Multiple events per subject ignored
Rate (events per person-time)	What is the per-time-unit rate?	AEs per subject-year	Requires exposure-time tracking; differential dropout biases rates
Composite (per ICH E9 R1)	Event becomes part of endpoint definition	Death = treatment failure	Direction of components conflict; needs hierarchy

These are NOT interchangeable. A drug might not change the proportion with AEs (binary: no effect) but increase events per patient (count: harmful). The choice must be pre-specified in the SAP based on the scientific question, not analytic convenience.

Decision Tree by Scenario

Scenario	Recommended aggregation	Why
Primary safety endpoint, single SAE event	Any event (binary); analyse with logistic	Standard regulatory; cite FDA Safety Reporting Guidance
Total adverse-event burden across study	Event count per subject; analyse with Poisson or negative binomial	Captures all events; sandwich SE recommended
Toxicity grade comparison across arms	Max severity per subject; ordinal logistic with PO check	Preserves grade ordering; cite Brant test for PO
Time-to-first AE (Kaplan-Meier visualisation)	First event + time; censor non-events	See clinical-biostatistics/survival-analysis
Rate of exacerbations per patient-year	Rate via negative binomial with offset for exposure time	Standard in COPD/asthma trials
Composite endpoint (e.g., MACE)	Component-level definition with hierarchy	Pre-specify per ICH E9(R1) composite strategy
Stratification factor extraction	Use STRATA1, STRATA2 from RANDB or DM SUPP	Must appear in analysis (Kahan-Morris 2012)
Baseline value derivation	VSBLFL='Y' / LBBLFL='Y'; derive latest pre-dose only if flag missing	Trust SDTM flag when present

SDTM vs ADaM -- The Regulatory Layer Cake

Layer	Standard	Purpose	Granularity	Examples
Source CRF	EDC system	Raw data capture	Form/page	Rave, Medidata, Veeva
SDTM	SDTM 2.0 / SDTMIG 3.4	Tabulation; "what happened"	One row per observation	DM, AE, EX, VS, LB, DS
ADaM	ADaMIG v1.3 (2021); v3.0 in development	Analysis-ready; "one-PROC-away from CSR table"	Subject (ADSL), parameter-timepoint (BDS), occurrence (OCCDS)	ADSL, ADAE, ADLB, ADTTE, ADRS
TLF	Sponsor SAS / R / Python	Tables, listings, figures for CSR	Output	Statistical methods section, demographic table, primary efficacy

The ADaM Fundamental Principles (the "ROT" document):

1. Analysis-ready (one procedure call -> the analysis result)
2. Traceability (every value links back to SDTM via metadata)
3. Clear/unambiguous communication via Define-XML
4. Naming conventions (PARAM, PARAMCD, AVAL, AVALC, BASE, CHG, PCHG, ABLFL, ANL01FL, ...)
5. Structural rules (ADSL one row per subject; BDS one row per subject/parameter/timepoint/analysis flag)

Postdoc reading: the ADaM IG v1.3 PDF (cdisc.org), ADaM ROT, FDA Study Data Technical Conformance Guide (current 2024 version), Pinnacle 21 validation rule catalog, PHUSE Connect 2023-2025 conference proceedings.

SDTM Domain Overview

Domain	Level	Description	Key Variables
DM	Subject	Demographics (one row per subject)	USUBJID, ARM, ARMCD, ACTARM, ACTARMCD, AGE, SEX, RACE, RFSTDTC, RFXSTDTC, RFENDTC
AE	Event	Adverse events (multiple per subject)	USUBJID, AETERM, AEDECOD, AEBODSYS, AESEV, AESER, AESTDTC, AEENDTC
EX	Event	Drug exposure/dosing	USUBJID, EXTRT, EXDOSE, EXSTDTC, EXENDTC
VS	Event	Vital signs	USUBJID, VSTESTCD, VSSTRESN, VSBLFL, VISIT
LB	Event	Lab results	USUBJID, LBTESTCD, LBSTRESN, LBSTRESC, LBORRES, LBBLFL, LBSPEC
DS	Event	Disposition	USUBJID, DSDECOD, DSSTDTC
SE	Event	Subject elements (treatment epochs)	USUBJID, ETCD, SESTDTC, SEENDTC
MH	Event	Medical history	USUBJID, MHDECOD, MHCAT
CM	Event	Concomitant medications	USUBJID, CMDECOD, CMSTDTC, CMENDTC

USUBJID = STUDYID-SITEID-SUBJID is the universal merge key. Subject-level domains (DM) have one row per USUBJID; event-level domains have multiple.

