This document describes how real-world data was used to help expand a clinical trial for a rare disease. Specifically: 1. Claims data was analyzed to identify patients diagnosed with the rare disease and their physicians, applying inclusion criteria like diagnostic tests and drug treatments. This sized the national patient population and located diagnosing/treating doctors. 2. Physicians were mapped to local hospitals to identify areas with high concentrations of relevant patients and doctors. 3. A density analysis identified the top 20 hospitals that could serve as potential new clinical trial sites based on clusters of physicians and their patient counts. This innovative use of real-world data provided new insights to strengthen the company's rare disease research program.

1. ACCESSPOINT • VOLUME 5 • ISSUE 10 PAGE 19
Enabling clinical trial expansion for
a rare disease using US RWD
The author
Robert Steen, BA, BS
is Principal, CES, IMS Health
Rsteen@us.imshealth.com
Innovative methodologies leveraging real-world data can inform,
strengthen and support expansion of critical drug research and
development programs.
Randomized controlled trials (RCTs) are the
mainstay for demonstrating the safety and
efficacy of medical technologies and for
advancing healthcare worldwide.
While increasingly demanding across all therapy areas and
geographies, the challenges they present are especially
acute in the case of rare diseases,1
where small populations,
limited clinical expertise, and scarcity of specialist
treatment centers pose particular problems for physician
and patient recruitment.
To date, few innovative approaches to facilitating research
in rare diseases have tapped the potential of observational
data.2
However, for one IMS Health client, the creative
application of several large, real-world databases proved
key to expanding its clinical development program, both
strengthening the power of an existing RCT and revealing
significant new patient and knowledge clusters for its future
research in this area.
background
The company was pursuing an RCT in patients with a rare
disease and was keen to identify further sites in the USA,
including both patients and physicians, in order to expand
its research program. Specifically, there was a need to
identify:
• Additional patients with the rare disease
• Subset of eligible patients meeting the trial’s
inclusion/exclusion criteria
• Physicians diagnosing and/or treating the condition
• Metropolitan areas and institutions with a concentration
of patients/physicians
To meet these objectives, the company required an approach
that would first pinpoint patients diagnosed with the
disease and then enable both patients and physicians to be
affiliated and ‘rolled up’ to local hospitals so that these
possible ‘hot spots’ could be compared nationally for their
potential as trial sites. However, the rarity of the disease,
combined with key inclusion criteria, made the process of
identifying eligible patients a complex challenge.
Innovative application of RWD
With access to the broadest, deepest collection of
scientifically-validated RWD datasets and the analytical
expertise to apply them to complex challenges, IMS Health
took a three-staged approach to find clusters of physicians
and patients. This involved sizing the national patient
population; mapping these patients to their physicians;
and affiliating the physicians to local institutions through
the use of a density analysis.
1. Sizing the national patient population with
inclusion criteria
The first step in sizing the national patient population was
to extract anonymous patient-level data from IMS RWD
Claims–US, the most comprehensive integrated US health
claims database available, using the ICD-9 diagnosis code
for the disease in question (Figure 1).
Fully adjudicated claims were analyzed for the period from
June 2012 to May 2013. A 12-month period prior to and post
these dates (June 2011 to May 2014) was then used to find a
second instance of the same diagnosis code to confirm the
accuracy of the original. Several additional inclusion and
exclusion criteria were applied, including administration of
diagnostic tests and intravenous drug treatment, based on
HCPCS codes (Healthcare Common Procedure Coding
System). Using these additional ‘flags’, the patient
population was narrowed down to more closely represent
the segments being targeted. These ‘raw’ populations then
formed the basis of national projections which provided the
client with a customized perspective on the likely size of its
intended patient population.
1
A rare disease is defined in the USA as one that affects fewer than 200,000 Americans and in Europe as one that affects fewer than 5 per
10,000 of the population.
2
Gagn JJ, Thompson L, O’Keefe K, Kesselheim AS. Innovative research methods for studying treatments for rare diseases:
Methodological review. BMJ, 2014;349:g6802. Available at:http://www.bmj.com/content/349/bmj.g6802 Accessed 14 April, 2014.
continued on next page

