Fitting joinpoint models for descriptive analysis of cancer trends in Stata

Investigation of temporal trends of cancer incidence and mortality rates is often performed visually with interest in changes in the gradient of increases or decreases in the rates. Joinpoint models are used to help quantify the trends, using linear splines where both the number and location of the knots (joinpoints) are selected as part of the modeling process. I will describe a Stata implementation of joinpoint models and introduce the joinpoint command and associated postestimation commands. The approach can be computer intensive as all possible combinations of the number and location of knots are fitted when selecting the models. I will describe how use of Mata to fit the models leads to dramatic speed improvements. The joinpoint command has various options, for example choosing different model selection criterion and choosing the maximum number of knots and the minimum number of data points between knots. Output options include estimation of the annual percent change (APC), with two different methods to calculate confidence intervals. There is a postestimation predict command and a command to provide visual summaries of the fitted model.