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CDISC for Medical Writers

Christine Quagan, CC

Senior Medical Writer, PROMETRIKA, LLC

02 November 2018

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▪ Learn what CDISC is and about the purpose of its work.

▪ Learn the meaning of the acronyms CDASH, SDTM, ADaM,

SEND, and others, and the purpose of these data

standards.

▪ Understand the benefits of data standards in clinical

research and their effect on medical writing.

Learning Objectives

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Why Data Standardization?

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▪ In a clinical trial, each piece of data is

linked to the subject from whom it was

collected, the type of analysis

(demographics, efficacy, safety), and

the time of collection.

▪ Different methods were used to

describe clinical trial data collected in

databases. Several methods could be

used in the same clinical development

program, or by different sponsors

within a therapeutic area, causing

confusion.

▪ The clinical development community

needed a way to standardize data

collection and reporting.

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▪ Clinical Data Interchange Standards Consortium

What Is CDISC? What Is Its Mission?

▪ “Enable clinical research to work

smarter by allowing data to speak the

same language.”

▪ Mission – develop and support global,

platform-independent data standards .

. . to improve medical research.

▪ Founded 1997; standards developed

by volunteers; paid staff only recently.

(Sources: www.cdisc.org; https://www.cdisc.org/system/files/all/CDISC_Brochure.pdf, accessed 07 May 2018)

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SEND: Standard for Exchange of Nonclinical Data SDTM: Study Data Tabulation Model

PRM: Protocol Representation Model ADaM: Analysis Data Model

CDASH: Clinical Data Acquisition StandardsHarmonization

ODM: Operational Data Model

BRIDG: Biomedical Research Integrated Domain Group SDM: Study/Trial Design Model

CDISC Standards in Clinical Research

(Sources: www.cdisc.org; https://www.cdisc.org/system/files/all/CDISC_Brochure.pdf, accessed 07 May 2018)

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▪ Observation: a discrete piece of information collected during

a study.

▪ Variable: components of the observation, such as timing

and severity.

• Who, what, where, when, how much, how many

▪ Domain: a collection of observations with a common topic.

How SDTM Classifies Data

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Subject 052-003 had mild dizziness on Study Day 7.

Variable This is the CDISC Type

Subject 052-003 Subject ID Identifier

Dizziness Adverse Event Topic

Mild Severity of AE Qualifier

Study Day 7 Date of AE Timing

These are the major types of variables, defined by the role

they perform within the observation.

A Sample Observation

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▪ For each variable within each set, the Model contains a set

of four attributes:

• Name: ≤8 characters; “--” represents a two-letter prefix used to make

the variable name specific to the domain. What does CDISC call it?

EXAMPLE: STUDYID

• Label: ≤40 characters, in Title Case. What is it in plain English?

EXAMPLE: Study Identifier

• Type: character (“Char”) or numeric (“Num”). Will the result be words or

numbers?

EXAMPLE: Char

• Role: A description, which may define rules for use and/or population

of the variable. What type of variable is this?

EXAMPLE: Identifier

• For some sets of variables, whether the variable may be used in

clinical (SDTM) or pre-clinical environments (SEND).

Variable Attributes

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▪ Interventions - investigational, therapeutic, or other

treatments administered to the subject (with some actual or

expected physiological effect).

▪ Events - Planned protocol milestones and

occurrences/incidents independent of planned study

evaluations occurring before or during the trial.

▪ Findings - Observations resulting from planned and

unplanned evaluations, tests, questions, and

measurements.

Observation Classes

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▪ The model contains defined sets of variables that can

be combined to build a domain.

▪ Represent predefined domains with a specific

purpose, such as

Special Purpose: Demographics (DM), Subject Visits (SV).

Interventions: Concomitant Medications (CM), Exposure (EX).

Events: Medical History (MH), Adverse Events (AE).

Findings: Vital Signs (VS), Tumor Results (TR).

Other: (eg, Trial Design) Trial Arms (TA), Trial Elements (TE).

Observations Grouped into Domains

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▪ The SDTM Implementation Guide (SDTMIG).

• Current version is 3.2, 2013.

▪ Example: Medical History (MH)

Building Domains

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Before and After CDISC SDTM

▪ “Homegrown” names, eg:

AESDT: AE start date AESEV: Severity of AE

AEREPD: AE report date AEINT: AE interrupt treatment?

BEFORE

AFTER

▪ Standard names, eg:

AESDT → AESTDTC AESEV: Same in both versions

AEINT → AEACN PTNO → USUBJID

DIZZINESSSDR-02-001

Do you recognize any variable names in the sample program?

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▪ Supplements the SDTMIG and guides the

organization, structure, and format of standard clinical

trial tabulation datasets in a given therapeutic area.For example, in multiple sclerosis – populating medical history

Therapeutic Area User Guides

Source: Therapeutic Area Data Standards User Guide for Multiple Sclerosis, Version 1.0, 2014

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▪ SDTM and ADaM datasets.

▪ Define.xml – metadata about the datasets.

▪ The Pinnacle 21 Checker checks the conformity of the

submitted SDTM datasets to the standard, and the Validator

Report must be submitted to the FDA.

▪ Anotated CRFs (CRFs with field names).

▪ Goal is traceability:

CRF → SDTM → ADaM → Clinical Study Report

What is Submitted to Regulatory Authorities?

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▪ Begin with the end in mind: protocols are written so that the

data can be collected in a CDISC-compliant format.

CDISC in the Medical Writing World:

Protocol Standards

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CDISC in the Medical Writing World:

Protocol Standards

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▪ Standard definitions of clinical trial terms

CDISC in the Medical Writing World:

Glossary

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▪ The ISS and ISE usually include pooled analyses,

especially of phase 3 data.

▪ Data can only be pooled if the datasets name the same

item by the same name. Otherwise, programmers must map

data to SDTM standard variable names, a complex process.

▪ An example from an ISE SAP: Analysis data sets for Studies A and B

were structured and formatted following ADaM IG for the individual

clinical studies. Because the data collected and the analysis rules used

were consistent between the two studies, their datasets were combined

to create pooled analysis datasets, which in turn were used to generate

the ISE tables and figures.

CDISC Standards and the Integrated

Summaries of Safety (ISS) and Efficacy (ISE)

http://crosnt.com/integrated-summaries-regulatory-requirements/ Accessed 23 September 2018

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▪ In 2010, JMP Life Sciences discussed a SAS program

that produced 683 SAE narratives in <1 minute.

▪ Variables from various domains (DM, MH, AE).

CDISC Standards and SAE Narratives

http://www.sctweb.org/public/meetings/2012/slides/2B%20-%20Scott.pdf Accessed 23 September 2018

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▪ CDISC standards are under continuous development

to standardize the collection of all healthcare-related

data:

• Preclinical Consumer Health

• Clinical Electronic Health Records

▪ CDISC-standardized data are easier for statisticians

to analyze, permitting clearer insights.

▪ CDISC will increase linkage among disciplines (for

example, clinical and health outcomes), providing

more material for writers.

Impact of CDISC Standards