sas_unlocking the value of clinical trials

8/3/2019 SAS_Unlocking the Value of Clinical Trials

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WHITE PAPER

Uc Vu Cc D

Bring repeatability and automation to the data integration processwith SAS® Clinical Data Integration



UnloCk the ValUe o CliniCal Data

Table o Contents

Executive Summary .........................................................................................1Introduction .....................................................................................................2

Improve Costs, Quality and Efciency with Standards and Automation .......3

Driving Competitive Advantage or CROs and Biopharmaceuticals ..............4

CROs ............................................................................................................4

Pharmaceutical and Biotechnology Companies ..........................................5

Automating Data Integration and Standardization ........................................6

The Future o Standards ..................................................................................8

Closing Thoughts .............................................................................................8

About SAS ........................................................................................................9

A Closer Look .................................................................................................10




Executive Summary

The lie sciences industry is under pressure to accelerate time to market or newcompounds – at lower cost. A key to compressing the cycle is to streamline the

data management tasks that consume as much as 65 percent o the clinical trial

process, by some estimates.

Traditionally, the process o managing clinical trials data has been cumbersome

and resource-intensive. The data needed or analysis o drug saety and ecacy

resides in multiple, disparate systems. T ime-consuming and expensive manual

coding is required to integrate all that data into analysis-ready orm. Further data

manipulations are required to prepare regulatory submissions. Every clinical trial

brings a new data integration and transormation project, requiring just as much

programming eort as the previous ones.

There is much room or improvement in the traditional modus operandi by taking

advantage o industry standards and automated data integration processes.

Contract research organizations (CROs) can reduce the cost o their services

while simultaneously growing revenues through new service oerings such

as legacy data migration projects and Clinical Data Interchange Standards

Consortium (CDISC) standards consulting. By eciently standardizing legacy

clinical data, pharmaceutical and biotechnology companies can unlock the

scientic and business value hidden in their clinical repositories. In addition, these

companies can leverage their standardized clinical data or more ecient and

eective cross-study data analysis, review and utilization.

With SAS Clinical Data Integration, clinical research proessionals can improve

eciency, quality and speed in collecting, managing, analyzing, reporting and

assessing data rom clinical trials.

Prebuilt models automatically transorm legacy data to CDISC standards.

Research analysts can readily customize the models using visual interaces,

create new ones and reuse the work o others. Embedded data quality routines

ensure standard, trusted clinical data or analysis within and across clinical trials.

Consistent, validated data structures enable timely, seamless fow o inormation

among all the parties involved in a clinical trial.

Industry analysts have stated that automated data integration and validation can

trim 30 to 50 percent rom the clinical trial cycle. Field experience with the SAS

solution has shown that even greater savings are possible.

This white paper makes a case or implementing data standards and applying

automated processes or managing data throughout the clinical trials process,

rom study design to regulatory submissions.

1




Introduction

Does the experience o this lie sciences executive match the current reality in

your organization?

“We also had a number o disparate systems holding various pieces o inormation

and they didn’t talk to each other, and we were actually … incapable o … having the

inormation at our ngertips …”

The workfow o clinical trials depends on multiple, complex data interactions that

may occur across multiple organizations. Inormation collected rom study subjects is

deposited into a clinical repository, painstakingly transormed to suit study purposes,

analyzed to determine product ecacy and saety, and then used to prepare the

regulatory submission.

By some estimates, as much as 65 percent o the t ime it takes to conduct a clinical

trial is spent on data interchange among collaborating entities. The process entails

a series o exchanges between sponsors and investigators, between sponsors and

CROs, between CROs and labs, between sponsors and regulators, and so on. The

growing volume and complexity o these data interactions is seen as a primary cause

or escalating costs in drug development.

The ideal o converging on a set o data standards such as those rom CDISC to

streamline collaboration is gaining momentum – but acing three key obstacles

as well:

1. Collaborating organizations rarely have compatible systems.

In many cases, the parties involved in the process o bringing a drug to market

do not share a common corporate data network, electronic data capture tool,

clinical data repository, statistical computing environment or clinical reporting

system. Key clinical inormation is scattered across disparate IT systems or

platorms that use dierent data denitions and ormats. This inconsistent data

must be aggregated and transormed beore it can be used or meaningul

analysis.

2. Data transformation processes tend to be highly manual.

In many cases, data preparation is treated as a unique project or each clinical

study. Time-intensive manual coding and processes are required to prepare data

or analysis. These processes are run protocol by protocol, oten edited and

updated to refect the unique nature o the study, and then executed many times

as data is updated over the course o the trial. And don’t orget the dramatic

schedule impact o last-minute changes identied by the project team.

