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

Abstract

Since its release of Version 1.0 in 1998, Joinpoint software developed for cancer trend analysis by a team at the US National Cancer Institute has received a considerable attention in the trend analysis community and it became one of most widely used software for trend analysis. The paper published in Statistics in Medicine in 2000 (a previous study) describes the permutation test procedure to select the number of joinpoints, and Joinpoint Version 1.0 implemented the permutation procedure as the default model selection method and employed parametric methods for the asymptotic inference of the model parameters. Since then, various updates and extensions have been made in Joinpoint software. In this paper, we review basic features of Joinpoint, summarize important updates of Joinpoint software since its first release in 1998, and provide more information on two major enhancements. More specifically, these enhancements overcome prior limitations in both the accuracy and computational efficiency of previously used methods. The enhancements include: (i) data driven model selection methods which are generally more accurate under a broad range of data settings and more computationally efficient than the permutation test and (ii) the use of the empirical quantile method for construction of confidence intervals for the slope parameters and the location of the joinpoints, which generally provides more accurate coverage than the prior parametric methods used. We show the impact of these changes in cancer trend analysis published by the US National Cancer Institute.

Trends of nonoccupational postexposure prophylaxis in the United States

“…To calculate these rates, we determined the number of individuals in each subgroup who were at least 13 years of age. We used Joinpoint Regression Software version 4.9.1.0 (Surveillance Research Program, National Cancer Institute) to identify inflection points in nPEP trends over the study period [42,43]. The software was used to fit the best linear regression model with a maximum of two inflection points to identify periods with statistically different changes in the rate of nPEP prescribing.…”

Differentials in deaths of despair in Brazilian subpopulations: a joinpoint regression analysis from 2000 to 2020

“…One of the advantages of this method is being able to identify the number and location of changes in the trend and estimate the annual percentage change (Annual Percentage Change -APC) for each period between in ection points [21,22].…”

“…We obtained the number of in ection points through a permutation test through a Monte Carlo resampling. Once we have de ned the number k of joining points, we compare the different models with k joining points by estimating their Bayesian Information Criterion (BIC) [22].…”

Trends in the incidence of adrenocortical carcinoma and pheochromocytoma/paraganglioma in Taiwan

“…[8] Starting with Joinpoint version 5.0 (released April 19, 2023), the empirical quantile method replaced the parametric method as the default for calculating the confidence intervals, and the weighted Bayesian information criterion replaced the permutation test. [9] In the present study, Joinpoint version 5.0.1 was used to calculate annual percent change (APC) when there was no joinpoint or average annual percent change (AAPC) when one or more joinpoints were identified. Confidence intervals (CIs) were calculated using the empirical quantile method, which generally provides more accurate coverage than the prior parametric methods.…”