ARM vs ACTARM: ARM is planned treatment from randomisation; ACTARM is actual treatment received. In crossover designs, ARM differs from ACTARM by definition; in parallel-arm trials, they diverge when subjects are randomised to one arm but receive another (per-protocol violations). Primary analyses use ARM (ITT); safety uses ACTARM.

RFSTDTC vs RFXSTDTC: RFSTDTC is "first study activity date" (typically screening start); RFXSTDTC is "first treatment date." For treatment-emergent adverse event (TEAE) calculations, ALWAYS use RFXSTDTC (per ICH E2A) — RFSTDTC includes screening AEs which are not treatment-emergent.

Reading .xpt Files

import pyreadstat
import pandas as pd

# pyreadstat (recommended -- handles SAS metadata)
dm, meta = pyreadstat.read_xport('dm.xpt')
# meta.column_names, meta.column_labels, meta.variable_value_labels

# pandas built-in (SAS XPORT v5)
dm = pd.read_sas('dm.xpt', format='xport', encoding='utf-8')

# CSV fallback (common in academic datasets)
dm = pd.read_csv('DM.csv')

When pyreadstat is available, the metadata object provides column labels, value labels, and format information lost with other readers. Critical for analysis-dataset derivation: the metadata carries the controlled-terminology codelist, essential for handling values like AESEV ('MILD'/'MODERATE'/'SEVERE') with semantic ordering.

SAS XPT v5 vs Dataset-JSON -- The 2025-2026 Transition

SAS XPT v5 is the current FDA submission format but dates to 1995, with constraints:

• 8-character variable names (so LBSTRESN is a max-length name)
• 200-character text values
• No UTF-8 (ASCII only) -> problematic for multilingual trials
• Single dataset per file

Dataset-JSON v1.1 (CDISC, December 2025; FDA Federal Register notice April 2025) is the modern replacement. PHUSE-CDISC-FDA pilot has demonstrated drop-in feasibility. FDA adoption timeline pending as of mid-2026; EMA and PMDA exploring in parallel.

Pragmatic position: for the next ~2 years, SAS XPT v5 will remain the de facto submission format; sponsors should architect for Dataset-JSON migration but maintain XPT compliance.

Joining Domains -- The Right Way

import pandas as pd

dm = pd.read_csv('DM.csv')
ae = pd.read_csv('AE.csv')

# WRONG: merging event-level directly onto subject-level inflates rows
# RIGHT: aggregate first, then merge
any_serious = ae.groupby('USUBJID')['AESER'].apply(lambda x: (x == 'Y').any()).reset_index()
any_serious.columns = ['USUBJID', 'HAD_SERIOUS_AE']

analysis = dm.merge(any_serious, on='USUBJID', how='left')
analysis['HAD_SERIOUS_AE'] = analysis['HAD_SERIOUS_AE'].fillna(False)

Always use how='left' when merging onto DM to preserve all randomised subjects, even those with no events. Fill missing event indicators with 0 or False.

Aggregation strategy must follow the scientific question

Strategy	Scientific question	Example
Any event (binary)	Does treatment increase probability of experiencing the event at all?	Had any serious AE: yes/no
Event count	Does treatment increase event burden per patient?	Total AE count per subject
Maximum severity	Does treatment shift toward more severe manifestations?	Worst AESEV per subject
First event + time	Does treatment delay onset?	Time to first serious AE
Rate (events per person-time)	What is the per-time-unit rate?	AEs per subject-year

These are NOT interchangeable. A drug might not change the proportion with AEs (binary: no effect) but increase events per patient (count: harmful). The choice must follow the SAP, not analytic convenience.

