Joinpoint regression involves fitting a segmented regression model to the data. The process includes the following steps:
1. Model Selection: The number of joinpoints is selected based on statistical criteria such as the Bayesian Information Criterion (BIC). 2. Fitting the Model: The model is fitted to the data using an iterative process to minimize the sum of squared errors. 3. Identifying Joinpoints: The points where the trend changes significantly are identified as joinpoints.
