40261372 sas clinical research nelson

8/13/2019 40261372 Sas Clinical Research Nelson

1/62


2/62

2

Outline

Introduction and Overview SAS 30 years of evolution

The SAS 9 Platform

Clinical Research Imperatives

Summary and Conclusions


3/62

3

Overview

Examples

Choosing the right environment

SAS 9

Summary

Using SAS for Clinical Research


4/62

4

Overview

Major computing trends in Pharma

The clinical research informationvalue chain

Standards that matter when it comesto DI (CDISC, ICH, HL7)

Pieces and parts of SAS

The roles for SAS (and SASprofessionals) in pharma


5/62

5

BI and DI in Pharma

a 13 to 15 percent annual increase inbusiness intelligence and datawarehousing spending


6/62

6

Value of the platform

With enhanced decision-making capabilities, and the ability to

respond to health authority requests and inquiries in a timeliermanner, the concerted organizational effort leading up to NDAreview/approval is being accelerated by up to five days leadingto earlier revenue recognition. Revenue is being estimated at$1,000,000 per day, each day the review or approval isaccelerated.

With enhanced decision-making capabilities along with the abilityto utilize all drug discovery and development data in a consistent,accurate and trusted manner, there is potential to kill atleast one more drug per year earlier in the cycle; and/orprevent one from entering a clinical program. Note: 9,999 out of10,000 drug candidates fail to make it to the market; and for the

one that makes it, the research and development costs is almost$750 million. The cost avoidance is estimated to be in themillions.

Spending Trends Demonstrate Value of Business Intelligence and Data Warehousing

by Tim Furey

Published: January 24, 2006


7/62

7

BI Defined

Business intelligence (BI) is a broad category ofapplication programs and technologies forgathering, storing, analyzing, and providingaccess to data to help enterprise users makebetter business decisions.

BI applications include the activities of decisionsupport, query and reporting, online analyticalprocessing (OLAP), statistical analysis,forecasting, and data mining.

a set of concepts and methods to improvebusiness decision making by using fact-basedsupport systems


8/62

8

DI Defined

Traditionally what we think of as data

management Involves combining data from lots of

different places, formats and structures

Used to create a single, credible version ofthe truth

Data Integration is about beating data into

submission getting the data right beforewe can use it.


9/62

9

Corporate Information Factory


10/62

10

Corporate Information Factory


11/62

How Drugs Are Used

Prevention Cure

Treatment

Diagnosis

Promotion


12/62

12

Understanding the Drug Development Life Cycle


13/62

13


14/62

14

Discussions

Decision

Support

EDC/ RDETrials Management

Workflow

Data Mining

Un-Structured

Content

CDW

Document Management

Structured

Data


15/62

Importance of Enabling Technologies

Pharmaceutical companies have made a tremendousinvestment...

Only 1 in 10,000 of the compounds synthesized ever reachmarket

An NDA is approved for 20% of the drugs which had an IND filed

By the time a drug is approved, generally only 8 years remain onthe patent

Only 25% of all drugs that are marketed achieve profits thatexceed the development costs

Hence, the importance of:

efficient processes meeting or exceeding timelines


16/62

16

Protocol Design and Study Start-Up

Patient and Investigator Recruitment

Clinical Trial Management

Clinical Data Management

Data Analysis

Clinical Supplies

Regulatory and Safety

Core Functions Targets for Improvement


17/62

17

Technology Map for Clinical Trials


18/62

18

Technology Challenges

Validation

Manageability

Scalability (Performance)

Security Data (and process) governance

Auditability


19/62

19

Compliance

compliance with 21 CFR part 11: security, audittrail, version control Validation - accuracy, reliability, consistent intended

performance and the means to discern invalid or alteredrecords

Restriction of system access to only authorized individuals

Secure, computer-generated, time-stamped audit trails

to record operator entries and action to create, modify anddelete electronic records retained for required period andavailable for agency review and copying

Operational system checks to enforce permitted sequencingof steps and events as appropriate

Authority checks for use, e-signature, access of input and outputdevice, altering a record and performing operation at hand

PDUFA III -Reviewable Units CDISC


20/62

20

Related Standards

ICH

The International Conference on Harmonization has been compiling a

series of guidelines for the preparation, design, conduct, and reportingof clinical trials with an aim to harmonize the interpretation andapplication of technical guidelines and requirements for productregistration.

