Aims:
The primary aim of this research project is to develop a semiparametric change point model to understand better the U-shaped relationship between biomarkers, such as BMI, and cardiovascular disease risk. By leveraging this model, we aim to identify critical points where the risk is lowest, enhancing our ability to predict and prevent heart-related conditions.
Scientific Rationale:
Cardiovascular diseases are among the leading causes of death globally. Research has shown that both extremely high and low levels of certain biomarkers can increase the risk of cardiovascular events, forming U-shaped risk curves. Traditional methods struggle to accurately model these complex relationships, especially for survival outcomes. Our proposed semiparametric change point model will address these challenges by providing more accurate risk predictions and helping to define optimal biomarker levels.
Project Duration:
This project is expected to take three years. During this period, we will develop the model, validate it using data from the UK Biobank, and test its effectiveness with real-world clinical trial data.
Public Health Impact:
This research has significant potential to improve public health by offering a robust tool for modeling U-shaped risk relationships between biomarkers and cardiovascular outcomes. By identifying optimal levels of biomarkers to minimize cardiovascular risk, we can help doctors classify patients into different risk categories more accurately. This means high-risk individuals can receive targeted and effective preventive care and treatment, ultimately reducing the incidence of heart attacks, strokes, and other cardiovascular events. This approach addresses a critical public health need and has the potential to make a meaningful difference in the fight against cardiovascular disease.
Additionally, the novel statistical methods developed through this project will benefit other researchers and healthcare professionals. These methods can be applied to various areas of medical research, potentially leading to further advancements in disease prevention and management. Leveraging the extensive data from the UK Biobank, which includes detailed information on biomarkers and cardiovascular outcomes, will ensure that our model is accurate and broadly applicable.
This research, by providing better risk prediction and stratification, will ultimately contribute to enhanced clinical decision-making and improved patient outcomes, addressing a significant public interest by potentially improving the health and longevity of millions of individuals globally.