2

■ Clinical study data is typically

scattered among multiple clinical

systems and stored in multiple

formats across multiple operating

environments and organizations.




This highly manual approach is not repeatable across studies, nor does this

process scale without adding expensive headcount. The company misses

opportunities to reuse processes, code and inormation. As a result, more

programmers are needed to support additional studies. And still there is the risk

o data corruption rom coding errors and inconsistencies.

3. Standards are not necessarily seen as a top priority.

For one, they are not required – yet. The US Food and Drug Administration (FDA)

has recommended the submission o clinical data in CDISC SDTM (Study Data

Tabulation Model) ormat, but has not mandated the use o standards. Within

the lie sciences industry, conversations about standards are generally moving

rom a discussion o “what and why” to “how and when.” But, when aced with

competing priorities, is the implementation o standards truly a top priority within

most lie sciences organizations?

Some may argue that – with limited programmer resources – high-priority

drug development projects must take precedence over some possible uture

requirement. Some organizations would assert that they can perorm business

activities aster using their own unique processes and data ormats. Internally,

that may be true, but since so much clinical trial activity is collaborative outside

the organization, unique processes and data ormats hinder the overall fow o

the process.

Improve Costs, Quality and Efciency

with Standards and Automation

Agreement on data standards enables seamless inormation fow through the entire

pre-clinical and clinical research process, rom protocol design to various sources

or data collection, data management, analysis and reporting, regulatory submission

and electronic data archive.

CDISC standards such as SDTM, Analysis Data Model (ADaM) and Operational

Data Model (ODM) support interoperability among all participants in the clinical trial

process. First available in 2001, CDISC standards were initially slow to be adopted

but are now gaining momentum.

The business case is compelling. One study concluded that adopting standards

could simpliy data aggregation and reduce rework by 35-40 percent – and increase

overall eciency o study startup, analysis and reporting by 30 percent. The study

concluded that those who ail to adopt standards and appropriate automation tools

will take 30 to 50 percent longer to complete clinical trials.

3

■ Inefficiencies in the collection

and preparation of clinical data

for analysis can slow the pace ofdrug development and dramatically

increase the cost of bringing a

new drug to market.




The potential or cost savings is enough to make the most tradition-bound

organization take notice. Consider a single clinical trial with an eight-month cycle time

(excluding subject participation time) and an average daily study cost o $37,000 per

day (an estimate developed by Tuts Center or Drug Development). The reduction in

cycle time would yield $9 million in cost savings or a typical trial – $15.7 million per

submission or a trial on the critical path.

In addition, the adoption o standards with automated clinical data integration would

improve the quality o clinical research by:

• Decreasingstartupandreportingtimeforclinicaltrials.

• Improvingcommunicationsamongprojectteams.

• Reducingthechanceofhumanerrorindatamanagementprocesses.

• Preservingknowledgefrompriorstudiesinareusablefashion.

• Enablinginvestigatorstoanalyzeadverseeventsandtrendsacrossaseries

o trials.

Driving Competitive Advantage

or CROs and Biopharmaceuticals

Delivering true competitive advantage is a dicult task in today’s lie sciences market.Using SAS Clinical Data Integration, many SAS customers are enjoying the benets

o competitive advantage derived rom lower costs, higher revenues and better

market awareness.

CROs

In order to remain competitive, CROs must deliver their services aster, better and

cheaper than both competing CROs and the sponsor’s own internal departments.

This is a major challenge or even the most ecient organizations.

There is tremendous potential or CROs to extract signicant competitive advantage

rom both a cost and revenue standpoint using SAS Clinical Data Integration. Major

cost savings are possible or the delivery o analytic services based on ecient

standardization and automation during analytical data preparation. More importantly,

CROs have the opportunity to grow revenue through more competitive bids and

the development o new service oerings to win legacy data migration projects and

consulting projects.

4

“CDISC standards implemented at

the beginning of a clinical study

can reap savings of 70-90 percent

of the startup stage time and

cost, for an overall study resource

savings of 60 percent (outside of

the subject participation time). The

resulting efficiencies would allow the

industry as a whole to save billions

of dollars and also to concentrate

on developing products that meet

escalating safety, public health andregulatory requirements.”

Statement rom the Clinical Data

Interchange Standards Consortium

■ CROs can extract significant

competitive advantage from both a

cost and revenue perspective using

SAS Clinical Data Integration.




Pharmaceutical and Biotechnology Companies

Most pharmaceutical and biotechnology companies have a wealth o existing clinical

trial data that is a largely ignored, yet invaluable corporate asset. Standard clinicaldata can easily be used to derive additional value by reducing the number o new

trials, identiying new indications or marketing claims or a compound, or designing

more ecient study protocols.

