Rationale and performances of a data-driven method for computing the duration of pharmacological prescriptions using secondary data sources

The assessment of the duration of pharmacological prescriptions is an important phase in pharmacoepidemiologic studies aiming to investigate persistence, effectiveness or safety of treatments. The Sessa Empirical Estimator (SEE) is a new data-driven method which uses k-means algorithm for computing the duration of pharmacological prescriptions in secondary data sources when this information is missing or incomplete. The SEE was used to compute durations of exposure to pharmacological treatments where simulated and real-world data were used to assess its properties comparing the exposure status extrapolated with the method with the true exposure status available in the simulated and real-world data. Finally, the SEE was also compared to a Researcher-Defined Duration (RDD) method. When using simulated data, the SEE showed accuracy of 96% and sensitivity of 96%, while when using real-world data, the method showed sensitivity ranging from 78.0 (nortriptyline) to 95.1% (propafenone). When compared to the RDD, the method had a lower median sensitivity of 2.29% (interquartile range 1.21-4.11%). The SEE showed good properties and may represent a promising tool to assess exposure status when information on treatment duration is not available.

Automated summary

The assessment of the duration of pharmacological prescriptions is crucial in pharmacoepidemiologic studies, and the Sessa Empirical Estimator (SEE) is a promising new method for computing prescription duration. The SEE showed good accuracy and sensitivity when compared to simulated and real-world data and outperformed the Researcher-Defined Duration (RDD) method.