The Use of Real-World Data for Studies of Dynamic Disease Processes

Obtaining valid real-world evidence about intervention effects from observational cohorts or administrative health records data is challenging. Visits to healthcare providers tend to occur more often during periods of increased disease activity and symptom exacerbation, or upon disease progression. Treatments likewise tend to change when it is apparent that disease activity has increased or a meaningful progression has occurred. This creates a dual problem that patient visits are disease-related, and treatments changes are driven by disease condition and clinical presentation. Disease-related visits and treatment by indication can produce a biased impression of the disease process in the target population and of the effects of treatment. We discuss how these challenges can be addressed through the use of joint models for the disease, marker and treatment processes, as well as the observation (visit) process. Using illustrative multistate models we demonstrate the biases that can arise from various types of analysis, and show how estimators from fitting such joint models to persons with psoriatic arthritis can be used to gain scientific insights and address common questions about treatment effects.