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COMBINE ADVANCED ANALYTICS AND REAL-WORLD DATA TO ACCURATELY IDENTIFY TREATMENT DRIVERS

Proprietary and Confidential: This material is proprietary to D Cube Analytics, Inc. It contains trade secrets and confidential information which is solely the property of D Cube Analytics, Inc.. This material shall not be used, reproduced, copied, disclosed, transmitted, in whole or in part, without the express consent of D Cube Analytics, Inc.. D Cube Analytics, Inc. © All rights reserved

MAY 2020

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MEET THE TEAM

SRIKANTH KATASANI

PRINCIPAL CONSULTANT

Srikanth is a Principal Consultant at D Cube Analytics, where he is responsible for leading high-performance teams in the areas of commercial analytics and

reporting. In his career spanning 8+ years, he has helped many Pharma and Healthcare companies plan and realize their data analytics journey.

SWETANK GUPTA

ASSOCIATE CONSULTANT

Swetank has over 4+ years of experience in working with real-world evidence data to generate key insights at the patient level assisting in different stages of

product life cycle. His work includes implementing effective use of RWD data across different business teams based on their functional needs.

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OUR AGENDA TODAY

1. UNDERSTANDING THE EVOLUTION OF REAL-WORLD DATA

2. NEED FOR ADVANCED ANALYTICAL APPROACH FOR UNLOCKING HIDDEN INSIGHTS FROM REAL-WORLD DATA

3. CASE STUDY: TREATMENT DRIVERS ANALYSIS

A. HYPOTHESIS BUILDING

B. CHOOSING THE REAL-WORLD DATA SOURCE AND FEASIBILITY ANALYSIS

C. MODELLING CONSIDERATIONS

D. MODELLING OUTPUTS

4. CONCLUSION

- Click on the corresponding boxes for navigating to their respective sections

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REAL-WORLD DATA IS GOING THROUGH A TRANSFORMATIVE JOURNEY LEADING TO INCREASED UTILITY FOR BOTH COMMERCIAL AND NON-COMMERCIAL DECISION MAKING

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Evolution of RWD Data Traditional RWD Use-casesKey Attributes Captured

Contains:

• Cost related information

• Data of insured population

• Diagnosis & treatment info relevant for reimbursement

• Captured at point of care

• Lab test results and other patient vitals/biomarkers not captured in claims

• Captured from live health tracking

• Realtime data of patient health diagnostics

• Captures patient sentiments shared online

ClaimsData

EHR Data

Apps, Social Media

Patient Attributes• Patient demographics

• Lifestyle and family history

• Geographic information

Provider Attributes• Site of care attributes

• Physician specialties

• Provider affiliations

Disease Attributes

• Diagnosis information

• Procedure & treatment

attributes

• Disease severity

• Biomarkers data

Payer Attributes• Reimbursement and copay

• Payor plan coverage

• Drug accessibility

Pre-launch Planning

Patient Journey Mapping

Analog Analysis

Patient Flow

Target

Population

Study

Source of Business

Prescribers OverlapAnalysis

Adherence & Persistence

Analytics

Line of Therapy

HEOR Analysis

Performance Tracking

Treatment Effectiveness

Studies

RWD DataRWD Data

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HOWEVER, TO BETTER TAP INTO THE POTENTIAL OF THE NEW-AGE REAL-WORLD DATASETS AND TO UNCOVER THE HIDDEN INSIGHTS, IT'S REQUIRED TO UTILIZE ADVANCED ANALYTICAL TECHNIQUES

Robust Classification Models to

produce highly accurate classifications

of the population being studied

Generalized Linear Models to

generate statistically significant

predictions, relevant to the healthcare

outcomes of interest

Advanced Text Mining & NLP modelsto mine the volumes of unstructured data

to generate patient and provider insights

Advanced Analytical Techniques

Reusable R and Python scripts that

mirror the baseline business rules

and KPI definitions that enable

quick-starts on the projects

NLP-ready drug and disease

taxonomies that help in social

media and other text mining studies

Baseline Business Rules Re-usable Code Modules Pharma Taxonomies

Innovative Use Cases

Acc

eler

ato

rs

Real-World Data

Ready-to-use standardized

business rules and KPI

definitions that can be used for

majority of the analysis

Patient Attributes

Payer Attributes

Provider Attributes

Real-World Data

Treatment

Journey

Optimization

Automated

Patient

Flows

Voice of

Patient

Patient

Attitude

Modelling

Drivers of

Treatment

Voice

Enabled

Chatbots

Economic

Value

Analysis

Treatment

Influencers

Modelling

Guided

Care

Navigation

Experience

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TREATMENT-DRIVERS ANALYSIS IS ONE SUCH ANALYSIS THAT LEVERAGES IN-DEPTH PATIENT LEVEL DATA AND ADVANCED CLASSIFICATION TECHNIQUES TO IDENTIFY THE TREATMENT DECISION DRIVERS

