SAS Life Science Analytics Framework
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1 CDISC Italian User Network Day 21Oct2016 (Data standard e loro applicazione) SAS Life Science Analytics Framework STIJN ROGIERS - SAS SENIOR INDUSTRY CONSULTANT GLOBAL PRACTICE, HEALTH & LIFE SCIENCES
2 AGENDA 1. Introduction to SAS Life Science Analytics Framework (LSAF) 2. CDISC Standards & Study Metadata Demo - Generation of Empty Datasets & Define.XML 3. End-2-End flow SAS LSAF + Extensions through LSAF API SDTM transformation Cross-functional Review (structural & content checks, visualizations) 4. Use Cases
3 INTRODUCTION LIFE SCIENCE ANALYTICS FRAMEWORK
4 SAS LIFE SCIENCE ANALYTICS FRAMEWORK KEY FACTS (1/3) Managing, analyzing, reporting, and reviewing (clinical) research information in the same analytical environment Repository provides seamless integration with the SAS program development (authoring environment) and execution environment, along with workflow capabilities -- NEW Authoring Environment compared to SAS DRUG DEVELOPMENT (SDD) -- NEW Business Process Model and Notation (BPMN) 2.0 standard support compared to SAS DRUG DEVELOPMENT (SDD) Cloud based Provides more flexibility, lower costs, greater scalability, ease of use and, if done correctly, increased security and disaster recovery IT seen as an Innovator supporting the rest of the company to achieve its goals
5 SAS LIFE SCIENCE ANALYTICS FRAMEWORK PROGRAM AUTHORING & EXECUTION
6 SAS LIFE SCIENCE ANALYTICS FRAMEWORK WORKFLOW (SUPPORT BPMN STANDARD)
7 SAS LIFE SCIENCE ANALYTICS FRAMEWORK KEY FACTS (2/3) Collaboration & Accessibility internally and externally (geographically remote) direct access to research content, clearly organized and searchable Configurable User Interface Highly flexible (configurable) permission model privileges & permissions High-performance (Remote) Application Programming Interface (extend & automate) Java API and SAS Macro API, moving to RESTful API Complete audit history and robust version control system managing change history of all files Job concept and Manifest file Job Concept reproducibility and usability (for non-technical savvy users) Manifest File transparency and traceability
8 REPOSITORY AND CREATION OF JOB EXAMPLE Create Job from existing SAS files + Run and Populate
9 REPOSITORY AND CREATION OF JOB EXAMPLE Create Job from existing SAS files + Run and Populate Run and Populate scans all SAS programs to produce Input & Outputs automatically (example of Inputs/Outputs on next slide).
10 JOB MANIFEST FILE EXAMPLE
11 SAS LIFE SCIENCE ANALYTICS FRAMEWORK KEY FACTS (3/3) Standards metadata management (driving automation) -- NEW compared to SAS DRUG DEVELOPMENT (SDD) Standard (Global) Level (SDTM & SEND) Data Standards Module Study Level Study Specification: Empty Datasets + Define XML 2.0 creation Studies Module Support Visual Analytics (integration) -- NEW compared to SAS DRUG DEVELOPMENT (SDD) Expanding Suite of Extensions (optional modules build on robust LSAF API) E.g. Templates extension to automate creation of folder structure within repository incl. administration/security. Note: See also additional extensions slides related to end-2-end flow.