SAS Clinical Data Integration can eciently standardize these clinical inormation

assets at a lower cost than existing practices. Once a repository o historical

standard clinical data has been developed, pharmaceutical and biotechnology

companies can unlock the signifcant scientifc and business value hidden

in their clinical data assets. The possibilities or driving competitive advantage

are signicant:

• Identifynewcomparative eectiveness signals that uncover new revenueopportunities through the identication o previously unknown advantages over

competitor therapies.

• Leverageexistingdatatomakenewmarketingclaimsoruncoverpotential

new indications.

• Improvetrialdesignandplanning.

• Automatethecross-studyintegrationefforttoderivevaluefromexistingdata

assets with reduced cost and improved data quality.

• Speeddatapreparationformedicalpublications.

• EasetheintegrationeffortfromM&Aandlicensingdealsbyautomatingmigration

or acquired data assets through data standards.

For current clinical research programs, SAS Clinical Data Integration can eciently

prepare and validate SDTM data or submission, speed timelines or analysis and

reporting by standardizing “in-process” data and support the exploration o data

across trials or business and scientic value.

5

■ Pharmaceutical and biotechnical

companies can easily automate

cross-study integration efforts,

reducing cost and increasing quality

to derive value from existing clinical

data assets.




Automating Data Integration and Standardization

SAS Clinical Data Integration brings repeatability and automation to the processesassociated with creating analysis-ready data. Clinical research proessionals now

have an easy-to-use visual interace or transorming, managing and veriying the

creation o standard data, or CDISC or other standards.

The solution provides the essential capabilities in our key areas:

• Aggregatingdatafrommultiplesources.

• Mappingdisparatedatatoauniform,consistentdatastructure.

• Cleansingthedatatoensureatrustworthybasisforanalysis.

• ValidatingthetransformationofdatatoCDISCorinternalstandards.

Data aggregation brings together clinical study data that is scattered among

multiple clinical systems and stored in multiple ormats across multiple operating

environments and organizations. Typical data sources include laboratory data, EDC

data,IVRSdata,patientdiarydata,pre-clinicaldata,CDMSdataandCTMSdata–

among others.

SAS Clinical Data Integration can access all o this data regardless o the source

and ormat.

Data mapping transorms clinical data to standard data (such as CDISC SDTM),

transorms operational data to analysis data sets, or combines data rom multiple

studies to support cross-study data analysis. Automated data transormationprocesses replace the inecient, costly manual coding used or most data mapping

eorts today.

The SAS solution has prebuilt transormations or CDISC models; those

transormation routines can be extended and customized as needed. Data

transormations are designed in easy-to-use visual interaces using standard SAS

Data Integration Studio unctionality.

Through metadata, the solution documents a ull mapping o data source (where the

data came rom), data manipulations (how the data has been altered) and the nal

destination or data. Impact analysis reports clariy the impact o any change to the

process, such as changes to incoming data ormats, data standards or additional

data required or analysis data sets.

Data preparation processes cleanse the data to deliver consistent, trusted and

veriable clinical inormation. Embedded data quality routines eliminate the problems

o inaccurate, contradictory and inconsistent data. With an accurate, real-time view o

clinical inormation, you can address potential issues beore they aect a study. Plus,

you can understand your compound aster through more requent interim analysis o

high-quality data.

6

■ The ability to provide fast, efficient

access to clinical data, regardless of

the location or source, is becoming a

critical capability for organizations to

improve time to market and contain

clinical research costs.

■ Build and document data

transformations with a user-friendlyGUI interface. Data transformations

are easy to explain to get new team

members up to speed quickly – and

easy to reuse, to reduce the need to

write unique code for each study.

■ SAS Clinical Data Integration

provides an accurate real-time view

of clinical information through the

application of automated data quality

and data transformation routines.




7

Data standardization and validation automates the process o validating CDISC

standards and/or an organization’s internal data standards. The solution is fexible

enough to map to dierent standards, such as dierent versions o SDTM or custom

standards as required by a specic therapeutic area, development program or

organizational entity. SAS Clinical Data Integration can evolve to support emerging

and uture health care data standards, with liecycle management or standards as

they evolve.

Metadata – The Power Behind Automated, Repeatable

Clinical Data Integration

The automated capabilities described in this paper are made possible

by metadata – data about data. Metadata records and tracks the

sources, transormations, uses and destinations o data. With shared

metadata, clinical data integration becomes a repeatable, auditable andautomatic process.

Suppose you get a request to rerun a data transormation, perhaps

because an SDTM model or its source data was updated, or there was a

suspected error in the process.

