Copyright BioPharm Systems, Inc. 2009. All rights reserved

An Easier Way to Prepare Clinical

Trial Data for Reporting and

Analysis

Mike Grossman

VP, Clinical Data Warehousing & Analytics

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Agenda

• Preparing Data for Analysis

• What is Study Data Mapper?

• Component Overview

• Other Key Features

• Questions and Answers

Agenda

• Preparing Data for Analysis

• What is Study Data Mapper?

• Component Overview

• Other Key Features

• Questions and Answers

Preparing Clinical Data for Analysis: Current State

• Companies collect clinical data primarily from EDC

systems that are supplemented with other sources, such

as central labs

• Many sponsors are increasing the use of CROs to conduct

trials

• To control costs and save on resources, companies are

preparing standard SDTM+ data models immediately after

the EDC extract process to be able to re-use analysis from

other studies

• The preparation of SDTM+ is still very labor intensive

Holistic Reference Clinical IT Reference Architecture

Outcomes

Common Data

Model

Project level

Conformed Data

Value Added

Study Data

Conformed Study

Data

Operational Trial

Metrics

Inbound

Data

Sources

Master Meta Data

AES & Complaints

Outcomes

External Study

Data

LIMS/PK

Central Labs

CDMS/ EDC

CTMS

Staging

Area

AES & Complaints

Source Specific

Outcomes Data

Shared Study and

Project Meta

Data

Study Specific

Data Staging

Trials

Management

Warehouse

Area

Specialized Data

Marts for

Scientific

Exploration and

Mining

Specialized Data

Marts for

Scientific

Exploration and

Mining

Specialized Data

Marts for

Scientific

Exploration and

Mining

Patient Sub

Setting and

Safety

Warehouse

Clinops Data

Marts

Meta Data Libraries, Version Control, Compliance Change Mgt

Ad-Hoc Query Dashboards Structured Reports Analytical Tools

Strategic

Analysis

Regulatory

Reporting

Data Mining

Clinical

Developmen

t Planning

Preparing SDTM+: Current State

• Preparing SDTM+ requires a mapping specification from

EDC and Labs, etc.

• Programming the transformation from raw data structures

to SDTM+ requires skilled programming resources

• Each study’s mapping specification requires a fresh start

with no clear strategy for tracking what can be re-used

from previous specification

• Subtle differences in source structures make it very difficult

to re-use standard code from one study to the next

Preparing SDTM+: A Better Way

• Preparing SDTM+ requires a mapping specification from

EDC and Labs, etc.

• SDTM+ standard is managed and tracked to evolve over

time

• The mapping specification is used to computer generate

the transformation code

• Re-use of mappings from one study to another is computer

assisted through metadata search

• The skill set for preparing mappings is changed from

programmer to data analyst

• BioPharm has built a software product called Study Data

Mapper as an example of implementing this new approach

Agenda

• Preparing Data for Analysis

• What is Study Data Mapper?

• Component Overview

• Other Key Features

• Questions and Answers

What is Study Data Mapper?

SDMapper is:

• A standalone software application for managing data

structures used for conducting clinical research

• An application for managing transformations between data

structures

• An application for intelligently re-using standards and

transformations

• An application for specifying standards and mappings

• Co-developed between ICON Clinical Research and

BioPharm Systems

What is Study Data Mapper?

SDMapper is:

• An integrated Excel spreadsheet template for specifying

and communicating metadata structures and

transformation maps

• An Oracle database for securely storing all the information

• A code generator for producing executable programs

based on the supplied metadata and mappings

• Includes features for version control, standards

management and validation management

• Designed specifically for clinical trials and other data

Study Data Mapper: Process Focused

1

Specify

metadata

and

mappings

2

Upload and

parse for

errors

3

Store under

version

control

4

Generate

executable

code and test

5

Recommend

re-use of

mappings

(TBD)

6

Download with

recommendations

7

Metadata Reports

Agenda

• Preparing Data for Analysis

• What is Study Data Mapper?

• Component Overview

• Other Key Features

• Questions and Answers

Mapping Project

• A mapping project is an object used to manage the

metadata for a set of tables and value lists. It may also

manage the relationship between that metadata and

transformations to other structures.

• Examples of a mapping project

– The list of tables and controlled terminology for the SDTM+ data

standard

– The definition of the source data structures from EDC and Labs and

their mapping to the CDISC SDTM

– The mapping specification for pooling tables form multiple studies

where the studies are similar but not the same.