Treatment

Decision

Patient

Demographics

Loyalty

Disease State

Targeting

Influence

Market AccessPeer/Account

Influences

Outcomes Targeted

Factors Driving Treatment Decision

1 Understand the important factors driving treatment decisions at different stages of a patient journey

2 Identify the favorable patient profiles that are more likely to adopt our drug vs. competitor drug

3 Aggregate patient-level insights to prescriber level to identify the favorable prescriber profiles

4 Basis the identified treatment drivers and the favorable treatment profiles, design the relevant targeting strategies

AI/ML driven classification engine which differentiates patients based on treatment drivers contributing to

the treatment decision

Treatment Prediction Engine Analytical Outputs

Universe

Non-Favorable

• Middle-aged groups

• Unknown Disease

Severity

• Market Access

Controlled

Favorable

• Specialist Treated

• High Risk Comorbidities

• Old-aged groups

• High Disease Severity

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THE SUCCESS OF SUCH SOLUTIONS DEPEND ON THE HYPER-CARE THAT MUST BE PROVIDED RIGHT FROM DATA PROCESSING PHASE TO THE MODEL FINE-TUNING PHASE

Variables & Cohorts

Identification

• Identify different patient, clinical,

disease and provider attributes

that can drive a drug decision

• Explore data nuances to frame

business rules in defining the

variables

Modelling & ResultsExploratory Data

Analysis

Understand the most important factors driving drug choices at different stages of a patient’s treatment journey, basing which create favorable patient profiles

for NTM and Switch targetingAnalysis Objective:

• Perform univariate analysis to

understand the distribution of the

identified variables

• Perform bivariate analysis to

understand the impact of the identified

variables on the target variables

Analytical Datasets

Preparation• Retain only those patients that had risk

information

• Stratify the patient universe to maintain

similar # of records between the

primary cohort and the comparison

cohort

• Split the dataset into training and test

sets for building the model

• Shortlist the differentiating variables

based on bivariate analysis, missing

value ratios & variable importance

from Random Forest

• Build iterations with Decision tree and

choose the optimal one based on

confusion matrix statistics and business

relevance

• Identify the favorable patient

profiles, which are more likely to

adopt our drug vs. competitor drug

• Aggregate patient-level insights to

prescriber level to design the

relevant targeting tactics

Recommendations &

Actions

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THE VERY FIRST STAGE IS THE VARIABLES AND COHORT IDENTIFICATION PHASE, WHERE WE IDENTIFY THE KEY TREATMENT DECISIONS AND THEIR CORRESPONDING POTENTIAL DRIVERS TO BE EXPLORED

NTM Analysis• Understand the profiles of patients who

can initiate treatment with client’s drug

Switch Analysis• Identify the key drivers and profiles of

patients who switch from a competitor

drug to client’s drug

Persistent Analysis• Identify the key drivers and profiles of

persistent patients on client’s drug

Continuing Patient Analysis

• Understand the profiles of patients who

continue client’s drug

Oral vs IV Analysis• Understand the profiles of patients who

initiate on IV therapies

Patients who are New to Market on client’s drug

Patients who are switching to client’s drug

Patients who are persistent on client’s drug

Patients who continue on client’s drug

Patients who start on IV therapies

Patients who are New to Market on competitor’s drug

Patients who are switching to the competitor’s drug

Patients who are not persistent on client’s drug

Patients who switch or drop from client’s drug

Patients who start on oral therapies

Objective Study Cohort Comparison Cohort

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ILLUSTR

ATIV

E

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THROUGH A HYPOTHESIS BUILDING APPROACH, ALL THE KEY FACTORS THAT CAN INFLUENCE A TREATMENT DECISION WILL BE IDENTIFIED TO BE FURTHER ANALYZED

