© CDISC 2012

1

Institute of Medicine Workshop:

DIGITAL DATA PRIORITIES FOR

CONTINUOUS LEARNING

IN HEALTH AND HEALTH CARE

“Data Quality in Clinical Research”

23 March 2012

Rebecca D. Kush, PhD

President and CEO, CDISC

© CDISC 2012 2

© CDISC 2012

JPMA Presentation –

Data Quality for Clinical Research

3

© CDISC 2012

Qu

ality

Re

qu

ire

me

nts

Type of Project

Non-medical

Sales and

Marketing

Signal or

Trend

Detection

Regulated

Research

Basic

Research

Signal

Validation;

Active Safety

Surveillance

“Sushi-grade”

“All you can eat

buffet grade”

Clinical

Decisions

SP

EC

UL

AT

ION

by

R.D

. K

us

h

© CDISC 2012

Key Messages

• Regulated Clinical Research requires ‘sushi-

grade’ data quality

• Building quality in from the beginning is ideal

• Data standards improve data quality

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© CDISC 2012

Healthcare and Clinical Research:

Parallel Universes

Source: Landen Bain, CIO, DUMC ~ 2004

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“The Plight of the Site”

• For clinicians doing research today…

~ 50-60 % of trials - data collected on paper (3- or 4-

part NCR paper)

~ 40-50 % of data are collected by eClinical “point

solutions” (electronic data capture tools)

data are entered 4-7 times total, 2-3 times by the

clinicians or study coordinators

An average active study site has 3 disparate solutions

• To report an unexpected or serious adverse

event does not fit into normal clinical care

workflow and takes excessive time.

• Most clinicians do one regulated clinical research

study and no more.

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Current Typical Research Data Flow

© CDISC 2012 9

Data Quality in Clinical Research: Cost of Error Correction

Site

~$30

Co

st

of

Qu

ality

$

(1

99

5)

Point in Process for Error

Detection/Correction (per error)

CRO

~$75

Stats-

Tables

Listings

>$5,000

QA of

Final

Report

>$8,000

Database

Lock

© CDISC 2012

CFR - Code of Federal Regulations

Title 21 - FDA

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TITLE 21--FOOD AND DRUGS

CHAPTER I--FOOD AND DRUG ADMINISTRATION

DEPARTMENT OF HEALTH AND HUMAN SERVICES

SUBCHAPTER A--GENERAL

PART 11 ELECTRONIC RECORDS; ELECTRONIC SIGNATURES

The regulations in this part set forth the criteria under which

the agency considers electronic records, electronic signatures,

and handwritten signatures executed to electronic records to be

trustworthy, reliable, and generally equivalent to paper records

and handwritten signatures executed on paper.

© CDISC 2012 11

Mapping (Source to Submission)

© CDISC 2012 12

… but also Submission to Source

Trustworthiness

© CDISC 2012 13

ODM & Audit Trail

What

Why

Who

When

© CDISC 2012 14

The “Road” to Quality Clinical Data

• Build quality into the system

• Train and educate

site personnel, project team and reviewers/auditors

• Decrease the amount of data collected

• Define the data set needed and specify requirements

• Standardize formats and procedures

• Also plan for data quality during post-marketing

• Decrease the number of times data are ‘handled’

(Note: Anticipated ‘by-products’ of these steps to improve

quality are increased efficiency and lower costs.)

Source: Assuring Data Quality and Validity in Clinical Trials

for Regulatory Decision Making: Workshop Report, 2000

© CDISC 2012 15

Optimizing the Process

auto

reconciliation

data

conception

(e)CRFs (e)Source

Documents

EHR

eSource Healthcare

Delivery Clinical

Research

~ 1997

B. Kisler, R. Kush

© CDISC 2012

CRO or Partner

Reviewers (e.g. Research Partner,

Sponsor, Registry, Regulator, IRB, DSMB,

Quality Measures)

Public Registries,

IRB, DSMBs EDC

EHR

Data Sources

Care and/or Research Site (Healthcare Location,

Investigator, Site Personnel)

Study Sponsor (e.g. ARO, CRO, Vendor, Principal Investigator, potentially AHRQ…)

Std. Common

Research

Dataset (+)

EHR

De

-id

en

tifi

ed

Dat

a Site

Research

Archive

Research Results,

eSubmission

Standard Formats

Patient Value: Quality of Healthcare, Safety

Research informs healthcare more effectively Build quality into process at beginning

Regulatory Authority

Scientific

Pub-

lication

Continuity

of Care

Doc RFD*

Interoperability

Specification

© CDISC 2012

Press Release: June 10, 2010

Copyright C-Path 2011 17

Initial database required mapping to a

standard (CDISC); can now be leveraged

to collect data using the standard

Database now has >6,000 research

study patients

Now being used by > 200 qualified

research teams in 35 countries

Efficient collaboration

Better science

Regulatory efficiency

Why Standards ?