CDISC

Operational Data Model (ODM) - operational support of data collection Study Data Tabulation Model (SDTM) data tabulation data sets

Case Report Tabulation Data Definition Specification (CRTDDS - akadefine.xml)

Laboratory Data Model (Lab)

Standard for Exchange of Non-clinical Data (SEND)

BRIDG - Protocol Representation

Analysis Data Model (ADaM) analysis data structures

And others.. LAB, SEND


21/62

21

Scope of this paper

H o w ca n SA S 9

b e u se d i n

C l i n i c a lR e s e a r c h ?


22/62

22

The Fundamentals

Clinical Research Objectives

To provide clinical information that is useful in the making of business andeconomic decisions.

To provide understandable information which will aid stakeholders inpredicting the safety and efficacy of a compound.

Qualitative Characteristics of Clinical Reporting

Relevance Timeliness Predictive Value Feedback Value

Reliability Verifiability Neutrality Representational faithfulness

Comparability (including Consistency)


23/62

Project

Data

Study Definition

Trial Management

Data Collection

Patient Enrollment

Study Preparation

Query

AnalysisReporting

Questions

Study

Data

QueryProject

Management

Information Flow

Communicate

Regulatory Submission

Data Analysis


24/62

24

Challenges in Repeatability

Define additional data pointsas the study's hypothesis isrefined

Clinical QA may revise coretrial processes such as

adverse event adjudication Trial managers sometimes

devise new metrics that betterreveal trial progress

FDA often requests additional

analyses that require newderived variables

Significant change occursbetween trials as well, asstudy teams invent new case

report form (CRF) elementsand technological advancesintroduce new ways tomeasure product safety andefficacy

Study Defini tion

Trial Management

Data Collection

Patient Enrollment

Study Preparation

Regulatory Submission

Data Analysis

New CRFElements

New BusinessProcesses

NewManagement

Tactic

ReviewerQuestions

New analysistechniques

Re-design


25/62

25

Pervasive Impact Change Has


26/62

26

Pharma imperatives

Compliance/Validation

Change control

Auditing

Security


27/62

27

Overview

Examples


SAS 9

Summary

Using BI for Clinical Research


28/62

28

SAS 9 Platform

Overview of the Platform (IVC)

Tools that make up the platform

What specifically is BI (in SAS 9)

Related Aspects (DI and Analytics)


29/62

29

SAS Information Value Chain

What is the SAS Information Value

Chain? It is a marketing message from SAS


30/62

30

SAS 9 Components

SASDIStudio

SASDataSurveyors

SASOLAP

Administrator

SASDataQualityServer

SASBASE

(asatransformatione

ngine)

SASAccessProducts

SASOLAP

Server(SASApplicationServer)

SASScala

blePerformanceDataServer

SPDE

SASAppD

evStudio

SASInformationDeliveryPortal

SASOfficeIntegration

SASWebReportStudio

SASEnter

priseGuide

SASInformationMapStudio

SAS Management Console is part of the Manageability cornerstone

SAS Workspace Server and SAS Stored Process Server are used by all the Clients


31/62

31

Two ways to go with SAS 9.1

Traditional SAS using DM or, preferably,EG

Evolutionary. Works just like the old SAS, butwith some enhanced functionality (i.e. newprocedures, functions, SPDE, etc.).

The new client/server paradigm(metadata-managed platform)

Revolutionary! If you really want to takeadvantage of the new power of SAS, you willhave to make the leap.


32/62

32

SAS Perspective


33/62

33

Supporting Clinical Research

Keys that make this more challenging in

Clinical: Regulatory

Reuse (repeatability)

Flexibility Security and Auditability

XML

Movement away from datasets to self-describing data constructs

Can we use these as information


34/62

34

Overview

Examples


SAS 9

Summary



35/62

35

Examples: Non-clinical

Non-Clinical Uses

Sales and MarketingManufacturingFinance

Human Resources Information ServicesExecutive and Portfolio management


36/62

36

Examples: Clinical

Clinical Research

Pre-clinical ResearchClinicalStat/Programming

Supporting other groups Data Management (patient profiles)

