beforeafteranalyses

Before-after analyses, also known as pre-post analyses, are study designs that assess the effect of an intervention by comparing outcomes measured before the intervention to outcomes measured after, in the same units or subjects. This approach treats each unit as its own control, aiming to isolate changes attributable to the intervention from inherent differences between units.

Common methods include simple pre-post comparisons of means or proportions, paired statistical tests such as the paired t-test or the Wilcoxon signed-rank test for continuous data, and McNemar’s test for binary outcomes. When data are analyzed at the individual level, regression techniques can model change scores or incorporate covariates, and mixed-effects models may account for repeated measurements.

Before-after analyses are often used in program evaluation, clinical settings, and quality improvement to obtain a quick, cost-effective assessment of impact. They are particularly appealing when randomized controlled trials are not feasible or when units cannot be randomized.

However, these analyses have notable limitations. They are vulnerable to time trends, seasonal effects, and external events that occur concurrently with the intervention, making causal attribution challenging. Regression to the mean, measurement changes, and regression biases can also distort results, especially with small samples or extreme baseline values. A lack of simultaneous control groups further weakens causal inference.

Best practices include collecting multiple pre- and post-intervention measurements, using a control or comparison group when possible, pre-specifying analysis plans, and considering more robust designs such as interrupted time series or difference-in-differences when feasible.