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ATM

Clinical Trials

Date of Birth

Jan. 15, 2011

January 15, 2011

1/15/11

1/15/2011

15/1/11

15 January 2011

15-1-11

2011-1-11

Clinical Trials

What is the average

age of patients

enrolled in 11 industry

trials for Alzheimer’s

Disease?

© CDISC 2012

What was learned?

ADAS-Cog Variability

ADNI J&J Wyeth sanofi-aventis Pfizer AstraZeneca Abbott

Item 1 Word Recall Word Recall Word Recall Word Recall Word Recall Word Recall Word Recall

Item 2 Commands Name Obj/fing. Name Obj/fing. Commands Name Obj/fing. Name Obj/fing. Name Obj/fing.

Item 3 Constr. Praxis Delayed recall Commands Constr. Praxis Commands Commands Commands

Item 4 Delayed recall Commands Constr. Praxis Delayed recall Delayed recall Constr. Praxis Constr. Praxis

Item 5

Naming Obj/fing. Constr. Praxis Idea Praxis Name Obj/fing. Constr. Praxis Idea. Praxis Idea. Praxis

Item 6 Idea. Praxis Idea Praxis Orientation Idea. Praxis Idea. Praxis Orientation Orientation

Item 7 Orientation Orientation Word Recog Orientation Orientation Word Recog Word Recog

Item 8 Word Recog. Word Recog. Remem. Instr. Word Recog Word Recog Remem. Instr. Spoken Lang Abil.

Item 9 Remem Instr. Remem Instr. Spoken Lang. Abil. Remem. Instr. Remem. Instr.

Spoken Lang. Abil. Comprehension

Item 10 Comprehension Spoken Lang. Abil.

Word Finding Dif.

Spoken Lang Abil.

Spoken Lang Abil.

Word Finding Dif.

Word Finding Dif.

Item 11

Word Finding Dif.

Word Finding Dif. Comprehension

Diff. Spont. Speech

Word Finding Dif. Comprehension Remem. Instr.

Item 12

Spoken Lang. Abil. Comprehension Concentration Comprehension Comprehension Concentration

Item 13 Number cancel. Concentration Concentration Concentration

© CDISC 2012

Now published….

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CDISC Alzheimer’s Disease SDTM User Guide (Version 1.0) ©

See also “Questionnaires” on the CDISC website

under Standards & Innovations.

CAMD Cognition Test Data

10-Year Disease Progression

by Severity at Entry

Mean (line) and 90% Credible Intervals* (gray shaded area)

* the posterior probability of the average individual's mean ADAS-cog being in the interval is 90%

Mild Moderate Severe

ADAS-cog

© CDISC 2012

• Data from individual clinical trials of 200-400 patients with Alzheimer’s

Disease had limited power and frequently failed due to variability in

outcome and small sub-groups.

• Mapping control arm data to a standard format for >6,000 patients in 20

trials created a dataset with higher quality (common methodology for

ADAS-cog) and greater power to assess variables affecting progression

Severity and age at entry

ApoE4 genotype

• Data standards can

1. Increase learning from clinical research study analysis

2. Facilitate data sharing across research studies

3. Create databases with which to design more informative and

efficient research studies

NOTE: Data standards are most valuable (significantly reducing time and

resources) when implemented from the beginning.

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Conclusions from CAMD

© CDISC 2012 24

Value of Standards

Using the data wisely……

Making sure it is

accurate, that it is

used as agreed,

and that we can find it!

We owe it to the patients

who agree to participate

in research studies and

share their data.

© CDISC 2012

Key Messages

• Regulated Clinical Research requires ‘sushi-

grade’ quality data

….and this translates to a robust quality process that is

not easy to execute and must be trustworth

• Building quality in from the beginning is ideal

….adding in quality at the ‘back end’ (e.g. through

mapping or normalization) can certainly be done but

only at a high cost (time and resources)

• Data standards improve data quality

…..especially when implemented in the data collection

steps at the beginning

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© CDISC 2012

Research findings to inform

healthcare decisions

Information from healthcare (private, aggregated)

to enable research

•Discovery of new therapies •Understanding diseases •Assessing efficacy •Monitoring safety •Public health/quality evaluations •Understanding responses (genomics, biomarkers) •Testing/comparing therapies (CER) •Post-marketing surveillance

•Quality healthcare •Informed decisions •Personalized medicine •Patient safety and privacy •Public health •Improved therapies •Efficiencies/reduced costs

Research Healthcare

© CDISC 2012 28