癌症疾病数据高级建模的新统计模型

IF 1.2 4区 综合性期刊 Q3 MULTIDISCIPLINARY SCIENCES
Ahmad Abubakar Suleiman , Hanita Daud , Aliyu Ismail Ishaq , Abbas Umar Farouk , Aminu Suleiman Mohammed , Mohamed Kayid , Vasili B.V. Nagarjuna , Shahid Mohammad , Mohammed Elgarhy
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引用次数: 0

摘要

经典概率模型往往难以捕捉生物医学数据,特别是生命周期数据中固有的可变性和复杂模式。为了解决这些限制,引入了广义奇素数广义(GOBP-G)类分布,以及称为广义奇素数-威布尔(GOBPW)分布的扩展。这种新模型提供了更强的灵活性,可以表示各种数据特征,从对称分布到倾斜分布,以及不同的风险率模式,如增加、浴缸和减少趋势。这些特点使GOBPW模型适用于生物医学和工程应用中的统计分析。本文推导了GOBPW分布的关键性质,包括矩、矩生成函数、熵测度、应力-强度函数、分位数函数和序统计量。建立了累积函数和概率密度函数,为模型提供了基础结构。采用多种估计方法来评估参数估计的准确性和可靠性。蒙特卡罗模拟进一步验证了模型在各种条件下的鲁棒性。通过对三个数据集的应用,证明了GOBPW模型的实用性:132例膀胱癌患者的缓解时间(CD1), 73例急性骨癌患者的生存时间(CD2)和血癌数据(CD3)。采用赤池信息准则(AIC)、贝叶斯信息准则(BIC)、汉南-奎恩信息准则(HQIC)和一致赤池信息准则(CAIC)等评价指标对竞争模型的性能进行评价。对于CD1, GOBPW模型在竞争模型中获得了最低的AIC(381.0622)和BIC(772.1244)。对于CD2, GOBPW通过更有效地捕获急性骨癌生存时间的极值行为,再次表现出优异的表现,AIC最低(140.2969),BIC最低(290.5938)。对于CD3, GOBPW模型提供了最佳的拟合,AIC为65.77 700,BIC为141.5400,优于所有其他竞争模型。这项研究为医疗数据分析中的决策提供了一个有价值的工具,将GOBPW分布定位为传统统计模型的强大替代方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new statistical model for advanced modeling of cancer disease data
Classical probability models often struggle to capture the variability and complex patterns inherent in biomedical data, particularly lifetime data. To address these limitations, the generalized odd beta prime Generalized (GOBP-G) class of distributions is introduced, along with an extension called the generalized odd beta prime-Weibull (GOBPW) distribution. This new model offers enhanced flexibility and can represent a variety of data characteristics, from symmetric to skewed distributions, as well as diverse hazard rate patterns, such as increasing, bathtub, and decreasing trends. These features make the GOBPW model suitable for statistical analysis in biomedical and engineering applications. This study derives key properties of the GOBPW distribution, including its moments, moment-generating function, entropy measures, stress-strength function, quantile function, and order statistics. The cumulative and probability density functions are also developed, providing a foundational structure for the model. Multiple estimation methods are employed to assess the accuracy and reliability of parameter estimates. Monte Carlo simulations further validate the model's robustness across various conditions. The practical utility of the GOBPW model is demonstrated through applications to three datasets: remission times of 132 bladder cancer patients (CD1), survival times of 73 acute bone cancer patients (CD2), and blood cancer data (CD3). Various evaluation metrics, including Akaike information criterion (AIC), Bayesian information criterion (BIC), Hannan–Quinn information criterion (HQIC), and consistent Akaike's information criterion (CAIC) were used to assess the performance of the competing models. For CD1, the GOBPW model achieves the lowest AIC (381.0622) and BIC (772.1244) among competing models. For CD2, GOBPW again demonstrates superior performance with the lowest AIC (140.2969) and BIC (290.5938), by capturing the extreme value behavior of acute bone cancer survival times more effectively. For CD3, the GOBPW model provides the best fit with an AIC of 65.7700 and BIC of 141.5400, outperforming all other competing models. This research offers a valuable tool for enhanced decision-making in medical data analysis, positioning the GOBPW distribution as a powerful alternative to traditional statistical models.
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来源期刊
Kuwait Journal of Science
Kuwait Journal of Science MULTIDISCIPLINARY SCIENCES-
CiteScore
1.60
自引率
28.60%
发文量
132
期刊介绍: Kuwait Journal of Science (KJS) is indexed and abstracted by major publishing houses such as Chemical Abstract, Science Citation Index, Current contents, Mathematics Abstract, Micribiological Abstracts etc. KJS publishes peer-review articles in various fields of Science including Mathematics, Computer Science, Physics, Statistics, Biology, Chemistry and Earth & Environmental Sciences. In addition, it also aims to bring the results of scientific research carried out under a variety of intellectual traditions and organizations to the attention of specialized scholarly readership. As such, the publisher expects the submission of original manuscripts which contain analysis and solutions about important theoretical, empirical and normative issues.
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