2. PAGE 20 IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS
PROJECT FOCUS RESEARCH & DEVELOPMENT
Figure 1: IMS RWD Claims−US patient-level database
Next, the process was repeated using IMS RWD LRx–US
prescription data to understand how patients were being
treated – the drugs in use having been researched and
confirmed by the IMS Health clinical team. As before, an
index and look back/look forward periods were used to
identify the variety of treatments prescribed.
An index period of June 2012 to May 2013 was used to
identify the diagnosis code, and a post-index period of June
2013 to May 2014 to flag instances of drug therapy use.
These counts were then projected nationally. Patients were
grouped according to their first-, second-, and third-line
treatments to identify those who had received treatment
and subsequently progressed to a later stage of disease.
This information, combined with the various ‘flags’
included in the diagnosed population from the medical
claims data, enabled the potential size of the target
population to be more accurately gauged for potential future
clinical trial designs.
2. Locating diagnosing and treating physicians
Since the adjudicated claims are based on the CMS-1500
claim form, isolating the patient population also identified
the physician involved. This enabled aggregation of specific
counts of diagnosed patients who had two or more office
visits during the three-year period, according to physician.
The client was then in a position to compare this list of
physicians with its own internal information for
consideration with regard to targeting as well as potential
clinical investigators.
A similar process was used to identify patients receiving
drug therapy since the treatment data also included the
prescribing physician. Taken together, the list of physicians
who had diagnosed and/or treated patients for the condition
provided a valuable reference for clinical planning,
communication and promotion applications.
3. Creating density analysis
Next, the identified physicians were aligned to local
hospitals using IMS Health’s Healthcare Physician Services
reference data. This enables doctors to be affiliated with
local institutions based on their attending/admitting
privileges. The assumption made here is that these will be
the hospitals where the diagnosed patients will be treated
for any acute aspect of their condition. By ‘rolling up’ the
counts of physicians with their respective patient counts,
IMS Health was able to generate a density analysis to show
which hospitals across the country had significant clusters
of relevant physicians and patients (Figure 2).
These groupings could then be analyzed by the client to
identify potential future clinical trial sites as well as new
areas for increased promotion and targeting efforts.
IMS RWD Claims−US was leveraged to conduct the market sizing analysis and identify the targeted clinical population
Pharmacy claims are derived from a subset of the
IMS Health prescription database and provide robust
coverage across pharmacy channels.
• Over 65% of all retail prescriptions in the USA are
catured within the database as well as over 55% of
traditional mail-service and 45% of specialty
pharmacy transactions
• Database contains over 150 million unique
anonymous patients
• Over one million unique prescribers are captured
within the database
Medical claims are derived from electronic routing of
medical office claims through practice management
software and third-party electronic switches to
health insurers or web service providers.
• Over one billion claims are received per year
• Data is collected from 865,000 practitioners
per month
• Patients and providers are demographically and
geographically representative
• All major payer types are represented
IMS RWD Claims–US
patient-level database
Pharmacy
Claims Data
Medical
Claims Data
Retail
Pharmacy
Diagnosis
Claims
Specialty
Pharmacy
Procedure
Claims
Mail-order
Pharmacy
Lab
Values

3. ACCESSPOINT • VOLUME 5 • ISSUE 10 PAGE 21
Use of custom analytics for clinical and
commercial applications
Through the innovative use of related RWD – medical
claims, longitudinal prescription information and
physician-hospital reference affiliations – IMS Health was
able to identify clusters which could serve as a potential list
of new trial sites for future research studies in the rare
disease, and a physician list of potential new contacts who
could serve as investigators. Critically, too, the analysis
shed new light on the disease for the client, providing
previously unknown insights into diagnosed patients,
treatments and experienced physicians, at both national
and sub-national level.
Figure 2: Top 20 facilities diagnosing and/or treating the rare disease
Top facilities by number of doctors
Source: IMS Health
90
80
70
60
50
40
30
20
10
0
Unique Treating and/or Diagnosing Treating Diagnosing