# Advantage of using interrupted time series over t-test/ANOVA

I would like to conduct a study to look at the effect COVID-19 pandemic lockdown has on the following:

• number of cases at the hospital emergency department (broken down into the different triages and different complaints/diagnoses
• waiting time to consultation
• returns to hospital emergency department within 24 hours (percentage data)
• subsequent admission to general ward or ICU

The pre-period (i.e. before lockdown) and the post-period (i.e. after implementation of lockdown) are not consecutive. We are looking at the same months both in pre- and post-periods, each having a total of 60 days.

Would it be okay if I use t-test to compare pre- and post-period data? But that would mean aggregating the data into 1 data point pre and 1 data point for post. Would we lose resolution here?

Or should I do an interrupted time series, aggregating by day?

• If you were to use a t test, what would $n$ be? Are you looking at various locations? Countries, cities, US states? – BruceET Oct 9 '20 at 5:22