Patient Dynamics Forecasting

Objective Insights employs a patient dynamics forecasting approach when a sequential segmentation method doesn’t adequately capture disease characteristics or if more patient segment detail is desired.

The patient dynamics approach accounts for the transition of patients through different stages of disease or treatment. This type of forecast is most appropriate for oncology, infectious diseases, progressive chronic diseases, or when warranted by the nature of the disease (multiple stages, lines of therapy, disease progression, etc.). This approach also takes product persistence and compliance into account when forecasting product volume.

Objective Insights uses an iterative approach to modeling. We develop a conceptual design, review it with you, and adjust as necessary until we have a design that meets your project needs. This process involves understanding your target product profile and the epidemiology and treatment patterns of the disease.


After the forecast design has been substantially completed, we research the information necessary to drive the forecast model, typically from public sources such as epidemiology databases and the medical literature. For a oncology project, for example, we may obtain incidence rates from SEER (NCI Surveillance, Epidemiology, and End Results) or IARC (International Agency for Research on Cancer) and survival and progression rates from published trials of significant products used to treat the tumor type.

Driven by Survival Analysis

The key drivers of our patient dynamics models are the functions governing hazard rates (survival, progression, persistence) in each treatment state. We typically find Kaplan-Meier curves for these measures in the medical literature, extract the data, then use statistical analysis to determine the hazard functions that most closely approximate the underlying patient data. These curves then determine the month-to-month probabilities of a patient moving to a different state.

Survival Distribution Graph

We use the selected parametric curves in our models, where we can alter the parameters to adjust the shape or change factors like median survival. We can also apply ranges around the parameters for use in Monte Carlo simulation to aid in understanding of uncertainty and sensitivity.

Model Structure

Objective Insights strives to make our patient dynamics models parametric, transparent, and easy to use, while still offering extensive modeling capabilities. Model inputs are easily changed and can be overridden on an annual or monthly basis.

LTF Lifecycle Entry

We construct our models in Microsoft Excel at a monthly level and provide an annual summary over the forecast horizon. We also provide a launch forecast at a monthly and quarterly level over the first six years, summarizing the important volume and revenue outputs.

Objective Insights calibrates our patient dynamics models to reflect market data from survey- or claims data-based secondary data sources.

We would be happy to discuss your forecasting needs and explain how our comprehensive approach can address your business planning requirements. Please contact us to set up a free consultation.