Natural Experiments by Interrupted Time Series Analysis for the NHS

This project explores how a statistical method called interrupted time series analysis (ITSA) can be simplified for better use in healthcare research. ITSA helps to measure the impact of interventions, like new drug policies, by comparing data before and after the intervention. The team, is particularly focused on the challenge of autocorrelation in health data, which can make analyses complex. Autocorrelation happens when past data points influence future ones, a common issue in economic data but less so in healthcare. By reviewing numerous studies that used ITSA with UK health datasets, the research aims to find ways to reduce complexity in these analyses. Simplifying ITSA could make health research more accessible and impactful, leading to clearer insights into how healthcare interventions work. This could ultimately improve patient care and policy decisions in the health sector.