Title: Modelling the Natural History of Rare Diseases Using Disparate Data Sources: Application to Patients with Duchenne Muscular Dystrophy.
Funder: Department of Health and Social Care

Abstract: Background Health technology assessment (HTA) compares the effect and value of two or more treatments for a disease. Often, the disease in question will have a number of key phases that a patient progresses through, with different treatments/resource requirements\quality of life at each stage. These stages can be represented by a multi-state model. To compare treatments in progressive diseases, a baseline model (termed the natural history) is estimated to represent the underlying transitions made by the average patient with standard/current treatment, to which different treatments can be compared. This project will address the deficiencies of current methodology when modelling the trajectory of rare progressive diseases, such as Duchenne Muscular Dystrophy (DMD), as data sources will scarcely be available in either sufficient quantity or quality and current methods do not suitably account for this. Aims The aims of this project are to: Identify methodology for combining evidence sources to construct and populate multi-state models of disease histories. Develop the methods to ensure robustness to a variety of data sources of differing quality, as is often the case in rare disease research. Evaluate the methods by a simulation study and application to DMD data. Evaluate the impact of the methods on economic models via a sensitivity analysis and provide recommendations for future HTA of rare diseases, and DMD in particular.Methods I will achieve these aims with the following methods: I will first perform a literature review of the current methods for combining a variety of data types for use in multi-state models. I will extend a review of the appropriate sources for populating natural history models to consider cases when these sources are unavailable, or are obtained from different populations. Where these methods do not suitably account for heterogeneity between data sources, I will extend them accordingly to ensure their suitability in the context of rare diseases. I will create freely available software packages implementing new methodology for use by fellow researchers. I will apply the methods to simulated data representing different real-world possibilities of evidence availability, and to a variety of DMD data, evaluating sensitivity and robustness. I will use the developed natural history modelling to fit health economic models, comparing HTA outcomes and conclusions from the different methodologies. I will make recommendations on future study design and analysis of rare diseases.Impact The methods developed will be applicable to future evidence synthesis of rare diseases, which will greatly assist HTA for such diseases. They will be disseminated to researchers via publications, conference presentations and a workshop at the end of the fellowship. The work will assist methodological and statistical developments, and software packages will enable implementation of the methods in HTA decision-making, which will translate into a benefit in health care for patients that suffer from numerous rare diseases. The work will also be regularly disseminated to Duchenne UK Project HERCULES, a multi-stakeholder collaboration for DMD, and to broader settings such as the European Rare Diseases Conference, who will see the substantial benefits of the project from their perspectives.

Lay abstract: Background When a new intervention (such as a drug or therapy) for a disease is developed in the UK, health agencies such as the National Institute for Health and Care Excellence (NICE) evaluate it against existing treatments, and identify the most clinically and cost-effective by a process known as health technology assessment (HTA). For progressive diseases, patients experience many different stages to their health, which affect quality of life and resource requirements, which needs to be considered in HTA. For instance, a patient with Duchenne Muscular Dystrophy (DMD), a rare muscle-degenerating disease, progressively loses the ability to walk, requiring a wheelchair, and then are unable move their fingers, requiring assistance operating their wheelchair. When assessing new treatments, researchers consider the multiple outcomes that patients can experience. This impacts both the clinical and economic aspects of HTA. A statistical/mathematical model is built to represent how a typical patient might move through the different stages of the disease. The effect of the new and existing treatments are expressed in terms of improvement to this typical patient, such as a delay in disease severity. Economic analysis then considers the costs associated with these treatments to determine whether the new treatment costs are justifiable. Over 3.5 million people in the UK will experience a rare disease such as DMD in their lifetime. Despite this, rare diseases are unfortunately under-researched compared to more common diseases. This means there may not be enough data from any one study to build a typical patient mathematical model. As a result of this, data from many different sources are required. However, this poses difficulties such as how to account for any differences between the sources of data, and how to compare different types of data. Aims The aims of my project are: To investigate the methods for combining different sources and types of data into a mathematical model of a disease. To develop and extend these methods to models of rare diseases like DMD, when there is typically less data and of a lower quality. To test the accuracy of methods developed using simulated/hypothetical and actual DMD data. To improve advice to researchers on future HTA of new treatments for rare diseases.Methods In order to address the aims, I will apply the following methods: I will search the literature for previously published methods on constructing mathematical models of disease progression. I will then extend the search to methods of model construction from diverse data sources, and evaluate how dependent these methods are on the amount and quality of data available. I will make adjustments to methods that do not seem to be well-suited to modelling rare diseases. I will test these adjustments by simulating data representing the hypothetical disease progression of a patient. I will also apply the methods to real-life data on patients with DMD. I will compare the accuracy of the different methods that I develop, and how they impact the results of economic decision models and therefore decisions by organisations such as NICE. I will develop recommendations on the methods that researchers can use when faced with similar issues due to a lack of data, and propose guidelines for future data collection.Patient and Public Involvement My previous work, and the longstanding collaboration of my research group, with Duchenne UK have benefitted the project with support from multiple stakeholders. This provides a unique opportunity for sharing research findings with patient, public and research networks. I will extend the reach of the project with a workshop at the end of the fellowship, to which I will invite a wide variety of people interested in rare diseases.

Research activity codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: MUSCULOSKELETAL