Five Tips to Improve Your Oncology Forecasting Efforts
Navigating the oncology market can be a challenging task for forecasters. Unlike other therapeutic areas, forecasting oncology treatments and drugs require a very unique approach due to the complexity of the disease.
Since oncology treatments are designed to target a particular patient population depending on the line of therapy, presence of biomarkers, or tumor types, forecasting models should factor in every critical element, such as patient identification, the likelihood of treatment switching and discontinuation, and time of therapy for different patients.
However, it is not always easy to develop detailed and accurate forecasts in this space, especially when the oncology environment is a rapidly evolving one. Hence, there is a significant need to adopt oncology forecasting best practices for better accuracy, increased reliability, and model robustness.
Here is a list of 5 essential tips to help forecasters navigate the shifting sands of oncology treatments and build an effective forecasting approach.
1. A monthly forecasting model
Before building an oncology forecast model, it is important to understand the level of data granularity that users demand on an immediate and mid-to-long term basis. Annual models, albeit easier to build and maintain, do not answer key business questions like monthly sales. It can also be difficult to adapt to an event like a data readout, where changes in forecasting output are needed at a monthly level by business users. These challenges make annual models inflexible with low precision. On the other hand, a monthly model can offer an ideal time granularity for forecasting because it incorporates oncology-specific dynamics based on available monthly data.
2. Identifying the right target patient pool
Forecasting in oncology is different from other therapeutic areas because of the significant need to follow patients through different stages, lines, and treatments as they progress through the disease. As important as this is to do, inaccurate identification of the target patient pool has been a common pitfall in oncology forecasting. Forecasters should split the population into smaller and more specific segments, and accurately model them based on incidence, recurrence, diagnosis, treatment, and other important factors to maximize the accuracy of forecast outputs.
3. Understanding dynamic patient flows
Forecasters must be able to model patients through the different stages of the disease as cancer therapy models have become more complex. They need to assess the advancement of each patient segment, understand how patients move between the lines of therapy, analyze dosing regimens, rates of progression, remission and discontinuation, patient dependency on old and new drugs or therapies, and more. A holistic understanding of the disease space, as opposed to a myopic one, is critical for forecasters to model such complex and dynamic patient flows.
4. Understanding dosing patterns
Understanding the dosing patterns of your target patient population is crucial before building a forecasting model. In oncology, drug dosing is influenced by patient-specific characteristics such as body surface area, age, weight, and more. Each of these inputs need to be modelled differently, because patient segmentation and the associated granularity heavily depend on drug dosing specifications.
Duration of Treatment (DoT), Persistency Curve, and cohort models can be considered for oral targeted therapies that carry a fixed dosing approach. These factors can also be considered for monoclonal antibody (mAbs) therapies that have a weight-based dosing approach.
5. Estimating bolus demand
In addition to estimating the first-time users of your new product, forecasters must also look at 2 additional streams where patients could potentially flow in from:
1) Patients transferred from one product to another midway before completing the line of therapy
2) Patients re-initiating the treatment after temporarily discontinuing it due to tolerability issues
This is called the Bolus demand, and it forms a critical part of any forecasting model. Forecasters often tend to underestimate the demand from these channels, but it accounts for an average of 30% of the total product demand in the first 6-to-9 months of launch.
These are our top 5 picks for strategies that we believe will define the effectiveness of oncology forecasting. Forecasters need to pay attention to the changing dynamics, complexities, and steer clear of common pitfalls that can break their forecasting model as oncology continues to be one of the fastest growing therapeutic areas in the pharma industry. These 5 tips should help you in implementing best practices and bring better accuracy and more insights to your forecasts.