Evaluation of Methods for Joinpoint Analysis of Time Series Using Simulated and Real-World Data

This study compares two methods for analyzing changes in trends over time. The two tools examined are the Joinpoint Regression Program (JRP) and the R "segmented" package. Using both simulated data and real-world hospital data, we assessed the performance of these tools. The simulated data included two scenarios: one without residual autocorrelation (simpler) and another with residual autocorrelation (where data points are related to previous ones, often seen in health care data). In the simpler scenario, the R package outperformed JRP by being more accurate in detecting changes and avoiding false detections. It also provided more precise estimates, with smaller uncertainty around the change points. However, when dealing with the more complex scenario involving residual autocorrelation, the JRP performed better, especially when changes occurred later in the timeline. However, early trend changes in the time series were challenging to detect for both JRP and R software. When analyzing real-world data on children's mental health hospitalizations, the JRP tended to identify more changes in trends than the R package. However, both softwares produced broadly similar results for the overall trends. The JRP is user-friendly and works well with data that include complex patterns like residual autocorrelation, but it does not account for seasonal effects and may detect more changes than are truly present. The R package, on the other hand, offers greater precision and flexibility for simpler data but requires more advanced programming skills and does not perform as well when handling data with residual autocorrelation. In summary, the choice between these tools should be driven by the type of data being analyzed: the R package is better for straightforward data, while the JRP is more suited for complex datasets such as those found in health care.