Key result
Weighted segmented regression for interrupted time series yields more precise, less biased estimates than traditional methods.
Why the study?
Segmented regression for interrupted time series designs is prone to aggregation bias, leading to imprecision and loss of power to detect clinically meaningful differences.
A proposed weighted segmented regression method for interrupted time series data improves precision and statistical power by accounting for patient heterogeneity.
May enhance precision of ITS analyses in observational cardiovascular data; leaves open external validation before routine use.
Interrupted time series (ITS) design is commonly used to evaluate the impact of interventions in healthcare settings. Segmented regression (SR) is the most commonly used statistical method and has been shown to be useful in practical applications involving ITS designs. Nevertheless, SR is prone to aggregation bias, which leads to imprecision and loss of power to detect clinically meaningful differences. The objective of this article is to present a weighted SR method, where variability across patients within the healthcare facility and across time points is incorporated through weights. We present the methodological framework, provide optimal weights associated with data at each time point and discuss relevant statistical inference. We conduct extensive simulations to evaluate performance of our method and provide comparative analysis with the traditional SR using established performance criteria such as bias, mean square error and statistical power. Illustrations using real data is also provided. In most simulation scenarios considered, the weighted SR method produced estimators that are uniformly more precise and relatively less biased compared to the traditional SR. The weighted approach also associated with higher statistical power in the scenarios considered. The performance difference is much larger for data with high variability across patients within healthcare facilities. The weighted method proposed here allows us to account for the heterogeneity in the patient population, leading to increased accuracy and power across all scenarios. We recommend researchers to carefully design their studies and determine their sample size by incorporating heterogeneity in the patient population.
No takes yet. Share an insight, caveat, or question.
Ewusie et al. (2021) studied this question. Weighted segmented regression method vs. Traditional segmented regression was evaluated on Bias, mean square error, and statistical power in simulations. A proposed weighted segmented regression method for interrupted time series data produced estimators that were uniformly more precise, less biased, and had higher statistical power than traditional methods.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: