Are you interested in RWD, RWE, Nordic data, or longitudinal data analytics? Then we warmly encourage you to sign up for Quantify’s ISPOR courses “Why All the Hype? Nordic Data Explained” and “Analysis of Longitudinal Data – Fixed and Random Effects Models”. The courses will be held on November 2nd at ISPOR Europe 2019 in Copenhagen and are led by Quantify’s Kirk Geale, Fredrik Borgström, Hanna Fues Wahl and Ingrid Lindberg.

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Why All the Hype? Nordic Data Explained
2 November 8:00 – 12:00

Nordic data has become a hot topic in epidemiology, HEOR, and other observational research in recent years, and for good reason. Denmark, Finland, Iceland, Norway, and Sweden maintain datasets covering the entire Nordic population of over 26 million inhabitants for the duration of their lifetimes. The data includes information on demographics, diagnoses, prescription drugs, socioeconomics, PROs, disease severity, and more. All of this data may all be linked through unique, patient-level social security numbers. The coverage and breadth of data available to researchers in the Nordic countries is globally unparalleled. This course focuses on describing the content and structure of the Nordic data in terms of the variety of ways patients are included, how long they are followed up, what variables are available, and the quality of the data. This leads directly into a discussion about how this data can benefit researchers as well as the key limitations. The course will also consider practical aspects of using Nordic data, including data access from legal and procedural perspectives. Comparisons to other well-known European data such as CPRD will be explored. Key applied examples of analyses using Nordic data will be presented to illustrate the usage and possibilities of this data, including registry-based randomized clinical trials.

Analysis of Longitudinal Data – Fixed and Random Effects Models
2 November 13:00 – 17:00

Longitudinal data is often encountered by researchers, allowing them to use the time dimension to uncover deeper insights than what is possible with a traditional cross-sectional snapshot. But this advantage comes at a cost: the assumption that each observation is independent is broken due to the fact that patients are measured on multiple occasions over time. Failure to account for this feature when analyzing data can result in bias, and longitudinal methods should be used to account for this problem. Two powerful but simple solutions are the fixed and random effects estimators, both of which have recently become more popular in medical research. A key feature of both is that they model the unobserved differences between patients, and can even control for unobserved confounding. This course will discuss the methods and intuition behind both modelling techniques, alongside practical examples and interactive sessions in STATA. Attendees will gain both knowledge and practical skills in this course.