PURPOSE: Cancer treatment is complex, involving the sequential use of different treatment regimens as disease progresses. This workshop will discuss the challenges of identifying distinct lines of therapy (LOT) in real-world oncology studies and present best practices to generate reliable LOT algorithms. The workshop will provide practical examples of cancer treatment pattern analysis using retrospective, observational, real-world data (RWD).

DESCRIPTION: The accurate quantification of LOTs is critical for the identification of distinct points of care for comparative research, treatment decision-making at disease progression, and for the accurate evaluation of treatment pathways. There is no standard method to define LOTs for solid or hematologic malignancies, in part due to disease heterogeneity and the rapid pace of treatment innovation.

The learning objectives of this workshop are:

• To understand the importance of accurately defining LOTs
• To identify the necessary components of a LOT algorithm
• To distinguish the variables that can impact algorithm development from different databases
• To assess the differences in algorithms by tumor type
• To critically evaluate research using LOT algorithms
• To share a case study using administrative claims and clinical data

The content will include:

• LOT background and key considerations (Chi Nguyen, 10 minutes);
• Step-by-step best practices to develop and validate LOT algorithms (Lisa Hess, 15 minutes)
• Case study example of LOT analysis using large administrative claims data integrated with clinical data (Bal Nepal, 15 minutes).
• Challenges with data sources; algorithms for solid vs. hematologic malignancies (Julia Slejko, 10 minutes).

Participants will have the opportunity to interact and share experiences and challenges working with oncology LOT algorithms using RWD via real-time polling and interactive discussion. This workshop is designed for both academic and industry scientists interested in using RWD for oncology-related research.