thresholdanalysis: An R Package for Detecting and Visualizing Threshold Effects in Logistic Regression, Linear Regression, and Cox Proportional Hazards Models

When a dependent variable exceeds a certain threshold, a significant change in its magnitude and/or direction is called a threshold effect. As a statistical tool, threshold effect analysis can predict the occurrence, development, and prognosis of a disease, which is highly important. However, there is currently a lack of open-source, free, simple, and effective tools to implement threshold effect analysis. The process of determining thresholds is complex and labor intensive, requiring high programming skills. Therefore, this study demonstrates how to simplify this process and reduce programming skill requirements via the self-developed R package "thresholdanalysis", which enables nonstatistical professionals to conduct threshold effect analysis. First, smooth curve fitting is used to observe whether there is a nonlinear relationship between the dependent variable and the independent variable. If a threshold is visually observed, the maximum likelihood method, recursive method, piecewise linear model, and likelihood ratio test are used to determine the threshold, the effect sizes before and after the threshold, and whether the effect sizes of the two segments are significant.