Case Control

Case-control studies are a powerful epidemiological design used to investigate the relationship between exposures and outcomes, particularly when the outcome is rare. Current research focuses on improving the efficiency and accuracy of statistical models used to analyze case-control data, including the development of semi-supervised learning methods that leverage both labeled and unlabeled data and the application of deep learning architectures like neural networks. These advancements are crucial for improving the reliability of risk prediction models in various fields, such as healthcare, where they enable large-scale screening for conditions like severe mental illnesses and enhance the precision of diagnostic tools. The ability to accurately estimate parameters, even with imbalanced datasets, is a key focus of ongoing work.