Wu Xiangrui, Yang Xianmei, Fan Ruoxin, Liu Jun, Xiang Hu, Zuo Chuanlong, Liu Xiang, Liu Yuanyuan
/ （ 610041） Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China.
Sichuan Da Xue Xue Bao Yi Xue Ban. 2024 Jul 20;55(4):918-924. doi: 10.12182/20240760504.
To construct a model for predicting recidivism in violence in community-based schizophrenia spectrum disorder patients (SSDP) by adopting a joint modeling method.
Based on the basic data on severe mental illness in Southwest China between January 2017 and June 2018, 4565 community-based SSDP with baseline violent behaviors were selected as the research subjects. We used a growth mixture model (GMM) to identify patterns of medication adherence and social functioning. We then fitted the joint model using a zero-inflated negative binomial regression model and compared it with traditional static models. Finally, we used a 10-fold training-test cross validation framework to evaluate the models' fitting and predictive performance.
A total of 157 patients (3.44%) experienced recidivism in violence. Medication compliance and social functioning were fitted into four patterns. In the counting model, age, marital status, educational attainment, economic status, historical types of violence, and medication compliance patterns were predictive factors for the frequency of recidivism of violence (<0.05). In the zero-inflated model, age, adverse drug reactions, historical types of violence, medication compliance patterns, and social functioning patterns were predictive factors for the recidivism in violence (<0.05). For the joint model, the average value of Akaike information criterion (AIC) for the train set was 776.5±9.4, the average value of root mean squared error (RMSE) for the testing set was 0.168±0.013, and the average value of mean absolute error (MAE) for the testing set was 0.131±0.018, which were all lower than those of the traditional static models.
Joint modeling is an effective statistical strategy for identifying and processing dynamic variables, exhibiting better predictive performance than that of the traditional static models. It can provide new ideas for promoting the construction of comprehensive intervention systems.
采用联合建模方法构建社区精神分裂症谱系障碍患者（SSDP）暴力再犯预测模型。
基于2017年1月至2018年6月中国西南地区严重精神疾病的基础数据，选取4565例有基线暴力行为的社区SSDP作为研究对象。我们使用生长混合模型（GMM）来识别药物依从性和社会功能模式。然后使用零膨胀负二项回归模型拟合联合模型，并将其与传统静态模型进行比较。最后，我们使用10倍训练-测试交叉验证框架来评估模型的拟合和预测性能。
共有157例患者（3.44%）出现暴力再犯。药物依从性和社会功能被拟合为四种模式。在计数模型中，年龄、婚姻状况、教育程度、经济状况、暴力历史类型和药物依从性模式是暴力再犯频率的预测因素（<0.05）。在零膨胀模型中，年龄、药物不良反应、暴力历史类型、药物依从性模式和社会功能模式是暴力再犯的预测因素（<0.05）。对于联合模型，训练集的赤池信息准则（AIC）平均值为776.5±9.4，测试集的均方根误差（RMSE）平均值为0.168±0.013，测试集的平均绝对误差（MAE）平均值为0.131±0.018，均低于传统静态模型。
联合建模是识别和处理动态变量的有效统计策略，其预测性能优于传统静态模型。它可为促进综合干预系统的构建提供新思路。
