Estimation of Risk Factors Affecting Screening Outcomes of Prostate Cancer Using the Bayesian Ordinal Logistic Model

IF 1 Q3 STATISTICS & PROBABILITY
J. Sirengo, D. Alilah, D. Mbete, R. Keli
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引用次数: 0

Abstract

Prostate cancer occurs when cells in the prostate gland grow out of control. Almost all prostate cancers are adenocarcinomas. The survival rate for prostate cancer patients depends on the screening outcome, which can be either no prostate cancer, early detection, and late detection or advanced stage detection. The main objective of this study was to estimate the risk factors affecting the screening outcome of prostate cancer. With ordinal outcomes, a generalized Bayesian ordinal logistic model was considered in the analysis. The generalized Bayesian ordinal logistic model helped in estimation of coefficient parameters of the risk factors affecting each level of prostate cancer-screening outcomes. In the study, positive coefficients, that is, β k > 0 , indicated that the higher values on the explanatory variable increased the chances of the respondent being in a higher category of the dependent variable than the current one, while the negative coefficients, that is, β k < 0 , signified that the higher values on the explanatory variable increased the likelihood of being in the current or lower category of prostate cancer. For instance, from the analysis, positive or negative outcomes of prostate cancer showed that an increase in weight lowered the chances of an individual having the disease.
应用贝叶斯有序逻辑模型估计影响癌症筛查结果的危险因素
当前列腺中的细胞生长失控时,就会发生前列腺癌症。几乎所有的前列腺癌都是腺癌。前列腺癌症患者的存活率取决于筛查结果,筛查结果可以是无前列腺癌症、早期检测、晚期检测或晚期检测。本研究的主要目的是评估影响癌症筛查结果的危险因素。在有序结果的情况下,分析中考虑了广义贝叶斯有序逻辑模型。广义贝叶斯有序逻辑模型有助于估计影响各级前列腺癌筛查结果的危险因素的系数参数。在研究中,正系数,即βk>0,表明解释变量上的较高值增加了被调查者处于比当前因变量更高类别的机会,而负系数,即,βk<0,表明解释变量的较高值增加了处于当前或较低类别的前列腺癌症的可能性。例如,从分析来看,前列腺癌症的阳性或阴性结果表明,体重增加会降低个体患该疾病的几率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Probability and Statistics
Journal of Probability and Statistics STATISTICS & PROBABILITY-
自引率
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发文量
14
审稿时长
18 weeks
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