cdash and sdtm: what are they and why do we need both … · and uses the same controlled...

CDASH and SDTM: What Are They and Why do We Need Both Standards?

Kit Howard, MSSenior Education Expert CDISC

Learning Outcomes

Define SDTM and CDASH

Identify how SDTM and CDASH are the same

Discuss how and why are they different

Explain why it is best to use both

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10/23/2018Footer Text 3

Metadata

Standards are…

What is Metadata?

37What is this?

Number? Could be char?

Integer? Decimal places?

Prime?

Age? In weeks?

Percent?

Lab result?

Scale score?

What Are CDASH and SDTM?

Clinical Data Acquisition Standards Harmonization

What is CDASH?

A set of documents

CRF content standards for basic questions in medical research

Standards that “face” the site

Collaboration among data management, programming,

statisticians, clinical, et al

Metadata supporting data capture design for SDTM datasets

CDASH Has Two Parts


Model Implementation Guide

CDASH Model


Model

Gives us generic structures

for building standards

Provides some rules and

guidance on how to use

those structures

CDASH Implementation Guide


Implementation Guide

Gives us implemented

“domains” based on the

model, e.g., AEs, Con Meds,

Demographics, etc.

Also provides best practices

for CRF design &

production, information

about what conformance

means, and…

ImplementationsObservation

Class DomainCDASHIG Variable

CDASHIG Variable Label Question Text Prompt

Data Type

Events AE AETERM Adverse Event Reported Term

What is the adverse event

term?

Adverse Event

Char

Events AE AESTDAT Adverse Event Start Date

What is the adverse event

start date?

Start Date Char

CDASHIG Core

Case Report Form Completion Instructions

SDTMIG Target

Mapping Instructions Implementation NotesHR Record only one

diagnosis, sign or symptom per line …

AETERM Maps directly to the SDTMIG variable …

Can be represented either as …field to capture

verbatim terms ... or could be pre-printed …

HR Record the start date …using this format (DD-MON-YYYY).

AESTDTC … concatenate all collected CDASH START DATE and TIME

components …

N/A

Controlled Terminology


CodeCodelist

Code

Codelist

Extensible

(Yes/No)

Codelist

Name

CDISC

Submission

Value

CDISC

Synonym(s)CDISC Definition

C66768 No Outcome

of Event

OUT Outcome of

Event

A condition or event that is attributed

to the adverse event and is the

result or conclusion of the adverse

event. (NCI)

C48275 C66768 Outcome

of Event

FATAL 5; FATAL;

Grade 5

The termination of life as a result of

an adverse event. (NCI)


of Event

NOT

RECOVERED/

NOT

RESOLVED

One of the possible results of an

adverse event outcome that

indicates that the event has not

improved or recuperated. (NCI)


of Event

RECOVERED/

RESOLVED



indicates that the event has


… … … … … … …

• Consistent CRFs – questions, answers, assumptions

• Ease of use for investigative sites

• Data quality rules that enable SDTM’s quality assumptions

Data Collection & Management

• Beginning with the end in mind

• Smooth flow of data from collection to submission

• Data integrity and traceability back to the source

Supports

Study Data Tabulation Model

SDTM Also Has Two Parts


Model


SDTM Model


ModelGives us generic structures

for building standards

Provides some rules and

guidance on how to use

those structures

SDTM Implementation Guide



Gives us implemented

“domains” based on the

model, e.g., AEs, Con Meds,

Demographics, etc.