# Count events per subject
ae_counts = ae.groupby('USUBJID').size().reset_index(name='AE_COUNT')

# Maximum severity per subject (map to numeric first -- string max is unreliable)
severity_map = {'MILD': 1, 'MODERATE': 2, 'SEVERE': 3}
ae['AESEV_NUM'] = ae['AESEV'].map(severity_map)
max_severity = ae.groupby('USUBJID')['AESEV_NUM'].max().reset_index()

# Specific event: COVID-19 adverse event
covid_ae = ae[ae['AEDECOD'] == 'COVID-19']
covid_ae['AESEV_NUM'] = covid_ae['AESEV'].map(severity_map)
had_covid = covid_ae.groupby('USUBJID')['AESEV_NUM'].max().reset_index()
had_covid.columns = ['USUBJID', 'COVID_SEVERITY']

analysis = dm.merge(had_covid, on='USUBJID', how='left')
analysis['HAD_COVID'] = analysis['COVID_SEVERITY'].notna().astype(int)

ADaM Architecture -- The Postdoc Deep Dive

ADSL (Subject-Level) -- The Spine

Exactly one row per subject. Every other ADaM dataset must merge to ADSL on USUBJID. Standard variables:

• USUBJID -- universal subject ID
• TRT01A / TRT01P / ACTARMCD / ARMCD -- planned and actual treatment, period 1
• TRTSDT / TRTEDT -- treatment start/end dates (derived from EX, not SDTM)
• AGE, SEX, RACE, ETHNIC -- demographics from DM
• RANDDT -- randomisation date
• DCSREAS / DCSREASP / DCSREASCD -- discontinuation reason (coded + verbatim)
• Population flags: ITTFL, FASFL, SAFFL, PPROTFL, EFFFL (Y/N flags for analysis populations)
• Stratification factors: STRATA1, STRATA2 (from randomisation)
• Baseline covariates that will be used as model covariates downstream

BDS (Basic Data Structure) -- Long Format Analysis Data

One row per subject per parameter per analysis timepoint per analysis flag. Used for ADVS, ADLB, ADEFF, ADQS, ADTTE.

Required variables:

• USUBJID, STUDYID -- merge keys
• PARAM, PARAMCD, PARAMN -- parameter name, code, number
• AVISIT, AVISITN -- analysis visit name, number
• ADT, ADY -- analysis date, analysis day (relative to TRTSDT)
• AVAL, AVALC -- analysis value (numeric, character)
• BASE -- baseline value (replicated per subject/parameter)
• CHG, PCHG -- change from baseline, percent change
• ABLFL -- 'Y' for the baseline record
• ANL01FL, ANL02FL -- analysis flags for primary/secondary analyses
• DTYPE -- derivation type ('LOCF', 'WOCF', 'AVERAGE', 'BOCF', or null for original)
• BASETYPE -- when multiple baselines per subject/parameter (crossover)
• EPOCH -- study period (SCREENING, TREATMENT, FOLLOW-UP)

# Example: derive ADLB BDS structure from LB SDTM
import pandas as pd

lb = pd.read_csv('LB.csv')
adsl = pd.read_csv('ADSL.csv')

# Filter to active tests
adlb = lb[lb['LBTESTCD'].isin(['ALT', 'AST', 'CREAT', 'HGB'])].copy()
adlb['AVAL'] = adlb['LBSTRESN']
adlb['PARAM'] = adlb['LBTEST']
adlb['PARAMCD'] = adlb['LBTESTCD']

# Merge subject-level treatment from ADSL
adlb = adlb.merge(adsl[['USUBJID', 'TRT01A', 'TRTSDT']], on='USUBJID')

# Compute analysis day
adlb['ADT'] = pd.to_datetime(adlb['LBDTC'], errors='coerce')
adlb['TRTSDT'] = pd.to_datetime(adlb['TRTSDT'], errors='coerce')
adlb['ADY'] = (adlb['ADT'] - adlb['TRTSDT']).dt.days + 1  # Day 1 = first dose

