Tools, Technologies and Techniques in Clinical Data Standardization

Pankaj Bhardwaj (Lead - Statistical Programmer)

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Agenda

•Why Data Standardization?•Clinical Data Standardization Tools and Approaches•Building own Clinical Data Standardization Framework- Techniques•Building own Clinical Data Standardization Framework- Technologies•Metadata Set Up•SAS – Java Connectivity•Clinical Data Standardization Tool – Ready•CDM Practices for standardizing the Future Trials•Q & A

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Why Data Standardization?

Data Standardization

Productivity

Cost Efficient

Accuracy

Streamlines Study set up

Data Reusability

Fewer redundant datasets and flows

Efficient Clinical Database

Efficient exchangeof data withsuppliers, partnersand customers

High Data Quality

Quality Regulatory Response

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Clinical Data Standardization Tools and Approaches

Base SAS and Macros

SAS Drug Development

SAS Integration Studio and Web Report Studio

SAS Clinical DATA integration Tool

Oracle LSH

Data Transformation in Informatica and Reporting in BO

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1. Metadata Set Up (Clinical Data Standard Specific Metadata, Control Terminology)considering appropriate technologies and Operating Systems.

2. Customization of Target Data Standards provision.

3. Provision for Pre and Post processing of Data into the staging area and reusingmapping definitions across trials.

4. Dictionary Coding.

5. Automatic Generation of Data transformation programs.

6. Data Integrity Checks.(Dataset Checks, Cross Datasets Checks, Metadata ComplianceChecks).

7. Creation of Analysis Datasets, Patient Profiles, Define.xml and Reporting onStandardized Data using generalized Algorithms across trials.

8. After having all the above functionalities, the GUI can be built on top of it.

Building own Clinical Data Standardization Framework- Techniques

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Target Standard Metadata (CDISC / Sponsor compliant)

CODELIST METADATA

MedDRA WHO Drug

DICTIONARY METADATA

Metadata Set Up

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Metadata Integration with Source Data

Integrator Code for:1. Metadata and

Source Data Integration

2. Target Datasets Generation

Target Standardized

Data:DMAE

Source Data(Oracle Data, SAS Datasets,

Transport Files and others)

Tool Metadata

INTEGRATION

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Unix or Windows Environment

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Building own Clinical Data Standardization Framework- Technologies

SAS A/F

. Net and SAS

JAVA and SAS

Note : The Analysis Tool can be Non-SAS as well

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SAS – Java Connectivity

SAS

JAVA

Updating SAS Configuration File with the Class Path Variable and Using the Objects Through SAS Programs

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JDBC Connectivity

Closing Database Connection

Getting Results

Executing Statements

Establishing Connection

Loading Drivers Class.forName(driver).newInstance()

con = DriverManager.getConnection()

ResultSet res = st.executeQuery()

System.out.println( employeeName )

con.close()

SAS Connect

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Clinical Data Standardization Tool - Ready

Source DataVariable Label Length Type

Subj Subject 30 Char

Race Race 35 Char

Ht Height 8 Num

Country Country Name

50 Char

Smk Smoke 2 Char

Wt Weight 8 Num

Target DataVariable Label Length Type Codelist

Country Country 50 Char Cnt

Race Race 20 Char Rc

Height Height 8 Num

Weight Weight 8 Num

Smoke Smoke 1 Char Y/N

Subject Subject 20 Char

MAP

Select Study Select Target Standard Import Source Data Process Data

Generate Target Data Validation Reporting Export Target Data

Patient Profiles Define.xml Analysis Datasets

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CDM Practices for standardizing the Future Trials

Revisiting non - Standardized Clinical Data Management (CDM) approach

Standard and Therapeutic area oriented CRFs

Clinical Data Acquisition Standards Harmonization” (CDASH) guidelines

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Q & A

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Thank-You