Projects
CHADS is involved in a range of ongoing projects, some of which are described briefly below. They overlap in methodology and applications.
BiomarCaRE
The Biomarker for Cardiovascular Risk Assessment across Europe (BiomarCaRE) consortium is an EU-funded consortium including over 30 partner from academia and industry. BiomarCaRE aims to determine the value of established and emerging biomarkers to improve risk estimation of cardiovascular disease in Europe.
It has been shown that biomarkers such as C-reactive protein have the potential ability to predict who is at risk for a cardiovascular event. Decision-analytic models can be used to assess whether different prevention strategies are not only effective but cost-effective. Within decision-analytic models, Markov models have often been used to quantify the movements of individuals between different health states over time, where movements can be influenced by characteristics of the individuals as well as the prevention strategies being applied.
Combinations of biomarkers such as C-reactive protein, NT-pro BNP, and Troponin I can be used to estimate 10 year risk prediction of cardiovascular death, and combined with cost and utility information to create a cost-effectiveness model.
Cancer screening and COVID-19
Cancer screening is a critical component of our armamentarium against cancer, facilitating identification of citizens at risk of developing the disease at the earliest stage, thus informing effective health management of the newly identified patient when cancer treatment may be more effective, avoiding significant ill health and in some cases premature death.
Striving for optimal health resource utilisation is particularly relevant in the context of the coronavirus pandemic, with COVID-19-repurposing of our health service leading to unintended de-prioritisation of non-COVID-related healthcare activities, including cancer screening.
Developing robust and resilient COVID-era cancer screening programme configurations requires new, more precise data-enabled modelling approaches, which will have relevance locally, nationally and globally.
Colorectal Cancer Detection
Colorectal cancer tumours which are early stage (T1) can be investigated to establish if there are molecular differences between those that have and have not spread to lymph nodes.
Computational tools can be use to understand the differences and to identify the biologies associated with the spread to lymph nodes and cancer recurrence thereby addressing the clinical need to identify high risk T1 tumours that require chemotherapy in addition to surgery from T1 tumours that can be treated safely with surgery alone.
Multiple Myeloma
Multiple myeloma is a non curable rare disease which culminates in bone destruction and renal failure.
Approximately half of patients diagnosed with multiple myeloma live longer than 5 years, resulting in a
continued unmet need for novel therapies for patients with refractory or relapsed multiple myeloma.
Clinical studies identify amongst others signalling pathways and transcriptional regulators that are required for a B cell to develop into an immunoglobulin producing plasma cell, and these have become targets for many novel treatments in multiple myeloma. As these novel agents emerge from Phase II and Phase III trials there is limited data on long-term survival and health care costs. Policy makers find it extremely difficult to get accurate cost effectiveness results as real world evidence often does not correlate from clinical trials predominantly because of the heterogenous nature of the patient in real world outcomes.
The difficult question is if these novel agents are worth their price, or are they offering false hope to broader patient populations who often receive these treatments in return for a very poor quality of life.
Cost-effectiveness modelling in R
A cost-effectiveness analysis is a type of Health Economic Evaluation that compares one intervention to one or more alternatives by estimating how much it costs to gain an additional unit of health outcome. The go-to software for cost-effectiveness analyses has usually been Microsoft Excel.
R is a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques.
Ongoing work from the team involves encouraging the use of R for Health Economic Evaluations. This includes delivering short-courses, lectures, seminars and tutorial publications on the topic.