# Set ABLFL from LBBLFL
adlb['ABLFL'] = (adlb['LBBLFL'] == 'Y').map({True: 'Y', False: None})

# Derive BASE per subject/parameter
baselines = adlb[adlb['ABLFL'] == 'Y'][['USUBJID', 'PARAMCD', 'AVAL']]
baselines.columns = [, , ]
adlb = adlb.merge(baselines, on=[, ], how=)


adlb[] = adlb[] - adlb[]
adlb[] =  * adlb[] / adlb[]

OCCDS (Occurrence Data Structure) -- One Row per Event

OCCDS v1.1 (Nov 2021) handles AE, CM, MH. Variables: AEDECOD, AEBODSYS, AESEV, AESER, ASTDT (analysis start date), AENDT, TRTEMFL.

OCCDS v1.1 added TRTEM01FL through TRTEM##FL for multi-period treatment-emergent flags — essential for crossover and multi-phase studies where a single TRTEMFL is ambiguous.

ADTTE (Time-to-Event) -- The CNSR Convention Trap

The ADaM BDS for TTE v1.0 uses BDS structure with extra variables for survival analysis:

• STARTDT -- time origin (typically TRTSDT for OS; RANDDT for PFS; response date for DOR)
• ADT -- analysis date (event date if event, censoring date if censored)
• AVAL = ADT - STARTDT (+1 if "first day = day 1" convention)
• AVALU = 'DAYS' (or 'MONTHS' for some endpoints)
• CNSR -- censoring indicator. CONVENTION: CNSR = 0 for events; positive integers for censoring, integer encodes censoring reason
• EVNTDESC -- text description ('Death due to disease', 'Last alive contact')
• CNSDTDSC, SRCDOM, SRCVAR, SRCSEQ -- traceability back to SDTM source

The CNSR convention is OPPOSITE to most statistical packages, which use 1 = event. R survival::Surv(time, event) expects event=1; SAS PROC LIFETEST takes CENSORED= statement that's opposite to CNSR convention. This is a perpetual bug source. When passing ADTTE to analysis:

# Convert CDISC ADTTE CNSR to R/Python statistical convention
adtte['event'] = (adtte['CNSR'] == 0).astype(int)  # 1 = event for survival packages

Define-XML 2.1

Every ADaM dataset requires variable-level metadata in Define-XML 2.1 (FDA-recommended for studies starting on/after March 15, 2023). Fields per variable:

• Origin -- CRF, derived, predecessor SDTM variable
• Derivation rule -- free text or controlled algorithm
• Codelist -- linked controlled terminology
• Length, datatype, label

The FDA reviewer's Analysis Data Reviewer's Guide (ADRG) is now expected in every NDA/BLA — walks reviewer through how each analysis dataset was built.

Two-level traceability expectation: SDTM raw -> ADaM analysis-ready, with no orphan derivations. FDA reviewers explicitly trace AE counts in CSR table -> ADAE rows -> AE SDTM rows. Any break is a flag.

Treatment-Emergent AE -- The Convention Variation

ICH E2A (1995) defines an AE generically. TEAE is sponsor-defined:

TRTEMFL = 'Y' if AE.ASTDT >= TRTSDT AND AE.ASTDT <= TRTEDT + X days

X = post-treatment follow-up window. Common values:

• Small molecules: 28 or 30 days
• Biologics with extended half-life: longer (e.g., 60-90 days for mAbs)
• Cell/gene therapy: indefinite (lifelong monitoring expected)

Sponsor variation:

• Day-of-first-dose AE included as TEAE (FDA preference) vs excluded (some EMA reviewers)
• Partial-date imputation: impute day 15 if only month/year known, vs censor as missing
• Worsening of pre-existing AE: flagged via SEV change vs requires new PT (preferred term)

MedDRA SOC/PT hierarchy: AEs coded to MedDRA Preferred Terms (PT), grouped by System Organ Class (SOC). Clinically related PTs (e.g., 'Diarrhea' / 'Frequent bowel movements' / 'Loose stools') often combined via Standardized MedDRA Queries (SMQs) or sponsor-defined groupings. ADAE typically carries both AEDECOD (PT) and SMQ/group flags.