Medical writing

Finance

Project management

Patient Registries & Post marketingsurveillance


37/62

37

Sales / Marketing

Single version of the truth for customer

Data mining supports the business

Business analysts

Case managers

Account managers

SAS data warehouse

Internal Sales planning data

IMS Sales data

Score defined segments on the fly

Eliminates need to score entire database

Use only the freshest up-to-date data

Reduces manual intervention and error

Accelerates the market cycle

Increases likelihood of reaching andinfluencing customers and prospectswith the offer at the right time

Improves campaign results and lowerscosts

Positively influence the behavior of

customers and prospects through a streamof pertinent communications


38/62

38

Patient Registry

Worlds largest patient registry

Access to research analytics

Publication quality data available

Clinicians

Sales staff

Clinical data management system

Af forded sales staf f access to doctors

Provided much needed clin ical researchfindings

Remove need for dist ribution of data/reports for over 1M patients at 1500hospitals

h


39/62

39

Contract Research Organization

Modern SAS infrastructure

Repository for all clinical informationAssets

Developer and end user tool

SAS Programmers & Statisticians

Medical Writers

Data Management

Existing Oracle-based data managementsystem

Import various unstructured data

Significantly reduce time/manpower tofind data, prove that process wasfollowed

Provide infrastructure for global teamcollaboration

Leverage a validated environment

O i ti l Pl i


40/62

40

Organizational Planning

Measurement based leadership

Reposi tory for all measurement data

Decision support tool

Executives and managers in Fortune 1000companies

Business consultants

Survey data about customer, employeesatisfaction

Account ing data (financial metrics)

Provide a single platform for runningyour business

Combined historical data with predictivemodeling

Leverage a validated environment

U i BI f Cli i l R h


41/62

41

Overview

Examples


SAS 9

Summary


M lti l Ti E i t


42/62

42

Multiple Tier Environment

The SAS Intelligence

Platform consists of amultiple tier environmentthat is typically represented

by the client tier

middle tier

server tier

ClientClient

TierTier

MiddleMiddleTierTier

ServerServer

TierTier

SAS Intelligence Platform


43/62

43

SAS Intelligence Platform

Client

Tier

Middle

Tier

Server

Tier

SAS DI Studio

SAS OLAP Cube Studio

SAS Management Console

SAS Information Map Studio

Web Infrastructure KitWeb Infrastructure Kit

Java Servlet

Container

Java Servlet

ContainerwebDAV

Server

webDAV

Server

HTTP ServerHTTP Server SDKSDK

SAS Enterprise Guide

SAS Add-In for Microsoft Office

SAS Web Report Studio

SAS Information Delivery Portal

Workspace

Server

Workspace

ServerStored Process

Server

Stored Process

ServerOLAP

Server

OLAP

Server

Metadata

Server

Metadata

ServerSAS9

Foundation

SAS9

FoundationSAS/CONNECT

Server

SAS/CONNECT

Server

SAS 9 Clients


44/62

44

Server Tier

SAS DI Studio

SAS EnterpriseGuide 3.0

SAS ManagementConsole

SAS OLAPCube Studio

SAS EnterpriseMiner 5

SAS Information

Map Studio

SAS XML Mapper

SAS Information

Delivery Portal 2.0

SAS WebReport Studio

Centralized Metadata RepositoryCentralized Metadata Repository

Shared Metadata Shared Data

Business Metadata

SAS 9 Clients

But clients alone are just not enough!!


45/62

45

But clients alone are just not enough!!

They need to connect to something

They need to have a lookuplocation to know where to getinformation when the user selectsaccess to something.. Transparently!

So what do they connect to?

All clients connect to a SASApplication Server!

Server Tier


46/62

46

Server Tier

The server tier is the machine where one or moreSAS servers is installed and accessed by the BI tools.

There are different types of SAS servers, including:

MetadataMetadata

ServerServerenables centralized metadata delivery andenables centralized metadata delivery and

management to SAS applications across themanagement to SAS applications across the

enterprise.enterprise.

WorkspaceWorkspace

ServerServerexecutes SAS code on behalf of the clientexecutes SAS code on behalf of the client

applications.applications.

...

Stored ProcessStored Process

ServerServer

executes and delivers results from SAS Storedexecutes and delivers results from SAS Stored

Processes.Processes.

