Ahmad Abubakar Suleiman , Hanita Daud , Aliyu Ismail Ishaq , Abbas Umar Farouk , Aminu Suleiman Mohammed , Mohamed Kayid , Vasili B.V. Nagarjuna , Shahid Mohammad , Mohammed Elgarhy
{"title":"癌症疾病数据高级建模的新统计模型","authors":"Ahmad Abubakar Suleiman , Hanita Daud , Aliyu Ismail Ishaq , Abbas Umar Farouk , Aminu Suleiman Mohammed , Mohamed Kayid , Vasili B.V. Nagarjuna , Shahid Mohammad , Mohammed Elgarhy","doi":"10.1016/j.kjs.2025.100429","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":17848,"journal":{"name":"Kuwait Journal of Science","volume":"52 3","pages":"Article 100429"},"PeriodicalIF":1.2000,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A new statistical model for advanced modeling of cancer disease data\",\"authors\":\"Ahmad Abubakar Suleiman , Hanita Daud , Aliyu Ismail Ishaq , Abbas Umar Farouk , Aminu Suleiman Mohammed , Mohamed Kayid , Vasili B.V. Nagarjuna , Shahid Mohammad , Mohammed Elgarhy\",\"doi\":\"10.1016/j.kjs.2025.100429\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":17848,\"journal\":{\"name\":\"Kuwait Journal of Science\",\"volume\":\"52 3\",\"pages\":\"Article 100429\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2025-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Kuwait Journal of Science\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2307410825000732\",\"RegionNum\":4,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Kuwait Journal of Science","FirstCategoryId":"103","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2307410825000732","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
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.
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.