Baseline Derivation

ABLFL = 'Y' marks the record whose AVAL becomes BASE for all other records of the same subject/parameter.

Standard rule: last non-missing assessment on or before first dose (TRTSDT). If protocol mandates a specific baseline visit ('Day 1 pre-dose'), that visit's record is flagged.

# Derive ABLFL when SDTM baseline flag is missing/inconsistent
import pandas as pd

vs['VSDTC_dt'] = pd.to_datetime(vs['VSDTC'], errors='coerce')
vs = vs.merge(adsl[['USUBJID', 'TRTSDT']], on='USUBJID')
vs['is_pre_treatment'] = vs['VSDTC_dt'] <= pd.to_datetime(vs['TRTSDT'])

# Latest pre-treatment value per subject/parameter
baseline_records = (vs[vs['is_pre_treatment'] & vs['VSSTRESN'].notna()]
                    .sort_values('VSDTC_dt')
                    .groupby(['USUBJID', 'VSTESTCD'])
                    .tail(1))
baseline_records['derived_ABLFL'] = 'Y'

Critical detail: filter on VSBLFL='Y' (or LBBLFL='Y') as the primary source. Only fall back to derivation when the flag is missing. Trust the SDTM flag when present; CRF-level baseline designation embeds clinical judgement the analyst cannot reconstruct.

BASETYPE required when more than one baseline exists per subject/parameter (crossover studies, multi-period trials). Distinguishes "Period 1 Baseline" vs "Period 2 Baseline."

DTYPE values per CDISC controlled terminology: 'LOCF' (last observation carried forward), 'WOCF' (worst), 'AVERAGE', 'BOCF' (baseline observation carried forward), null for original. Pinnacle 21 flags any DTYPE value not in CT.

SUPPQUAL and the NSV Transition

SUPPQUAL (supplemental qualifiers) is the legacy mechanism for sponsor-defined variables that don't fit standard SDTM domain columns. Long-format QNAM/QVAL pairs:

supp = pd.read_sas('suppae.xpt', format='xport', encoding='utf-8')
supp_pivot = supp.pivot_table(
    index='USUBJID', columns='QNAM', values='QVAL', aggfunc='first'
).reset_index()
ae_enriched = ae.merge(supp_pivot, on='USUBJID', how='left')

For record-level SUPPQUAL (where IDVAR and IDVARVAL identify specific rows):

supp_record = supp[supp['IDVAR'] == 'AESEQ'].copy()
supp_record['AESEQ'] = supp_record['IDVARVAL'].astype(float)
supp_pivot_record = supp_record.pivot_table(
    index=['USUBJID', 'AESEQ'], columns='QNAM', values='QVAL', aggfunc='first'
).reset_index()
ae_enriched = ae.merge(supp_pivot_record, on=['USUBJID', 'AESEQ'], how='left')

The 2024-2026 SUPP transition: Therapeutic Area User Guides (TAUGs) increasingly use NS-- domain extensions or Non-Standard Variables (NSV) Registry-listed variables directly in the parent domain, instead of QNAM/QVAL pairs in SUPP--. Not a hard deprecation but the direction is clear. The Non-Standard Variables Registry at cdisc.org is the new canonical place to look up sponsor-extension variables.

Date Handling -- The Partial-Date Reality

dm['RFSTDT'] = pd.to_datetime(dm['RFSTDTC'], errors='coerce')
ae['AESTDT'] = pd.to_datetime(ae['AESTDTC'], errors='coerce')
ae['AEENDT'] = pd.to_datetime(ae['AEENDTC'], errors='coerce')

# Days from randomization to AE onset
ae_with_ref = ae.merge(dm[['USUBJID', 'RFSTDT']], on='USUBJID')
ae_with_ref['AE_ONSET_DAY'] = (ae_with_ref['AESTDT'] - ae_with_ref['RFSTDT']).dt.days

SDTM dates are ISO 8601 strings. Partial dates (e.g., '2023-03' without day) are common. errors='coerce' converts these to NaT rather than raising errors. For analysis requiring complete dates, CDISC conventions impute missing day as the 1st for start dates and the last day of the month for end dates, but imputation rules should match the SAP.

