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.

Background Venous thromboembolism (VTE) is a hospital‐associated severe complication that may adversely affect patient prognosis. In this study, we evaluated the incidence of VTE and its risk factors in patients with epithelial ovarian cancer (EOC). Methods We retrospectively analyzed the electronic health record data of 1268 patients with EOC who received primary treatment at the National Cancer Center, Korea between January 2007 and December 2017 to identify patients who developed VTE. Demographic, clinical, and surgical characteristics of these patients were ascertained. Competing risks analyses were performed to estimate the cumulative incidence of VTE according to the treatment type. The associations between putative risk factors and the incidence of VTE were evaluated using the Fine–Gray regression models accounting for competing risks of death. Results VTE was the most prevalent cardiovascular event, found in 9.6% (n = 122) of all patients. Of these VTE events, 115 (94.3%) occurred within 2 years of EOC diagnosis. Advanced cancer stage at diagnosis (distant vs. localized, hazards ratio [HR])= 14.49, p = 0.015) and extended hospital stay (≥15 days, HR =3.87, p = 0.004) were associated with the incidence of VTE. There was no significant difference in the cumulative incidence of VTE between primary cytoreductive surgery followed by adjuvant chemotherapy and neoadjuvant chemotherapy followed by interval cytoreductive surgery (HR =0.81, p = 0.390). Conclusions Approximately 10% of patients with EOC were diagnosed with VTE, which was the most common cardiovascular disease found in this study. The assessment of VTE risks in patients with advanced‐stage EOC with an extended hospital stay is needed to facilitate adequate prophylactic treatment.