• Without metadata, you would have to look at every piece o the puzzle

to determine what actually needs to be done to address the situation.

The detective work might have to span many transormations. For

example, i the request is being driven by a new demography data set,

you would need to manually nd every place where that demography

data set has been used.

• Incontrast,a metadata-driven process automatically identies what

parts o the process need to be rerun – and then runs them.

A largely hands-o, automated approach reduces workload but also makes the next

project extremely straightorward. You already have a working model or many or

even all o the data transormations that might be required. Simply update the data

source reerences (one small subset o metadata) and go. Each subsequent project

can build on the previous ones, each project being in turn less resource-intensive

and more ecient than the previous ones.

With SAS Clinical Data Integration, you can build data transormation processes

to standardize legacy and other disparate data to CDISC models, customize the

embedded CDISC models and create new models to match your organization’s

business requirements.

In the process, you ensure the proper use o CDISC or custom standards, deliver

cleaner data or analysis and submissions, while decreasing costs and time

to submission.

■ Visually convert legacy data to

standard data – validating both

data structure and content forconformance to CDISC and/or

internal standards – while ensuring

proper use of standards.



8


The Future o Standards

SAS maintains a strong commitment to the development and implementation o data

standards in the health sciences industry. As a CDISC member since 2000, SAS has

been a leader in the development o vital data standards or the lie sciences industry.

SAS is making certain that our products and solutions support the implementation o

key CDISC models.

Data standards or the health sciences will continue to evolve and the importance

ofbothHL7andCDISCstandardswillincrease.Futurebreakthroughhealthcare

discoveries will be powered by simpliying access to key health care inormation

through the power o industry data standards.

In order to take ull advantage o constantly evolving industry standards, your

organization requires sotware that both supports and easily incorporates newstandards. You can rest assured that SAS has a strong track record or supporting

key industry data standards within SAS sotware – both now and in the uture.

Closing Thoughts

In an industry being reshaped by mega-mergers and acquisitions, regulation and

globalization, commoditization and competition, every day counts. Every dollar

counts. Every resource counts. Pharmaceutical companies must seize every

opportunity to compress the clinical trial cycle.

Automating the data aggregation, transormation and validation tasks associated

with analytical data preparation or clinical trials can potentially trim signicant time

and costs rom the clinical trials process.

The SAS Clinical Data Integration solution provides value or sponsors, CROs and

regulatory authorities through mature data transormation capabilities, embedded

CDISC capabilities, the ability to automate repeatable processes and the fexibility to

support the evolution o both new and custom models.

One SAS customer, a leading CRO, signicantly reduced the time required tostandardize data to SDTM. What used to take a week can now be nished in a

morning, in just 6 percent o the t ime ormerly required. The organization estimates

that eciency gains will yield cost savings o between $2 million and $2.3 million in

three years, plus an estimated $3 million in new revenues due to having SAS Clinical

Data Integration as a competitive dierentiator in the CRO market.




9

Time and cost savings – as compelling as they are – are only part o the story:

• Prebuiltandcustomizabledatatransformationsensuretheproperuseof

standards, especially CDISC.

• Embeddeddataqualityroutinesdelivercleaner,moretrustworthydata.

• Automatedprocessesdramaticallyreducethetimerequiredtomanageand

report on clinical data.

• Commonmetadatamakesdataprocessesrepeatable,auditableandautomatic.

• Standardizeddatafostersgreatercommunicationamongprojectteamsand

across projects – thereby improving the quality o scientic discovery.

To nd out more about SAS Clinical Data Integration and other SAS solutions or

lie sciences organizations, contact your SAS account representative or visit

www.sas.com/industry/pharma.

About SAS

SAS is the leader in business analytics sotware and services, and the largest

independent vendor in the business intelligence market. Through innovative solutions

delivered within an integrated ramework, SAS helps customers at more than 45,000

sites improve perormance and deliver value by making better decisions aster. Since

1976, SAS has been giving customers around the world THE POWER TO KNOW®

.



10


Figure 1. Design data ow and transormations in a visual interace with

specifc clinical data unctionality.

Figure 2. Detailed reports describe data standards usage patterns. These reports

can be used to determine the impact o changes to an existing standard or version

changes like migrating rom SDTM 3.1.1 to SDTM 3.1.2.

A Closer Look




11

Figure 3. Status reports about mapping and adherence to a standard are

also available either by study or or all active studies.

Figure 4. Administrators can add, remove and modiy industry data standards and

internal data standards, as well as manage clinical studies – including standards use,

deault content or new studies and controlled terminology packages.



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To contact your local SAS ofce, please visit: www.ss.cm/fcs

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