12 CLINICAL STUDIES VISUALIZATION EXAMPLE Aggregate Panel Review 1. Direct link to Patient Profile 2. Drop down list showing # of subjects in a site 3. Drop down list showing # of subjects who died in study 2 3 1
13 CLINICAL STUDIES VISUALIZATION EXAMPLE 1. DM domain view 2. SV domain view 3. SE domain view 4. DS domain view 5. CM domain view 6. AE domain view 7. MH domain view 8. LB domain view 9. VS domain view 10. Visits view 11. Link back to Demographic view Patient Profile
14 CDISC STANDARDS AND STUDY METADATA
15 GLOBAL STANDARDS eshare Standards STANDARDS MANAGEMENT STUDY METADATA DATA PROCESSING REVIEW ANALYSIS Protocol 1 Global Standards Study Metadata 2 IMPORT eshare CDISC STANDARDS CREATE CUSTOMER STANDARDS DEFINE STUDY LEVEL METADATA Specifications CREATE SUBMISSION READY SDTM DEFINE.XML SHARE WITH INTERNAL & EXTERNAL STAKEHOLDERS Specifications Analysis Activities PROGRAMS-JOBS DEVELOPMENT * CREATION, QC & USAGE (INCL. VERSION CONTROL) * SIGN-OFF * AUTOMATION VIA WORKFLOW 6 EDC External / Other Data Sensors/ Wearables Internal system(s) / customers EDC DATA EXTRACTION (Life Science Analytics Framework Extension/App) Other DATA EXTRACTION (Life Science Analytics Framework Extension/App) Event Streaming (SAS Event Stream Process Engine) 4 SDTM Transformation (Life Science Analytics Framework Extension/App) Extraction & Transformation Deliverables (Internal sharing of SDTM data after Structure automated verification) Automated + Manual Review Activities (Life Science Analytics Framework Programs/Jobs + SAS Visual Analytics + Pinnacle 21 (~Open CDISC),...) Cross-Functional Review Tracking (Life Science Analytics Framework Extension/App) Cross-Functional Review 5 Outbound data ( Life Science Analytics Framework Extension/App ) 8 Final Deliverables Review Input RUNNING R CODE THROUGH PROC IML PROGRAMMING TRACKING & PROGRESS ( Life Science Analytics Framework Extension/App ) VISUALIZATIONS (SAS Visual Analytics,...) ADAPTIVE DESIGN AUTOMATION ( Life Science Analytics Framework Extension/App ) 7
16 STUDY METADATA eshare Standards STANDARDS MANAGEMENT STUDY METADATA DATA PROCESSING REVIEW ANALYSIS Protocol 1 Global Standards Study Metadata 2 IMPORT eshare CDISC STANDARDS CREATE CUSTOMER STANDARDS DEFINE STUDY LEVEL METADATA Specifications CREATE SUBMISSION READY SDTM DEFINE.XML SHARE WITH INTERNAL & EXTERNAL STAKEHOLDERS Specifications Analysis Activities PROGRAMS-JOBS DEVELOPMENT * CREATION, QC & USAGE (INCL. VERSION CONTROL) * SIGN-OFF * AUTOMATION VIA WORKFLOW 6 EDC External / Other Data Sensors/ Wearables Internal system(s) / customers EDC DATA EXTRACTION (Life Science Analytics Framework Extension/App) Other DATA EXTRACTION (Life Science Analytics Framework Extension/App) Event Streaming (SAS Event Stream Process Engine) 4 SDTM Transformation (Life Science Analytics Framework Extension/App) Extraction & Transformation Deliverables (Internal sharing of SDTM data after Structure automated verification) Automated + Manual Review Activities (Life Science Analytics Framework Programs/Jobs + SAS Visual Analytics + Pinnacle 21 (~Open CDISC),...) Cross-Functional Review Tracking (Life Science Analytics Framework Extension/App) Cross-Functional Review 5 Outbound data ( Life Science Analytics Framework Extension/App ) 8 Final Deliverables Review Input RUNNING R CODE THROUGH PROC IML PROGRAMMING TRACKING & PROGRESS ( Life Science Analytics Framework Extension/App ) VISUALIZATIONS (SAS Visual Analytics,...) ADAPTIVE DESIGN AUTOMATION ( Life Science Analytics Framework Extension/App ) 7
17 SAS LIFE SCIENCE ANALYTICS FRAMEWORK STANDARDS MANAGEMENT
18 SAS LIFE SCIENCE ANALYTICS FRAMEWORK STUDY METADATA MANAGEMENT
19
20 END-2-END FLOW (LSAF + EXTENSIONS)
21 END-TO-END FLOW
