Novel Approaches to Plugging Data Gaps
Written by Elisabeth Fenwick & Marjanne Piena
Data gaps are common in all stages of drug development. In a recent webinar, in collaboration with ISPOR, we presented novel modeling approaches that bridge data gaps to support stakeholder decision-making over the product life cycle.
Data gaps are common in all stages of drug development, from discovery and evidence generation to support reimbursement and access, through to marketing and loss of exclusivity. Bespoke modeling solutions are required to address these data gaps and the challenges they bring. The outputs of these customized models can be used to help inform stakeholders’ decision-making. In a recent webinar, in collaboration with ISPOR, we focused on novel modeling approaches that plug these data gaps. The framework we presented bridges data gaps to support stakeholder decision-making over the product life cycle. Additionally, we shared an example of how we used pharmacometric (PM) modeling to perform a pharmacoeconomic (PE) evaluation in a post-marketing setting. Two recent scientific publications were used as input for the ISPOR webinar.
The publication Value of Information Analysis for Research Decisions – An Introduction: Report 1 of the ISPOR Value of Information Analysis Emerging Good Practices Task Force introduces the concept of value of information analysis used in the framework to drive and manage research decisions and ensure that the appropriate data is available to support stakeholder decision-making over the product life cycle. The case study presented during the webinar was based on An Integrated Pharmacokinetic-Pharmacodynamic-Pharmacoeconomic Modeling Method to Evaluate Treatments for Adults with Schizophrenia, which details how a PM model was used to fill data gaps related to clinical outcomes to enable a PE analysis. Briefly, multiple doses of long-acting injectable medications for schizophrenia are on the market. Due to limited clinical data, the relative costs, efficacy, and cost-effectiveness of the different dose regimens are unclear. Innovative techniques utilizing PM modeling allowed Modeling & Meta-analysis consultants to create a comparison framework.
Figure 1 The three aspects of PK-PD-PE modeling: Pharmacokinetic (PK): Models drug concentration in the blood for individual patients. Pharmacodynamic (PD): Uses drug concentrations to predict the probability of a relapse. Pharmacoeconomic (PE): Calculates costs and effects of the different dose regimens based on their relapse probabilities.
Watch the webinar if you would like to:
- Understand the common data gaps in health economic (HE) analyses and the need for bespoke modeling solutions
- Consider an application of a PM model used for a PE analysis to fill a data gap relating to clinical outcomes
- Explore further methods and opportunities to navigate and fill data gaps for HE analyses in different stages of drug delivery
Elisabeth Fenwick, is a Senior Director in Modeling and Meta-Analysis, located in the Oxford, UK office. Liz provides scientific and strategic support to Health Economics projects globally. She has extensive experience in economic evaluation and health economic modelling having worked in the field for over 20 years.
Marjanne Piena is an Associate Director in Modelling & Meta-Analyses, located in the Rotterdam office. Marjanne’s main expertise is in health economic modelling in many disease areas, including multiple sclerosis, hepatitis, oncology and orphan diseases. In addition, she is experienced in survival analysis and supporting reimbursement requests across Europe, and particularly in the Netherlands.