SDTM records include EPOCH (SCREENING, TREATMENT, FOLLOW-UP). For TEAEs, filter AEs to onset during or after the treatment epoch. Including pre-treatment AEs confounds the treatment effect estimate.

Validation -- Pinnacle 21 and CORE

Pinnacle 21 (Certara, formerly OpenCDISC) is the de facto FDA submission validation standard. Validates against FDA Validation Rules + CDISC IG conformance + Define-XML schema. Severity tiers:

• Reject -- submission will not be accepted
• Error -- must justify
• Warning -- should investigate

FDA Validation Rules are published quarterly by FDA Office of Translational Sciences; Pinnacle 21 wraps these into its rule engine.

CORE (CDISC Open Rules Engine, 2024) is a newer open-source alternative using YAML-defined rules from the CDISC Rules Catalog. Gaining traction but not yet at Pinnacle-21 parity for confirmatory submissions.

# Pinnacle 21 Community (free; appropriate for non-pivotal trials)
p21-community validate --rules sdtmig-3.4 --output-dir validation_output study_data/

Population Flags

Flag	Source	Purpose
ITTFL	DM all randomised	Primary efficacy population (ICH E9 default)
FASFL	ITT minus eligibility failures + no post-baseline	Practical primary (FAS = Full Analysis Set)
SAFFL	EX (received at least one dose)	Safety analysis (AE reporting)
PPROTFL	SE + DS + protocol-violation list	Per-protocol; sensitivity only
EFFFL	Sponsor-defined	Modified ITT variants

FAS vs ITT subtlety: FAS may exclude post-randomisation subjects (ineligibility, no post-baseline efficacy); ITT cannot. Many SAPs equate them; FDA may insist on stricter ITT at submission. Pre-specify both with explicit FAS exclusion criteria in the protocol.

Missing Data Considerations -- The Clinical Reasoning Layer

Before any imputation/complete-case decision, examine the DS (Disposition) domain to tabulate reasons for discontinuation by treatment arm. If discontinuation rates or reasons differ between arms, missing data is likely informative (MNAR) and standard MMRM-MAR is questionable.

ds = pd.read_csv('DS.csv')
dropouts = ds[ds['DSDECOD'] != 'COMPLETED']
dropouts_by_arm = dropouts.merge(adsl[['USUBJID', 'ARM']], on='USUBJID')
discontinuation_reasons = dropouts_by_arm.groupby(['ARM', 'DSDECOD']).size().unstack(fill_value=0)

This is the data-quality precursor to choosing the estimand strategy in trial-reporting (see ICH E9(R1)) — missing patterns informed by DS drive the choice between treatment-policy, hypothetical, or composite ICE strategies.

Common Pitfalls

Pitfall	Symptom	Solution
Event-level merged onto subject-level without aggregation	Row count inflates after merge	Aggregate first, then merge
First chronological record used as baseline	Misclassified baseline	Filter on VSBLFL='Y' / LBBLFL='Y'; derive only if missing
Character (xxORRES) used for analysis	Inconsistent numeric coercion	Use xxSTRESN (numeric standardised); missing xxSTRESN with present xxORRES means 'NOT DONE' or '<LLOQ'
ARM used in safety analysis	Crossover or actual-treatment differs	Use ACTARM for safety; ARM for ITT efficacy
RFSTDTC used as TEAE reference	Includes screening AEs	Use RFXSTDTC (first treatment); cite ICH E2A
ADTTE CNSR confused with stat-pkg convention	Wrong event/censoring assignment	CDISC: CNSR=0 means event; convert: event = (CNSR == 0).astype(int)
Partial date parsing error	NaT in date column	pd.to_datetime(..., errors='coerce')
SUPPQUAL granularity confusion	Wrong rows merged	Check IDVAR before choosing subject vs record-level merge
Non-standard column names treated as standard SDTM	Missing variables	Inspect actual columns; map to semantic roles
Pinnacle 21 not run before submission	FDA reject	Always validate against current SDTMIG and FDA Validation Rules before submission

