Using Data to Design and Execute Studies to Support End-to-End Real-World Evidence Needs

According to the International Society for Pharmacoeconomics and Outcomes Research (ISPOR), real-world evidence (RWE) is the #1 trend in HEOR for 2020 and is playing an increasing role in health care decisions globally. Regulatory bodies such as the FDA and EMA have traditionally used RWE to monitor post-market safety and adverse events, but are now beginning to use RWE to make regulatory decisions. The healthcare community is using RWE increasingly to support coverage decisions and to develop guidelines and decision support tools for use in clinical practice.

Our Real-World Evidence and Data Analytics (RWE/DA) team leverages data from sources such as medical charts, administrative claims data, electronic health records, registries, and government databases to answer questions about disease epidemiology, burden, treatment patterns, comparative effectiveness, and costs. The studies may also be used to populate and validate economic models, support strategic value messages, and integrate patient-reported outcomes (PROs) with other sources of data as appropriate.

Our ‘data agnostic’ approach ensures that we will aim to find the best dataset to meet your research question. This, along with our strategic insights on study design and innovative analyses, provide tailored, fit-for-purpose insights that demonstrate the real-world value of healthcare innovations and interventions.

Pre-clinical Development

Real-world data should be used early in the drug development process to inform target populations for clinical trials, estimate achievable sample sizes, and identify relevant clinical outcomes.

Phase I/II – Clinical

Early in clinical development is the best time to initiate studies that aim to characterize disease burden and unmet needs. These studies are critical for demonstrating to payers and other decision makers that they should invest in the condition of interest and its treatment.

Phase III - Clinical

During Phase III clinical development, current treatment patterns and cost drivers should be assessed to inform economic models and market access strategy. In addition, a post-marketing RWE strategy should be formulated to guide post-marketing activities.

Post-Approval

Following regulatory approval, RWE can be used to confirm clinical effectiveness, fulfill post-marketing efficacy and safety obligations, and assess comparative effectiveness of treatments. RWE is increasingly used in supporting label expansions to target new indications and patient populations.

Types of Real-World Data

Primary Data

Data collected by the investigator themselves for a specific purpose

Registry

Prospectively collected clinical data either from patient charts, clinicians, caregivers, and/or the patients themselves

Chart Review

Data collected by a study investigator and other staff from a patient’s paper or electronic medical chart

Survey

Data collected directly from respondents (clinicians, patients, and/or caregivers)

Secondary Data

Data collected by someone else from some other purpose

Registry

Use of an existing registry for research purposes

Claims Data

Data downloaded from electronic billing records in structured fields

Electronic Health Records

Data downloaded from electronic patient charts in structured fields, sometimes supplemented with curated free-text data

Why Us?

Our real-world data and analytics experts are actively involved in the field. We have the experience needed to guide you to the right solutions and be a partner in support your research.

RWE/DA Projects

Claims Database Analyses

Government Database Analyses

Boston, United States

Rachel Shah

Bethesda, United States

Hrishikesh P. Kale

PhD

Bethesda, United States

Xiacong Li

PhD, MHA, BMed

Shanghai, China

Nehemiah Kebede

Bethesda, United States

MSc

Rotterdam, The Netherlands

Jordana Schmier

Bethesda, United States

Nifasha Rusibamayila

BA, MPH

Bethesda, USA

Wei-Jhih Wang

PhD

Bethesda, United States

Sanika Rege

MS, PhD

Bethesda, United States

Real-World Evidence and Data Analytics Publications

Date

Headline

Novel Risk Stratification Algorithm for Estimating the Risk of Death in Patients with Relapsed Multiple Myeloma: External Validation in a Retrospective Chart Review

Authors: R Hajek, S Gonzales-McQuire, Z Szabo, M Delforge, L DeCosta, MS Raab, W Bouwmeester, M Campioni, A Briggs

A QTWiST Analysis of Glasdegib Plus Low‐Dose Cytarabine Versus Low‐Dose Cytarabine as Initial Therapy for Acute Myeloid Leukemia in Patients Who Are Not Considered Candidates for Intensive Chemotherapy

Authors: CT Solem, TJ Bell, Y Kwon, JC Cappelleri, C Johnson, H Bhattacharyya, CJ Hoang, JE Cortes

Real‐World Treatment Patterns and Outcomes in Non‐Transplant Newly Diagnosed Multiple Myeloma in France, Germany, Italy, and the United Kingdom

Authors: M Mohty, W Knauf, D Romanus, S Corman, K Verleger, Y Kwon, D Cherepanov, MJ Cambron-Mellott, HG Vikis, F Gonzalez, F Gavini, K Ramasamy

Early Switch/Early Discharge Opportunities for Hospitalized Patients With Methicillin Resistant Staphylococcus Aureus Complicated Skin and Soft Tissue Infections: Saudi Arabia and United Arab Emirates

Authors: A El Houfi, A Al Thaqafi, T H Alenazi, F Farahat, CT Solem, JM Stephens, C Johnson, C Macahilig, WY Tang, S Haider

Real-world Treatment Patterns and Outcomes Using Terlipressin in 203 Patients With the Hepatorenal Syndrome

Authors: K Moore, K Jamil, K Verleger, L Luo, N Kebede, M Heisen, S Corman, R Leonardi, R Bakker, C Mai, N Shamseddine, X Huang, A Allegretti

Recovery From Opioid Use Disorder (OUD) After Monthly Long-acting Buprenorphine Treatment 12-Month Longitudinal Outcomes from RECOVER, an Observational Study

Authors: W Ling, VR Nadipelli, MS Vijay, A Aldridge, NA Ronquest, C Solem, H Chilcoat, V Albright, C Johnson, S Learned, V Mehra, YC Yeh, SM Learned, C Heidbreder

Please get in touch

To learn more about our Real-World Evidence and Data Analytics capabilities and discuss what we could do for you, please get in touch.

Shelby Corman

Real-World Evidence and Data Analytics Lead

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