OLAP ServerOLAP Server delivers predelivers pre--summarizedsummarized cubescubes of data toof data to

OLAP clients.OLAP clients.

SAS Drug Development


47/62

47

Ad Hoc

Querying

Ad HocQuerying

Portal

ReportingReporting

Desktop

Source

Systems

Web Server

Data MiningData Mining

OLAP/CubesOLAP/Cubes

SDD Platform

Metadata Management

Version

Control

Analysis

andReporting

Regulatory Submission and

Document Management

Biomedical

Trial DataWarehouse W

eb

Hosting

21 CFR Part 11 Compliance

Data Quality, Cleansing,

Validation, Aggregation,Enrichment

ETLQ

(

DefinedBusinessRules)

ETLQ

(DefinedBusinessR

ules)

Access Engines

Meta

Data

Clintrial Connector

Opentext

Electronic

Submissions

Clintrial

eTrial

eClinical

Other

Oracle

CROs

OpenText


Common Data Integration Tasks


48/62

48

Common Data Integration Tasks

Extraction Data importing

Raw files (e.g., labs)

SAS Transport Oracle CDISC (e.g., ODM)

Transformation Filtering Data cleansing

Categorization & Enrichment Dictionary coding

Loading (derived and analysis datasets) Summarization (for data management) Exporting Data

SAS Transport ODM SDTM

Common BI Tasks


49/62

49

Common BI Tasks

Clear separation of data management andreporting (e.g., Reporting is one PROC

away) Derived (or analysis) datasets prepared

for us by upstream processes (e.g. DIStudio)

Delivery or consumption of information: Medical writers Data management

Biostatisticians Medical professionals

Which way to go with SAS


50/62

50

Which way to go with SAS

SAS Drug

Development


51/62

Clinical Architecture


52/62

52

External

Dictionaries

Multi-study

Relational

Repository

Relational

Janus

Repository

EDC System(s)

Other R&D Systems

Encoding

Dictionary

Repository

Metadata

Library

Dictionary

Repository


SDD Data

Explorer

SDD Process

Editor

SDD Processes

ADaM

Repository

System

Repository

SAS DIQuality and

Encoding

Data Marts and SASOLAP cubes

SAS9 Metadata and Foundation

Custom import

WebDAV

ETL

May be

the same API

Enterprise

Guide

BaseSAS

SASBIPortal&

WRS

Other

Enterprise

SystemsSAS DI

ETL & CDISC Transformations

Metadata Management

API

Optional

ETLSAS Grid

Manager

* Image provided by SAS



53/62

53

Overview

Examples


SAS 9

Summary

g

What to use?


54/62

54

Who the users are?

Stat/programmersMedical writers

Data management

Biostatisticians

other departments

What to use?


55/62

55

What is the purpose?

Information disseminationFDA submissions

Client review of data

Production versus ad-hoc

What to use?


56/62

56

Production state?

Production versus ad-hoc

What to use?


57/62

57

Existing or New Applications?

Web Publishing Level of Interactivity

What to use?


58/62

58

What kind of processing?

SQL QueriesSAS programs

Macros

What to use?


59/62

59

Available Skill Sets?

HTMLSQL

SAS programming

Java

Excel

Web browser

Information Based Medicine will require unprecedented

access to diverse, integrated information


60/62

60

Access to Diverse

HeterogeneousDistributed Data

Expression Arrays

(various tissues)

Personalgenomics

X-rays, MRI,

mamograms,etc

Clinical Record

Analysislab notes

Hospital events ....admission,surgery, recovery, discharge

1. PatientInformation

Challenges

Volume and complexity of data

Integrating massive volumes of

disparate data

Need for sophisticated analytics

Growing collaboration across ecosystem

Summary


61/62

61

Understanding the value of the platformapproach

Metadata Appropriate access to data Lineage & Impact analysis (for DI) Versioning (for some products) Speed time to market

Metadata use and reuse Multi-developer capabilities Template reuse (add-in for EG, stored processes)

Validation support (e.g., independent programming) Quick starts for unfamiliar tasks


62/62

62

ThotWave TechnologiesThotWave Technologies

Thinking DataThinking Data

How to reach us...

Greg NelsonCEO and Founder

[email protected]

40261372 sas clinical research nelson

Documents