Provides assumptions,

examples, overall guidance,

conformance rules…

And Uses the Same Controlled Terminology


CodeCodelist

Code

Codelist

Extensible

(Yes/No)

Codelist

Name

CDISC

Submission

Value

CDISC

Synonym(s)CDISC Definition

C66768 No Outcome

of Event

OUT Outcome of

Event

A condition or event that is attributed

to the adverse event and is the

result or conclusion of the adverse

event. (NCI)


of Event

FATAL 5; FATAL;

Grade 5

The termination of life as a result of

an adverse event. (NCI)


of Event

NOT

RECOVERED/

NOT

RESOLVED



indicates that the event has not



of Event

RECOVERED/

RESOLVED



indicates that the event has


… … … … … … …

• Logically groups data into topics

• Rules: naming conventions, data types, controlled terminology

• All clean collected data plus some derived

Defines predictable representation of clean collected data

• Data aggregation for submissions, data warehouses, registries

• Regulatory review, IRB reporting, safety surveillance

• Hypothesis generation, cross-product analyses…

Supports

SDTM

Transformation

CDASH

DatasetsData

Capture

34%

66%

Notidentical

Identical

11%

89%

Notidentical

Identical +mappings

Missing Data

Show me the data, not

lack of data

SDTM assumes that if there is no record then nothing

happened. This only works if it was checked in data

capture, which requires a question and record (e.g., Were

there any AEs?)

Absence of evidence is

not evidence of

absence: must check

that missing data is

missing

CDASHSDTM

RationaleUSUBJID AETERM AESTDTC AEENDTC

2006-34-1022 HEADACHE 2006-10-14 2006-10-20

2006-34-1022 RASH 2006-10-14 2006-10-21

2006-34-1024 TIRED 2006-09-24 2006-09

2006-34-1024 HEADACHE 2006-09-24 2006-09-24

SDTM

CDASH

Human vs Machine Readable Data

Machine-

readable:

Dates/Times: ISO

8601, 1 variable,

YYYY-MM-

DDThh:mm:ss

Duration: P6H

SDTM machine-readable formats for variables

such as dates are good for data reusability but

are not user-friendly for data capture. There is

more chance for error when people record data

in unfamiliar formats

Human-readable:

Dates/Times: 2 or

more variables,

DD- MM-YYYY,

HH:MM:SS

Duration: 6 HOURS

CDASHSDTM

Rationale

USUBJID HOTERM HOSTDTC HODUR

ABC1201 Hospitalization 2011-08-06 P6H

SUBJID HOTERM HOSTDAT HOCDUR HOCDURU

1201 Hospitalization 06-AUG-2011 6 HOURS

SDTM

CDASH

Data Organization

Each CRF should have the data that makes sense to

collect together. Per CDASH, this can mix domains if

standard variable names are used, and in the end the

data appear in the right domains.

In SDTM, data MUST appear only in the correct domain.

Data on each CRF is

driven by what is

captured together, not

necessarily by domain

CDASH

Data must be

organized into

datasets by domain

SDTM

Rationale

USUBJID SUBJID BRTHDTC SEX RACE

ABC1201 01 1948-12-13 M WHITE

ABC1202 02 1955-03-22 M BLACK

DM SDTM

USUBJID RPTESTCD RPORRES RPSTRESC

ABC1201MENOSTA

TPremenarchal Premenarchal

ABC1201 NUMLIV 2 2

RP SDTM

DM CDASH

RP CDASH

Unavailable Variables

In doubleblind studies, treatment and dose are

unknown until the blind is broken;

SDTM primary focus is pooling data for submission;

CDASH primary focus is individual studies

Some variables

cannot be collected

in the way needed

for summarization

and analysis, e.g.,

ECDOSE

CDASH

Some data needed for

summarization &

submission aren’t available

until the study is over, e.g.,

exposure in double blind

studies

SDTM

Rationale

Exposure as Collected EC CRF

USUBJID EXTRT EXDOSE EXDOSU EXDOSFRQEXSTDTC EXENDTC

ABC1201 BestDrug 350 mg BID

2011-08-

06 2011-09-01

SDTM

CDASH

Ensure asking same question using the same answer lists as analyzing

Optimize site data requirements and structure for transmission & analysis

Supports traceability back through data collection

Minimizes programming and validation resources and increases quality when transferring data from capture to tabulation

Can address the points in all the previous slides

Helps to “future-proof” the data for warehousing

Some Benefits of Using Both

Thank You!Bess [email protected]

Kit [email protected]

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