• Age• Gender• Race• Ethnicity• Region• Income status• Occupation, family size,

education

Demographic Attributes

• Comorbidities• Concomitances• Previous drug history• Current drug therapy• Patient pref. to the

types of drugs

Disease & Treatment Attributes

• Risk status of the patient

• Previous history of adverse events

• Biomarkers• Patient reported

symptoms• Vitals

Clinical History related Attributes

• Provider specialty• Facility type• Provider affiliation• Treating vs diagnosing

specialty• Payer plan mapping

Payer & Provider Attributes

Factors affecting a Prescriber when selecting a drug

Factors affecting a Patient while choosing a drug

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ILLUSTR

ATIV

E

Performed Univariate and Bivariate analysis to analyse the effect of variables on the cohorts

Univariate Analysis

Bivariate Analysis

Estimated the richness of data source for all the filtered variables

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THE NEXT STAGE IS THE FEASIBILITY ANALYSIS PHASE, WHICH INVOLVES DEEPER UNDERSTANDING OF THE DATA SOURCES FROM THE TA INSIGHTS PERSPECTIVE

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Integrated RWD Data

• Captures point of care health

information

• Diagnosis conditions

• Drug and procedural information

• Patient and physician sentiments

from physician notes (NLP)

• Lab test results, vitals and body

measurements

• Provider specialty

• Alcohol and smoking status

1. Data Source 3. Analysis to identify key variables

Rejected due to low fill rate

4. EDA on filtered variables

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Additional business rules applied to arrive at the intermediate patient pool for further optimizations

Initial patient pool based on cohort definitions

Patients with onlyrelevant attributes

Patients after applying eligibility criteria

2. Patient Funnel

100%

75%

60%

ILLUSTR

ATIV

E

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IN THE FOLLOWING STAGE, THE ANALYTICAL DATASETS ARE CREATED, AND THE REQUIRED PRE-PROCESSING IS APPLIED TO MAKE THEM MODELLING-READY

Master Data

Creation

Extract and integrate all the custom variables into one table with outcome of the event

Feature Engineering

Convert the data variables into modelling input features by applying required transformations

Robust Tracking:

• Ensure continuous coverage of patient data through active enrolment criteria

• Coverage across Pharmacy and Medical Claims both

• Ensure unique value for each variable

Integrating the Datasets:

• Combine attributes from multiple tables

• Apply deduplication rules to create final dataset

One-Hot Encoding:

• Convert categorical variables into numerical variables

• Comorbidities, concomitances to be created into separate variables based on their value

Custom Variables Creation:

• Create custom attributes from the raw attributes, more relevant for the analysis, e.g. Age to Age Groups

• Final data to be at each outcome level

Pre-processing:

• Using final list of features, stratify the model to give equal weightage to target cohort and comparison cohort

• Based on stratification, split the dataset into training and test dataset for random forest analysis

Variable Importance:

• Run the model on training dataset and validate on the test dataset

• Obtain variable importance and accuracy metrics. Use performance tuning to arrive at the best fit model

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3Final Analytical

Dataset

Perform required treatments to generate the final dataset to run our models on

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RANDOM FOREST AND DECISION TREE ANALYSIS ARE USED IN COMBINATION ON FINAL DATA TO ARRIVE AT THE ACTIONABLE RESULTS

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1Ensemble of decision trees is used to select the

best set of parameters for maximum accuracy

High accuracy compared to the decision

tree model

Scoring of the model is done based on Random

Forest

Only the relevant variables from random

forest are fed into the decision tree model

Simple classification methodology to split

the data for the target variable

Used to represent the analysis visually, that

can be consumed by end business users

The model output is aligned with the results of

random forest and hence combined with it

Highlights the key drivers for which targeting

decisions need to be taken

Random Forest Decision Tree RepresentationModel

ScoringModel

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4

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2

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THE DECISION TREE ANALYSIS HELPS IN UNDERSTANDING THE PRIORITIZED FACTORS DRIVING TREATMENT DECISIONS

HCP Specialty

RiskSegment

Prior Adverse Event

IDN Affil.

