The joinpoint regression model (JRM) is used to describe trend changes in many applications and relies on the detection of joinpoints (changepoints). However, the existing joinpoint detection methods, namely, the grid search (GS)‐based methods, are computationally demanding, and hence, the maximum number of computable joinpoints is limited. Herein, we developed a genetic algorithm‐based joinpoint (GAJP) model in which an explicitly decoupled computing procedure for optimization and regression is used to embed a binary genetic algorithm into the JRM for optimal joinpoint detection. The combinations of joinpoints were represented as binary chromosomes, and genetic operations were performed to determine the optimum solution by minimizing the fitness function, the Bayesian information criterion (BIC) and BIC₃. The accuracy and computational performance of the GAJP model were evaluated via intensive simulation studies and compared with those of the GS‐based methods using BIC, BIC₃, and permutation test. The proposed method showed an outstanding computational efficiency in detecting multiple joinpoints. Finally, the suitability of the GAJP model for the analysis of cancer incidence trends was demonstrated by applying this model to data on the incidence of colorectal cancer in the United States from 1975 to 2016 from the National Cancer Institute's Surveillance, Epidemiology, and End Results program. Thus, the GAJP model was concluded to be practically feasible to detect multiple joinpoints up to the number of grids without requirement to preassign the number of joinpoints and be easily extendable to cancer trend analysis utilizing large datasets.

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最优连接点检测的二进制遗传算法：在癌症趋势分析中的应用

连接点回归模型（JRM）用于描述许多应用程序中的趋势变化，并依赖于检测连接点（变更点）。但是，现有的连接点检测方法（即基于网格搜索（GS）的方法）在计算上要求很高，因此可计算的连接点的最大数量受到限制。本文中，我们开发了一种基于遗传算法的连接点（GAJP）模型，其中使用了显式解耦的优化和回归计算程序将二进制遗传算法嵌入到JRM中，以实现最佳连接点检测。连接点的组合表示为二元染色体，并通过最小化适应度函数，贝叶斯信息准则（BIC）和BIC ₃进行了遗传运算以确定最佳解。通过深入的模拟研究评估了GAJP模型的准确性和计算性能，并与使用BIC，BIC _3的基于GS的方法进行了比较以及排列测试。所提出的方法在检测多个连接点方面表现出出色的计算效率。最后，通过将该模型应用于美国国家癌症研究所的监测，流行病学和最终结果计划从1975年至2016年在美国结直肠癌的发病率数据中，证明了GAJP模型对于分析癌症发病率趋势的适用性。 。因此，得出结论，GAJP模型在检测多达网格数量的多个连接点上是实际可行的，而无需预先分配连接点的数量，并且可以轻松扩展到使用大型数据集的癌症趋势分析。