Enhanced statistical rigour in health data research  

It is important for research studies to be able to achieve repeatable results. Many research studies are unable do this, which raises concerns about the reliability of scientific findings and the subsequent wasted human and financial resources. Key contributing factors include the explosion in the amount of data available to researchers, inadequate statistical training, and a lack of statistical methods and software to produce reliable results. 

I will develop user-friendly methods and software, with accompanying guidelines so non-specialist data analysts can assess the sensitivity of their results to four key sources of bias in health research. While some data analysts provide a qualitative discussion of the effect of such biases on their results, few provide a quantitative assessment, leading to over-confidence in their scientific findings.  

My proposed research will make a major contribution to addressing the problem of reproducibility, leading to improved public confidence in health science.