The landscape of RWD can be difficult to navigate.

Real world data mapping provides a tool to systematically identify and evaluate RWD sources.

Real-world data (RWD) increasingly inform drug development, health technology assessment, and drug regulation (peri- and post-approval). They refine clinical guidelines by characterizing a product’s clinical outcomes in more diverse, real-world patient populations. RWD includes data from a variety of sources, including national and regional registries, electronic medical records, claims databases, health surveys, and clinical databases. The growth and heterogeneity of available RWD sources has created a data landscape that is increasingly difficult to navigate. These days, identifying data sources to help answer strategic or study objectives is rarely an obvious choice.

Data source mapping provides a tool to systematically identify and evaluate RWD sources. This is a flexible methodology that can be customized to meet company needs, whether to locate data sources to answer a specific study question or to get a larger overview of RWD sources for an indication of interest. Electronic tools help with navigation through an increasingly complex universe of data sources.

Join us to get an overview of data source mapping methodology and how it can be leveraged to uncover and navigate RWD. In this webinar we will:

  • Describe what a data source mapping is and where it can be useful
  • Outline the components of a data source mapping and how these can be tailored to meet company objectives

Audience

This webinar is intended for professionals from pharmaceutical, biotech, and medical device companies involved in:

  • Epidemiology
  • Health economics & outcomes research
  • Marketing
  • Market access
  • Medical affairs
  • Real world evidence
  • Regulatory affairs
  • Clinical outcomes

Presenters

Rene Schade, MD, MSc

Senior Principal, Real World Evidence Strategy and Analytics

René Schade has 20+ years of experience in clinical medicine and/or health sciences, with 14+ years of postgraduate experience in (pharmaco)epidemiology and medical informatics, mainly with the use of large databases of longitudinal electronic health records, insurance claims, and patient registries. He has a wealth of experience in designing, executing, and coordinating epidemiology studies in highly collaborative, mostly multi-country, real-world data and advanced analytics projects. His projects have encompassed methodologies to assess drug safety, comparative effectiveness, indirect treatment comparison (external controls), health resource utilization, treatment patterns, burden of illness, disease comorbidity, and methods for terminology mapping and common data models. René is a senior leadership team member in ICON’s Real World Evidence and Analytics (RWESA) group. He provides strategic direction and coaches an international team of epidemiology, health economics and data analytics experts at ICON.

Dr. Bernd Schweikert, MSc

Senior Research Consultant, Real World Evidence Strategy and Analytics, ICON plc.

Bernd Schweikert is a health economist with over 15 years of experience in health economics, epidemiological research and evidence generation, and data analytics, working with large longitudinal data sets claims data sets in different countries. Bernd has extensive expertise in study design and conceptualization; advanced epidemiologic methods; data collation and analysis; and dissemination into the scientific community. At ICON, Bernd is responsible for designing, leading, and analysing observational studies, including retrospective database studies and chart reviews in a variety of indications. 

Ruchika Sharma, BDS, MPH

Analyst II, Real World Evidence Strategy and Analytics, ICON plc.

Ruchika Sharma has over 6 years of experience encompassing epidemiology, public health, and health care. At ICON, Ruchika has worked in multiple database mapping and feasibility studies across Europe, North America and Asia-Pacific. She has extensive experience on literature review, disease forecasting techniques, risk-factor modeling, patient-population sizing, epidemiological methods and study design with expertise on analyzing large population-based databases like HCUP, NHANES, SEER and NSSO. Ruchika has worked on a variety of therapeutic areas like infectious diseases, neurology, oncology, cardiovascular diseases, metabolic disorders, immunology and respiratory diseases.

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