A new statistical technique offers a better way to gauge the effectiveness of complex healthcare interventions.
The method, developed by KAUST biostatistician Hernando Ombao and his colleagues Maricela Cruz and Miriam Bender from the University of California, allows health policy researchers to determine if and when a particular intervention has led to a changed specific outcome.
It is based on the interrupted time series (ITS) model, which has been used to study the health benefits of smoking bans, changes in drug packaging and other public-health initiatives. But the ITS model has had a major limitation: it required researchers to specify exactly when the intervention had an effect rather than estimating that time point from the data.