Common non-standard column mappings

Standard SDTM	Common alternatives	Role
ARM / ARMCD	TRTGRP, TRT01P, treatment, group	Treatment assignment
AEDECOD	AEPT, ae_term, preferred_term	AE preferred term
AESEV (text)	AESEV (numeric 1-4), severity, AETOXGR	Severity / toxicity grade
USUBJID	SUBJID, subject_id, patient_id	Subject identifier
RFXSTDTC	trt_start, first_dose_date	First treatment date
LBSTRESN	lab_value_num, result_numeric	Lab numeric result

Quantitative Thresholds and Conventions

Threshold/Convention	Source	Rationale
TEAE window: 28-30 days post-treatment for small molecules	ICH E2A; sponsor convention	Mode of action, half-life inform window
RFXSTDTC for TEAE reference (not RFSTDTC)	ICH E2A	RFSTDTC includes screening; TEAE is post-treatment
ABLFL='Y' for last non-missing pre-dose	CDISC ADaM IG v1.3	Standard baseline definition; trust SDTM flag
ARM (planned) for ITT efficacy; ACTARM for safety	ICH E9	Crossover/PP-violation handling
Pinnacle 21 validation before submission	FDA Study Data Technical Conformance Guide	Standard quality gate; reject errors block acceptance
Define-XML 2.1 for studies starting >=March 15, 2023	FDA Study Data Standards Catalog	Older 2.0 still accepted for prior studies
Dataset-JSON v1.1 (Dec 2025; FDA notice April 2025)	CDISC + FDA Federal Register	Modern replacement for XPT v5; timeline pending
CNSR=0 for events, positive integers for censoring	ADaM BDS-for-TTE v1.0	OPPOSITE of R survival and most stat packages

Anticipated Reviewer Pushback

Pushback	Response
"RFXSTDTC or RFSTDTC for TEAE?"	RFXSTDTC per ICH E2A; RFSTDTC would include screening AEs
"Baseline from VSBLFL or derived?"	VSBLFL when present; documented derivation rule when missing
"Pinnacle 21 errors?"	All errors resolved or justified; warnings reviewed and documented
"Define-XML 2.1 ADRG provided?"	Yes — analysis dataset traceability documented to variable level
"ITT vs FAS reconciliation?"	Pre-specified in protocol with explicit FAS exclusion criteria
"OCCDS v1.1 multi-period flags?"	TRTEM01FL...TRTEM##FL pre-specified for crossover periods
"ADTTE CNSR convention conversion documented?"	Explicit: CDISC CNSR=0 means event; convert to event=1 for downstream R/Python

References

• CDISC. 2021. Analysis Data Model Implementation Guide (ADaMIG) v1.3.
• CDISC. 2021. Occurrence Data Structure (OCCDS) v1.1.
• CDISC. 2012/2024. ADaM Basic Data Structure for Time-to-Event Analyses v1.0.
• CDISC. 2025. Dataset-JSON v1.1.
• FDA. 2024. Study Data Technical Conformance Guide.
• FDA Federal Register Notice. April 2025. Dataset-JSON Pilot Comment Request.
• ICH. 1995. E2A: Clinical Safety Data Management -- Definitions and Standards for Expedited Reporting.
• ICH. 1998. E9: Statistical Principles for Clinical Trials.
• ICH. 2019. E9(R1) Addendum on Estimands and Sensitivity Analysis.
• Pinnacle 21 (Certara). 2024. Community Edition Validation Rules.
• PHUSE/CDISC. 2024. Dataset-JSON Pilot Reports.

Related Skills

• clinical-biostatistics/logistic-regression - Model binary outcomes from prepared ADaM/SDTM data
• clinical-biostatistics/trial-reporting - Use prepared analysis datasets for ICH E9(R1) estimands and CONSORT 2025 reporting
• clinical-biostatistics/missing-data-sensitivity - DS-domain reasoning informs estimand choice
• clinical-biostatistics/survival-analysis - ADTTE CNSR convention; time-to-event preparation
• expression-matrix/metadata-joins - General metadata joining patterns