Ali Mohammadpour Reza, Alizadeh Ahad, Barzegar Mohammad-Reza, Akbarzadeh Pasha Abazar
Department of Biostatistics, Faculty of Health, Health Sciences Research Center, Mazandaran University of Medical Sciences, Sari, Iran.
Student Research Committee, Faculty of Health, Mazandaran University of Medical Sciences, Sari, Iran.
Caspian J Intern Med. 2020 May;11(3):324-328. doi: 10.22088/cjim.11.3.324.
Prostate specific antigen (PSA) is an important biomarker to monitor patients after treated with radiation therapy (RT). The aim of this study is to evaluate the relationship between the PSA data and prostate cancer recurrence using the joint modeling.
This historical cohort study was performed on 422 prostate cancer patients. Inclusion criteria included: patients with localized prostate cancer referring to Cancer Institute in Tehran (Iran) from 2007 to 2012, and under radiation therapy. Joint model has two components or sub-models. We showed the results by parameter estimating the longitudinal sub-model and survival sub-model. EM algorithm, Newton-Gauss and Gauss-Hermit law were used for final model parameters. R software version 3.2 was used for statistical analysis.
In this study, considering the inclusion and exclusion criteria, out of 422 patients, the data on 314 cases were selected for analysis and the main result of joint model was obtained. PSA directly and significantly was associated with recurrence risk, therefore increasing 2.6 ml/lit PSA (one unit in transformed PSA) increases 39% recurrence risk (95% CI for RR: 1.09-1.77). Also, slope of PSA trend has significant association with prostate cancer recurrence risk (95% CI for RR: 1.05-1.41).
This study showed a significant relationship between PSA, and its slope with the recurrence risk by joint model, with regard to the pathological, demographic and clinical features in the Iranian population.
前列腺特异性抗原(PSA)是监测放射治疗(RT)后患者的重要生物标志物。本研究旨在使用联合模型评估PSA数据与前列腺癌复发之间的关系。
对422例前列腺癌患者进行了这项历史性队列研究。纳入标准包括:2007年至2012年转诊至德黑兰(伊朗)癌症研究所且接受放射治疗的局限性前列腺癌患者。联合模型有两个组成部分或子模型。我们通过估计纵向子模型和生存子模型的参数来展示结果。使用EM算法、牛顿 - 高斯法和高斯 - 埃尔米特法则来确定最终模型参数。使用R软件版本3.2进行统计分析。
在本研究中,根据纳入和排除标准,从422例患者中选取314例患者的数据进行分析,并获得了联合模型的主要结果。PSA与复发风险直接且显著相关,因此PSA每增加2.6毫升/升(转换后的PSA增加一个单位),复发风险增加39%(RR的95%CI:1.09 - 1.77)。此外,PSA趋势的斜率与前列腺癌复发风险显著相关(RR的95%CI:1.05 - 1.41)。
本研究表明,就伊朗人群的病理、人口统计学和临床特征而言,联合模型显示PSA及其斜率与复发风险之间存在显著关系。