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The key idea is how each method handles time and what it aims to measure. Regression models predict an outcome by relating it to one or more covariates, showing associations between predictors (which can include patient characteristics or other factors) and the outcome. Time-series analysis, on the other hand, centers on how outcomes change over time, paying attention to patterns before and after an intervention and whether there is a shift in level or trend after the intervention.

This distinction matters when evaluating an intervention’s impact. Regression can adjust for differences across patients or settings to estimate associations, but it doesn’t inherently focus on the sequence of time points or the immediate change following an intervention. Time-series analysis is designed to detect changes in the trajectory of outcomes across time, making it well-suited to assess whether there is an abrupt level change or a change in slope after the intervention, while accounting for the underlying time trend.

An example helps: you might run a regression to see how outcomes relate to patient age, comorbidities, and treatment group, providing an adjusted estimate of the intervention’s effect. A time-series approach would plot outcomes over months or weeks, examine the pre-intervention trend, and determine whether there is a noticeable shift once the intervention is implemented.

That combination of focusing on time-based changes versus covariate-adjusted prediction is why the statement contrasting regression as covariate-based prediction with time-series as pre/post time changes best captures the distinction.