Twenty years since Joinpoint 1.0: Two major enhancements, their justification, and impact

Area of Science:

• Biostatistics
• Cancer Epidemiology
• Health Informatics

Background:

• Joinpoint software, developed by the US National Cancer Institute, is widely used for cancer trend analysis.
• Initial versions (1.0) utilized permutation tests for joinpoint selection and parametric methods for inference.
• Previous methods had limitations in accuracy and computational efficiency.

Purpose of the Study:

• To review Joinpoint software's basic features and summarize updates since 1998.
• To detail two major enhancements improving accuracy and computational efficiency.
• To demonstrate the impact of these enhancements on US National Cancer Institute cancer trend analyses.

Main Methods:

• Review of Joinpoint software features and historical updates.
• Description of new data-driven model selection methods.
• Explanation of the empirical quantile method for confidence interval construction.

Main Results:

• New data-driven methods are more accurate and computationally efficient than permutation tests.
• Empirical quantile method provides more accurate confidence interval coverage for slope parameters and joinpoint locations.
• These enhancements address prior limitations in Joinpoint software.

Conclusions:

• The updated Joinpoint software offers significant improvements in cancer trend analysis.
• Enhanced accuracy and efficiency benefit cancer surveillance and epidemiological studies.
• The software's advancements support more reliable cancer trend reporting and interpretation.