Theophilus G. Baidoo

I am a passionate researcher specializing in advanced statistical methodologies, committed to advancing knowledge and addressing complex health-related challenges through innovative approaches in biostatistics. My research interests encompass several key areas, including stochastic orderings in survival analysis, where I develop and implement statistical methods to analyze time-to-event data, providing crucial insights into outcomes and risk factors. My work also includes causal inference, where I explore relationships and uncover cause-effect patterns in data to support evidence-based decision-making. I apply machine learning algorithms to extract insights and predict health outcomes, contributing to a deeper understanding of public health trends and risks. In the field of time series analysis, I focus on modeling and forecasting temporal data to generate reliable predictions and inform practical applications. By integrating these diverse areas of research, I aim to make meaningful contributions to health research and the broader field of biostatistics.