High Drug A

Higher Drug B

Higher Drug A

Higher Drug B

Higher Drug B

Overall Accuracy 81%

Favorable class Accuracy 84%

Non-favorable class Accuracy 78%

Favorable class Precision 84%

Non-favorable class Precision 78%

Accuracy MetricsDecision Tree

• Push for early touch-points with specialists for treatment

• Currently, only 16% patients are meeting a specialist for 1L treatment

2/3rd of the HR patients without prior AE are still not getting treated with Drug A

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3

3

3

3

3

3

3

4

7

8

0 5 10

Cb - Osteoarthritis

Prior Adverse Event

Cb - Joints Disorder

Body Measure Index

Cb - Acqd. Hypothy.

Risk Segment

Scan Freq Bucket

Risk Factor

Age Bucket

Region

Speciality

Index Value

Variable Importance – Top Variables

Drug BDrug A

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THE INSIGHTS THUS DERIVED HELPED IN PRIORITIZING/CUSTOMIZING THE TARGETING MESSAGES IN PHYSICIAN DETAILING

Key Insights Recommended Actions

• Adheres to prescribed medication but require copay assistance

• Persistence & adherence takes a hit in absence of copay assistance

• Large gaps between medication exists

• Does not follow up with prescribers regularly

• Requires reminders for medication & doctor visit

• Frequently goes on and off therapy

• Discontinues therapy & this also impacts health

Severity of the Disease

Specialists Adoption

IDN Sensitivity

Comorbidities

Patient profiles favoring client drug were studied in

combination with drivers of the treatment choices

• Increase awareness of timely diagnostic procedures with

HCPs

• Bundle discount programs for diagnostic procedures

• Target specialists for front-line Tx targeting

• Increase KOL events for geographies where non-

specialist adoption is lower

• Stick to IDN targeting with account managers for IDN

linked facilities

• Push for strong front-line targeting for rest of the

entities

• Conduct post-launch trials to understand the potential

label extension

• Patients stratified as High-Risk and Very High-Risk have higher adoption of the biologic drugs

• However, the diagnostic procedures required for the risk assessment are not adopted owing

to high costs

• Specialists have a high preference for the client’s drug as the front-line treatment

• Whereas, non-specialists have lower adoption of client’s drug even in the second-line

treatments

• The choices of treatment are very rigid in the facilities with strong IDN influence

• Whereas, the other facilities had displayed more variation in the treatment choices

• Patients with high risk comorbidities like Vitamin D insufficiency have stronger adoption of

client’s drug

The on-going targeting activities are validated against

the derived insights to identify course-corrections

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ILLUSTR

ATIV

E

SIMILARLY, ADVANCED ANALYTICS CAN BE COMBINED WITH REAL-WORLD DATA TO UNLOCK HIDDEN INSIGHTS ACROSS MULTIPLE COMMERCIAL USE CASES

Finding the right prescribers for a novel drug in the market is crucial to its success. When the company can concentrate on the right audience and allocate their resources to the right efforts, they can channel their product to the right consumers

Advanced analytics can be employed in not only identifying the key decision makers in a patient’s journey, but also to estimate potential that can be achieved through targeting them

Treatment

Influencers

Modelling01

Social media and patient support forums are rich sources of information when it comes to patient feedback and experiences:

Apart from seeking guidance on post-diagnosis journeys, many patients express their experiences with existing therapies and expectations from the upcoming treatments.

Pharma companies can benefit from this information in understanding unmet needs and the targeting messages for their drugs

Voice Of Patient04

No two patients starting on the same front-line therapy for a given disease progress in a similar manner for rest of their treatment journey

There’s huge value in mapping out the drivers, opportunities and risks that are driving the patient’s attitude towards choice oftreatment and the adherence to the same

Patient Attitude

modelling03

More and more patients are now cognizant and involved with their overall health status and the best practices that they should follow for a healthy and stress-free life, which has become popular using different healthcare apps, digital devices, etc.

Using the data from the devices and with seamless connectivity to patient, we can track the overall patient characteristics and help them become more persistent on a therapy by sending alerts, reports, etc. while also providing virtual assistant for general queries

Guided Care

Navigation

experience02

Overall healthcare cost is something that both government and employers are working to optimize. Employers want to save on the premium costs while providing optimal care to their employees

Using advanced analytics and claims data sources, we can analyze the characteristics of patients and predict their future ailments and average cost that will be incurred by them. Based on it and their current income, we can suggest the best plan suited for them

Economic Value

Analysis05

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D Cube Analytics Inc.1320 Tower Road,

Schaumburg, Illinois 60173, USA

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