The Impact of Dependency and Serial Correlation in Matched ITSA: Methodological Considerations

Interrupted Time Series Analysis (ITSA) is a widely used quasi-experimental method for evaluating the longitudinal impact of interventions. This study explores the methodological considerations of Matched Controlled Interrupted Time Series (MCITS) designs, particularly the effects of serial correlation and dependency between matched cases and controls on type I error and bias.Using a Monte Carlo simulation approach, we assess the accuracy, type I error and bias, of parameter estimates and compare single and joint segmented regression models.Results indicate that type I error is underestimated in separate models when intra-cluster correlation (ICC) is nonzero and serial correlation is negative, whereas joint modeling maintains appropriate error rates. Bias estimates remain minimal across scenarios, reinforcing the robustness of the joint model for analyzing intervention effects in matched controlled ITSA designs.These findings provide important insights for researchers conducting longitudinal evaluations of interventions in fields such as healthcare and public policy.