22 SDTM TRANSFORMATION STANDARDS MANAGEMENT STUDY METADATA DATA PROCESSING REVIEW ANALYSIS eshare Standards Protocol 1 Global Standards Study Metadata 2 IMPORT eshare CDISC STANDARDS CREATE CUSTOMER STANDARDS DEFINE STUDY LEVEL METADATA CREATE SUBMISSION READY SDTM DEFINE.XML SHARE WITH INTERNAL & EXTERNAL STAKEHOLDERS Specifications Analysis Activities ANALYSIS PROGRAMS- JOBS 6 Specifications * CREATION, QC & USAGE (INCL. VERSION CONTROL) * SIGN-OFF EDC External / Other Data Sensors/ Wearables EDC DATA EXTRACTION (Life Science Analytics Framework Extension/App) Other DATA EXTRACTION (Life Science Analytics Framework Extension/App) Event Streaming (SAS Event Stream Process Engine) 4 SDTM Transformation (Life Science Analytics Framework Extension/App) Deliverables (Internal sharing of SDTM data after Structure automated verification) Automated + Manual Review Activities 5 (Life Science Analytics Framework Programs/Jobs + SAS Visual Analytics + Pinnacle 21 (~Open CDISC),...) Cross-Functional Review Tracking (Life Science Analytics Framework Extension/App) Final Deliverables Review Input * AUTOMATION THROUGH WORKFLOW * RUNNING R CODE THROUGH PROC IML VISUALIZATIONS (SAS Visual Analytics,...) ADAPTIVE DESIGN AUTOMATION (Life Science Analytics Framework Extension/App) 7 Internal system / Partner Extraction & Transformation Cross-Functional Review Outbound data 8
23 END-2-END FLOW: SDTM TRANSFORMATION (LSAF EXTENSION) Supported by SAS Solutions On Demand Current Flow Overview (all details can be provided during a demo) Define Study Identify CDISC Mapping Standard Identify Data Sources Understand / browse through raw data Table to Domain mapping Variable Mapping (Auto-mapping and manual + Progress) Publish Mappings Transformation Program Templates Publish manual mapping to Auto-mapping definitions for future (~ machine learning). Over time this will reduce manual mapping efforts significantly. Generate Program Templates with Pre-Code, Mapping information and Post-Code
24 END-2-END FLOW: SDTM TRANSFORMATION (LSAF EXTENSION) Define Study Identify CDISC Mapping Standard Identify Data Sources Understand / browse through raw data Table to Domain mapping Variable Mapping (Auto-mapping and manual + Progress) Publish Mappings Transformation Program Templates Generate Program Templates with Pre-Code, Mapping information and Post-Code
25 CROSS-FUNCTIONAL REVIEW STANDARDS MANAGEMENT STUDY METADATA DATA PROCESSING REVIEW ANALYSIS eshare Standards Protocol 1 Global Standards Study Metadata 2 IMPORT eshare CDISC STANDARDS CREATE CUSTOMER STANDARDS DEFINE STUDY LEVEL METADATA CREATE SUBMISSION READY SDTM DEFINE.XML SHARE WITH INTERNAL & EXTERNAL STAKEHOLDERS Specifications Analysis Activities ANALYSIS PROGRAMS- JOBS 6 Specifications * CREATION, QC & USAGE (INCL. VERSION CONTROL) * SIGN-OFF EDC External / Other Data Sensors/ Wearables EDC DATA EXTRACTION (Life Science Analytics Framework Extension/App) Other DATA EXTRACTION (Life Science Analytics Framework Extension/App) Event Streaming (SAS Event Stream Process Engine) 4 SDTM Transformation (Life Science Analytics Framework Extension/App) Deliverables (Internal sharing of SDTM data after Structure automated verification) Automated + Manual Review Activities 5 (Life Science Analytics Framework Programs/Jobs + SAS Visual Analytics + Pinnacle 21 (~Open CDISC),...) Cross-Functional Review Tracking (Life Science Analytics Framework Extension/App) Final Deliverables Review Input * AUTOMATION THROUGH WORKFLOW * RUNNING R CODE THROUGH PROC IML VISUALIZATIONS (SAS Visual Analytics,...) ADAPTIVE DESIGN AUTOMATION (Life Science Analytics Framework Extension/App) 7 Internal system / Partner Extraction & Transformation Cross-Functional Review Outbound data 8
26 CROSS-FUNCTIONAL REVIEW: AUTOMATED (1/4) Review Tracking Rules (= Compliance Rule(s) ~Structural, Global Content Rule(s), Trial Specific Content Rule(s))
27 CROSS-FUNCTIONAL REVIEW: AUTOMATED (3/4) Review Tracking Output (Follow-Up)
28 CROSS-FUNCTIONAL REVIEW: AUTOMATED (4/4) Review Tracking Output (Follow-Up) - Details including History, Attached Files.
