developing an end to end standard & automated risk based ... · 10 steps end-to-end s&a...

Developing an end to end Standard & Automated Risk Based Monitoring

Process.

AR01

Giuseppe Di Monaco,

UCB BioSciences GmbH, Monheim, Germany

Tim Williams,

UCB BioSciences, Raleigh, USA

2

ן Risk Based Monitoring (RBM) • Ensure quality of clinical trials by

- Identifying, assessing, monitoring, mitigating risks which could affect quality or safety of a study.

INTRODUCTION2

ן Key Risk Indicator (KRI)• A metric used to assess risk associated with an activity

ן Three major areas of Data Surveillance at UCB• Data Quality & Validity• Patient Safety• Site Compliance and Performance

ן Central Data Surveillance Oversight activities• Identify issues & risks• Relevant signals are monitored

ן RBM Programming Team• Creates KRI analysis datasets from SDTM/ADAM datasets

BACKGROUNDן Design Problems

• 3 areas of data surveillance are broad and complex• Lifespan of the RBM Programs

• Total Number of RBM Programmers

• Lifecycle of KRI programs

• Monolithic programs focused on KRIs Functional requirements (FR)

- FR describes what the program generates

- NFR describes how the program is built

ן Impacts• Maintainability

- Is it easy to understand the program code?• Usability

- Is it easy to use?

• Scalability - Can it handle a growing amount of work?

• Communicability - Is it easy to share information?

3

ASPECTS COVERED IN THIS PRESENTATION4

4

Usability&

Maintainability

Improve QualityImpact in ROI

Increases AccuracySimplicity

Round The Clock Operations

Automated Notification

Compliance (Audit Trails)

Externalization

Operation Efficiency

Modularization

Cost Effective

Quick To Deploy

Full Automation

Increases Productivity

Enable Collaboration

Adherence to Company Standards

Comprehensive &

Intuitive

1 2

3

4

5

67

8

9

10

4

End to End S&A RBM

Process

OBJECTIVES & STRATEGY – Zoom-Out5

ן End to End S&A

• RBM Data Surveillance process and adjacent areas re-engineered/improved/automated

STEP 1: AutoconfigurationSTEP 2: Check dataSTEP 3: Copy data STEP 4: Email notificationSTEP 5: Close previous runs

STEP 6: Adapt to standardsSTEP 7: Load study settingsSTEP 8: Run, compare, validate

STEP 9: Archive STEP 10: Quality, sanity checks

1 2 3 4 5 6 7 8 9 10

Input DataSDTM/ADAM

KRI Analysis Datasets

Programs

End to End Standard & Automated Risk Based

ZOOM-OUT

ZOOM-IN

10 major steps. We focus on step number 8

Full Automation

Automation of Notifications SMTP (Simple Mail Transfer Protocol)

1 2 3 4 5 6 7 9 10START

OBJECTIVES & STRATEGY – Zoom-Out

Study A

SDTM/ADAM Unzip by IT

DAMsAdvance Analytic

Programmers CSRMs

KRI Study X

KRI Study BStudy B

Study X

KRI Study A


10 STEPS End-to-End S&A Full Automation



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ן Types of modules:

• Process support (utilities), functional modules (KRI analysis creation)

ן Modular Programming

• MODULES: collection of data & function abstractions

• ZOOM-IN in to the NFR (Non-Functional Requirements)

ן Benefits

• Maintainability: minimal effort to repair• Usability: Debugging automation

• Scalability: Able to handle a growing amount of work

• Communicability: Automated email notifications

• Traceability: Same filenames for è .sas, .txt, .log, .sas7bdat, .xls

ן Example• ut_tstcd_mahap_dat_u.sas è Utility• v_dva_mahap_dat_u.sas è KRI “Low Variability”

OBJECTIVES & STRATEGY – Zoom-In Usability &Maintainability

7

Simplicity

Modularization

Externalization

Usability &Maintainability

KR

I M

acro

sU

tility

Mac

ros

Run all New Macros

Iut_kricreatval.sas

10987654321

New Process

DESIGN & IMPLEMENTATIONSTAR SCHEMA

Run all Old Programs

Old Process

III

Generate KRI output analysis datasets

“Low Variability” KRIv_dva_mahap_dat_u.sas

IV

II

ut_tstcd_mahap_dat_u.sas(Select study specific –TESTCD)

Generate values for macro variables and temporary datasets

Modularization ExternalizationFull Automation

8

10987654321

9

Modularization Externalization

Utility Macros

ut_kricreatval.sas

“Low Variability” KRIv_dva_mahap_dat_u.sas

IIIII

IV

ut_tstcd_mahap_dat_u.sas(study specific –TESTCD)

Study B –TESTCD = DIABP, SYSBP, PULSE, TEMP, WEIGHT

Study A–TESTCD = DIABP, SYSBP, PULSE

Input dataset Study A VS

Input dataset Study B VS Output dataset Study A VS

V

Output dataset Study B VS

Cal

l Mac

ro

Functional Macros

DESIGN & IMPLEMENTATION - STAR SCHEMA

DESIGN & IMPLEMENTATION PROCESS DEMO

10ModularizationSimplicity Externalization


Usability &MaintainabilityFull Automation

Conversion to Knowledge Graph

CODE MANAGEMENT WITH A KNOWLEDGE GRAPH

xxxxxx.SASParse

Convert

Map

Query,Visualize

Ontology

RDF Knowledge

Graph

11

CODE MANAGEMENT WITH A KNOWLEDGE GRAPH

Modularization

Simplicity

Externalization



Full Automation

12

CODE MANAGEMENT WITH A KNOWLEDGE GRAPHEnforcing rules

Macro calls in program ut_kricreatval.sas

ExternalizationUsability

&Maintainability

SimplicityModularization

13

CODE MANAGEMENT WITH A KNOWLEDGE GRAPHImpact Analysis Demo ModularizationSimplicity Externalization



Full Automation

14

CONCLUSIONSן SUMMARY

• Design Problems

ØData Surveillance areas are broad and complex

ØKRI Monolithic programs are difficult and expensive to maintain

ØHigh lifespan and Large scale system

ØHigh Number of RBM Programmers

• Solution

ØS&A Process covering RBM Data Surveillance process and other areas

• Strategy

ØZOOM-OUT è 10 Steps process

ØZOOM-IN è Macros Functionalities

ן CONSIDERATIONSØS&A is an Organizational attitude è Need for support of managers and colleaguesØCaution è Do not fall into the "prototype trap"

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CONCLUSION - CONTINUED16

16

Usability&

Maintainability

Improve QualityImpact in ROI

Increases AccuracySimplicity

Round The Clock Operations


Compliance (Audit Trails)

Externalization

Operation Efficiency

Modularization

Cost Effective

Quick To Deploy

Full Automation

Increases Productivity

Enable Collaboration

Adherence to Company Standards

Comprehensive &

Intuitive

1 2

3

4

5

67

8

9

10

16

End to End S&A RBM

Process

The authors would like to thank

Brigitte Bernhoff

Without her contribution all this would have not been possible.

ACKNOWLEDGMENTS17

Questions?18

Thanks!

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