MAXIMIZING THE USE OFPATIENT DATA WAREHOUSE VIA VISUAL ANALYTICS

Presented by Simson Alex & Sandy LeiJohnson & Johnson

2018-10-10 CDISC US Interchange

Agenda

1. Patient Data Warehouse (PDW)

2. PDW Study Browser

3. PDW Visual Analytics

4. PDW Data Conformance

5. Conclusion

PATIENT DATA WAREHOUSE

ENABLING EXPLORATION OF PATIENT DATA ACROSS JANSSEN TRIALS

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PATIENT DATA WAREHOUSE DATA SOURCE (IDAR SAS Data Folders)

a SDTM SAS Dataset Folder

SDTM: AE SAS Dataset

Dataset Metadata

Data and metadata browsed from a SDTM

SAS dataset folder via SAS Universal Viewer

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Patient Data Warehouse – PDW

• A centralized repository for:• Study documents : Protocol | SAP | CSR

• Clinical trial data : SAS datasets

• A technical capability for:• Data driven decision making

• Search, subset, integrate, export, analyze

Early-2013 Late-2013 2015 2016 2017 2018

ImplementStabilize Optimize

5

Conceptualized

and funded

First apps

released.

Partial SQL

Stable

applications.

Fully SQL

Visual Analytics

Development. Platform

Stabilization

PDW System Architecture

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Project Data

Internal and

External Sources

Patient Level

Data

SDTM, ADaM,

Legacy…

Relational Database

Automated

Loading

Processes

PDW System Architecture

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Database Application

Server

SAS Server

Trial

Catalog

Study

Browser

Visual

Analytic

s

Various Web

Applications

SQL

Data Response

Request

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PATIENT DATA WAREHOUSE

CONTENTS

467 Trials

With

Patient

Data

609

Reporting

Efforts

1251

Folders

40447

SAS

Datasets

~ 1.1

Billion SAS

Data Rows

All SAS datasets were loaded into database as is

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Data Divided Into Two Broad Sections

MetaData

SAS Value

Is the knowledge about

~33 Million Records

~1.1 Billion SAS Rows

Metadata is 3% of SAS data

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PDW Study Browser and Visual Analytics

Search

(Metadata in Database)

Select

(Metadata in Memory or in

Database)

Query

(Data, On-Demand)

Analysis Results

Visualization (Analysis

Templates)

Select

(Metadata In Database)

Output Datasets/Metadata

(sas, xpt, csv on file server)

Visual Analytics

(In Development)

Study Browser

(Production V2)

Retrieve Data in Database

(Query via a SAS Program)

Batch Job Submit & SAS

Server

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PDW Use Cases1. Signal Detections (9 studies) - PDW Safety Review proof of concept for Global

Medical Safety

2. Placebo Data Extraction (10 studies) - EMIF Metabolic: reuse of placebo data from completed clinical trials

3. Placebo Response (10 studies) – identify possible factors which resulted in high placebo response rate

4. Explore opportunities to increase trial participation (252 studies)

5. Hy’s Law by compound (17 studies in a compound)

6. Time-to-event for Major Adverse Events (10 studies)

7. Efficacy Analysis by Biological Age (all PDW studies) – Janssen Prevention Center proposal biological age in clinical trials

8. Concomitant Medication & PK response (80 studies) - Impact of a concomitant medication co-administration on PK of approved Janssen mAbs

9. Overall patient disposition rate by reasons of discontinuation (304 studies)

PDW STUDY BROWSER

ENABLING AUTOMATED DATA INTEGRATION & SUBJECT SUBSETTING

PDW Study Browser (SB) - Data Retrieval Tools

• Data Integration: based on metadata selections, datasets are stacked into one file for the datasets with the same dataset name. Conformance to data standard is the key.

• Subject Subsetting: based on metadata conditions/criteria set up, output all datasets for subjects meeting the conditions/criteria.

Note: work flows for both tools are very similar.

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Study Browser – Data IntegrationCross-Study Dataset, Variable, Row Selection

Users’ decision for analysis & reporting tools to perform research/analysis based on extracted data.

Datasets

Studies

Variables

Studies

Values,

Conditions

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A visualization of a Hy’s Law subjectAST/ALT > 3xUL and TB > 2xUL in 30 days

PDW VISUAL ANALYTICS

ENABLING AUTOMATED DATA INTEGRATION & ANALYSIS

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PDW Visual Analytics Data Integration & Analysis(in development )

End-to-End platform by enabling automated analysis from metadata browsing to data extraction to final integrated analysis:

• Set up the connections from a visualization tool to PDW database

• Set up on-demand framework for data query from PDW database to facilitate analysis

• Design and create reusable analysis templates which will populate analysis results based on the data query created by the selected metadata.

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Figure 1: 46 SDTM studies were selected from 3 compounds Figure 2: Select interested datasets (AE,CM, DM)

Figure 3: Select variables to facilitate analysis Figure 4: refreshed analysis results from an analysis template

PDW Visual Analytics

An Example Application Design

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PDW STUDY DATA CONFORMANCE

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PDW Data Integration Use CasePooling 10 Phase 2/3 Studies from 5 Compounds

Data Standardization Efforts Required

• Mapping: map datasets, variables and values

• Derivation: variable or value required by analysis but not created in the original analysis dataset

• Verification: data comparisons, reproduce CSR outputs using integrated data, etc.

Data Integration (by PDW tools):

• ADSL : Subject-Level (50 standard variables)

• ADEFF: Efficacy (44 standard variables, 40 standard efficacy parameters)

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Baseline XYZ DosePooling 10 Phase 2/3 Studies from 5 Compounds

STUDYID DATA SOURCE DATASET VARIABLE XYZ DOSE RANGE

S0001 SDTM CM CMDOSTXT (2,25)

S0002 SDTM CM CMDOSTXT (7.5,25)

S0003 ANALYSIS MEDRVIEW MRXYZDS (0,…,30)

S0004 ANALYSIS EXPHX EHSCRDOS (15,25)

S0005 ANALYSIS SUBJSF XYZDOSBL (0.5,30)

S0006 ANALYSIS MEDRVIEW MRXYZDOS (0,25)

ANALYSIS SUBJEF XYZBL (15,25)

ANALYSIS EXPHX EHSCRDOS (15,25)

S0007 ANALYSIS ADCM CMDOSTWK (7.5,20)

S0008 ANALYSIS ADSL XYZBL (12,5,15,17,…,25)

S0009 ANALYSIS WNLOBS XYZD (10,25)

ANALYSIS EXPHX EHSCRDOS (10,25)

S0010 ANALYSIS ADSL BLXYZDS (10,25)

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Data Standardization ChallengesPooling 10 Phase 2/3 Studies from 5 Compounds

• Same information created in different places - Baseline XYZ Dose

• Same variable applied different derivation - Eg: CHG=AVAL-BASE in some studies and CHG=BASE-AVAL in other studies

• Lack of metadata or variable flags for duplicate records:Without straightforward algorithm to select from duplicate observations/records for analysis is commonly observed in this PDW use case.

• Repetitive efforts for verification – verification tools for both analysis data and outputs need to be created due to the data manipulation above, reports comparisons are manual and time consuming.

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Conclusion

• The new capabilities to search, query and perform analysis over hundreds of studies from PDW platform is a big achievement.

• Adherence to clinical data standards is key to address diverse questions accurately, confidently, and in a timely manner.

• As PDW data content increases, we are continuously improving our technological solutions to address performance issues.

• The PDW Visual Analytics platform technologies will shape the clinical trial processes to make “real-time” analysis possible during any time point of the drug development cycle.

Thank You!