• Key attributes of mapping projects

– Name, Version, Validation State, Standard or Non-standard

Mapping Project Example

Table Set

• An object that contains metadata for a group of uniquely

named tables

• Examples

– All the tables extracted from an EDC system for a study

– All the tables in the SDTM+ data standard

• There can be multiple table sets in a single mapping

project

• When mapping is being used, a table set may be identified

as a source or a target

– Typically, there will be one or more source table sets and a single

target

Table

• A table is a metadata description of a data table, similar to

a SAS dataset or a database table

• A table belongs to a Table Set

• Examples of a table

– DM, EX, AE from CDISC SDTM

– The data extract view structures from Oracle Clinical

– Tables from the pooled data model for a therapeutic area

• Selected metadata attributes of a table

– Name, Version, Validation Status

– Table Label, Table Order, Repeating, Source Location, Crfloc,

Crfnote, Odm Structure, Odm Repeating, Odm Is Reference Data,

Odm Purpose, Odm Class

Example of a Table Set and a Table

Variable

• A variable is a metadata description of a column of a table

• A variable usually belongs to a table but can belong to a

Table Set

• Examples of a variable

– USUBJID, SEVERITY

• Selected metadata attributes of a variable

– Name, Version, Validation Status

– Vpkey, Vorder, Vlabel, Vlabellong, Vtype, Vlength, Vprecision,

Vformat, Vformatflag, Vcase, Ismandatory, Istemporary,

Vimportance, Vderivetype, Vdatadomain, Sdtmflag, , Vheader,

Vcrfloc, Vcrfnote, Vrole, Vorigin, Vsuppqualflag

Value Lists

• A value list is a named ordered list of possible values

• A value list belongs to a table set. Typically a table set

includes all the value lists for the controlled terminology

associated that are used by the tables in the same table

set

• Examples of value lists

– CDISC code list for Severity

– Oracle Clinical DVGs

– Units for labs

• Value list attributes - Name, Description, Version, values

– Value list value attributes-Start value, End value, Code, Sort order

Example of a Value List

Map Sets, Table Maps, and Value List Maps

• A map set is a group pf table mappings that specify the

transformation rules from one or more table sets to a target

table set

– Examples

• Transform from an EDC and central lab structure to the sponsor data

standard

• Transform selected tables from multiple studies to a single set of

pooled data

• A map set belongs to a mapping project

• A table map maps from one or more source tables to a

target table

• A value list map maps values between two different value

lists

Table Map Use Case

MappingProject–SDTM

Metadata

MappingProject–STUDY123

MapSet–STUDY123toSDTM

TableMap(DM)

Sub-MapGroup

Sub-Map

TableMap(VS)

Sub-MapGroup(VS1)

Sub-Map(Common+HGT)

CommonMappings–SBP.DBP.HRT

TableMap(LB)

Sub-MapGroup

Sub-MapGroup

Sub-Map

Sub-Map(Common)

Sub-Map

Source Target

DM

VS

LB

Join

DEMO

VS001

IC

VS002

LAB

LABExt

Variable Mappings

• Given a single output variable, you can map a single

variable via an equals operator or any combination of

variables through a more complex operator

• Examples

– HUB.DM.USUBJID=EDC.DEM.PATIENT

– HUB.DM.USUBJID=CATX(‘-’,HUB.DEM.STUDY,HUB.DEM.PT)

• Any number and complexity of operators may be registered

based on your installation and needs

• Automatic data type conversions

• Automatic conversion of terminology if value list maps are

defined

Example Simple Variable Map

Example Value List Map

Example Common Mapping

Mapping Project After Upload to SDMapper

Code Generator

• Mappings defined can generate code in a few seconds

• Each table map is generated separately and called in the

order that takes care of dependencies

• Current version generates portable standalone SAS code

that has no dependencies on SDMapper or LSH.

– Ideal for internal use, as well as sharing with partners and

regulatory authorities.

• Future versions will generate SAS programs or PL/SSQL

programs directly to an LSH environment

Example Code Generation

Example Generated Code Snippit

Agenda

• Preparing Data for Analysis

• What is Study Data Mapper?

• Component Overview

• Other Key Features

• Questions and Answers

Other Key Features

• Recommendations engine: This is a future feature that will

take your existing source and target structure and look

through already existing mappings and recommend

starting with similar mappings if they already exist. This will

maximize re-use of existing mappings.

• Tagging: This is the ability to assign attributes to any

object. The current version lets you tag the sponsor and

the study for a mapping project. Future versions will allow

for any tags, such as compound, therapeutic area, concept

etc.

Agenda

• Preparing Data for Analysis

• What is Study Data Mapper?

• Component Overview

• Other Key Features

• Questions and Answers

Contact Information

If you have additional questions, please contact:

United States: +1 877 654 0033

United Kingdom: +44 (0) 1865 910200

Email Address: info@biopharm.com

Website: www.biopharm.com