29 CROSS-FUNCTIONAL REVIEW: MANUAL Opportunity to raise Manual findings e.g. Visualizations (medical review); looking at output, listings; etc.
30 USE CASES
31 USE CASE 1 (PAGE 1/3) BUSINESS ISSUE(S) Needed better Quality Control on Data Management CROs ability to handle changing CDISC data standards (standards management) MAJOR PHARMACEUTICAL PROCESS IMPROVEMENT be able to create Study Level Metadata (Define.XML) and provide to DM-CRO as clear specifications wanted automated review process of DM CROs deliverables (SDTM data + Define.XML) prior to data being given to data management Needed new Data Management platform (in the Cloud) to facilitate new DM processes clean the data (focusing on Primary & Secondary endpoints; Standard work done by DM-CRO) be able to run CDISC Compliance Checks on the data received from the DM-CRO be able to explore the datasets within the same platform Transfer data from/to other stakeholders - systems (e.g. External Data such as but not limited to Central ECG or Central Lab; DM-CRO s end deliverables; Feeds of data into DWH of sponsor; etc.) Repository for all their Clinical Trial Data (incl. Audit Trail, Version Control, Part 11 compliant, Workflow Management) handle growth to allow other areas of clinical R&D to access information and do their analysis in same platform/environment. C opyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d.
32 USE CASE 1 (PAGE 2/3) SOLUTION SAS Drug Development + Custom Application Development by 3 rd party [ Java UI + Oracle DB using SDD API s ] RESULTS MAJOR PHARMACEUTICAL PROCESS IMPROVEMENT PHASE I ( ) Automated DM-CRO review process (less time and review cycles by customer) Quality increase of DM-CRO deliverables (through clear specifications) Increase of Traceability (Job Manifests) & Reproducibility (Jobs) PHASE II ( ) Scalable environment has allowed them to provide access to new teams (Statistical Programmers & Statisticians) for new clinical trials with no new investment in hardware Increase collaboration (partnering) between DM and Stats Programmers/Statisticians Migration of all ongoing clinical trials to LSAF (from own internal Servers / SAS environment) = Recently Started. FUTURE STEPS Provide access to other teams (scale up) As more standards functionalities become available in LSAF (compared to SDD), transition from the Custom Application to LSAF Build-in (standard) functionalities started C opyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d.
33 USE CASE 1 (PAGE 3/3) SOME FIGURES Users on SDD Current Status (including external company users): By End 2016: MAJOR PHARMACEUTICAL PROCESS IMPROVEMENT [ Data Management & Statistical Programming/stats ] C opyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d.
34 USE CASE 2 SOLUTION SAS Drug Development VACCINES SUBSIDIARY OF MAJOR PHARMACEUTICAL COMPANY RESULTS All data management and statistical programming Strict hierarchy set up to complete separate DM (Data Standards Programming and Study- Specific) and statistical work Data transferred from EDC system (Inform) to SAS solution Using Spotfire to help out with clinical data management review process, SAS to clean data Integration with safety system Integrations with Oracle user management database (trial access) and program lifecycle management database SAS solution is also being used by trial operations and clinicians as central and operationalcritical hub for accessing clinical trial data and support trial analytics About +/ users in total, of which about 300 concurrent in the environment C opyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d.
35 AS A REFERENCE
36 THANK YOU! LINKEDIN:
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