Statistical modelling for Bladder cancer disease using the NLT-W distribution
In data science, it is frequent that new and sophisticated computational methods and tools are used to build predictive models to perform time to event data analysis. Such predictive models based on previously collected data from patients can support decision-making and prediction for the clinical d...
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| Format: | Article |
| Language: | English |
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AIMS Press
2021-06-01
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| Series: | AIMS Mathematics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.2021538?viewType=HTML |
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| author | Heba S. Mohammed Zubair Ahmad Alanazi Talal Abdulrahman Saima K. Khosa E. H. Hafez M. M. Abd El-Raouf Marwa M. Mohie El-Din |
| author_facet | Heba S. Mohammed Zubair Ahmad Alanazi Talal Abdulrahman Saima K. Khosa E. H. Hafez M. M. Abd El-Raouf Marwa M. Mohie El-Din |
| author_sort | Heba S. Mohammed |
| collection | DOAJ |
| description | In data science, it is frequent that new and sophisticated computational methods and tools are used to build predictive models to perform time to event data analysis. Such predictive models based on previously collected data from patients can support decision-making and prediction for the clinical data. Hence, this paper introduced a novel superior distribution, namely a new lifetime Weibull (NLT-W) distribution, using an efficient method to generate new distributions called the T-X method for generating new distributions. Parameter estimation has been done through maximum likelihood estimation (MLE) to show the significance of this proposed model over other competitive models. Comparison to two-parameter Weibull, Exponentiated Weibull (EW), and the and the Kumaraswamy Weibull (Ku-W) indicates that the proposed model could preform better to model various types of survival. |
| first_indexed | 2024-12-22T00:27:12Z |
| format | Article |
| id | doaj.art-03498b83370045aa8d223f3314be53ac |
| institution | Directory Open Access Journal |
| issn | 2473-6988 |
| language | English |
| last_indexed | 2024-12-22T00:27:12Z |
| publishDate | 2021-06-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj.art-03498b83370045aa8d223f3314be53ac2022-12-21T18:45:02ZengAIMS PressAIMS Mathematics2473-69882021-06-01689262927610.3934/math.2021538Statistical modelling for Bladder cancer disease using the NLT-W distributionHeba S. Mohammed0Zubair Ahmad 1Alanazi Talal Abdulrahman2Saima K. Khosa 3E. H. Hafez4M. M. Abd El-Raouf 5Marwa M. Mohie El-Din61. Mathematical Sciences Department, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia 2. Department of Mathematics, Faculty of Science, New Valley University, El Kharga, Egypt3. Department of Statistics, Quaid-e-Azam University, Islamabad, Pakistan4. Department of Mathematics, College of Science University of Ha'il, Saudi Arabia5. Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan6. Department of Mathematics, Faculty of Science, Helwan University, Cairo, Egypt7. Arab Academy for Science, Technology and Maritime Transport (AASTMT)8. Department of Mathematical and Natural Sciences, Faculty of Engineering, Egyptian Russian University, EgyptIn data science, it is frequent that new and sophisticated computational methods and tools are used to build predictive models to perform time to event data analysis. Such predictive models based on previously collected data from patients can support decision-making and prediction for the clinical data. Hence, this paper introduced a novel superior distribution, namely a new lifetime Weibull (NLT-W) distribution, using an efficient method to generate new distributions called the T-X method for generating new distributions. Parameter estimation has been done through maximum likelihood estimation (MLE) to show the significance of this proposed model over other competitive models. Comparison to two-parameter Weibull, Exponentiated Weibull (EW), and the and the Kumaraswamy Weibull (Ku-W) indicates that the proposed model could preform better to model various types of survival.https://www.aimspress.com/article/doi/10.3934/math.2021538?viewType=HTMLparametric modelweibull distributionremission timesurvival data analysisinformation criteria |
| spellingShingle | Heba S. Mohammed Zubair Ahmad Alanazi Talal Abdulrahman Saima K. Khosa E. H. Hafez M. M. Abd El-Raouf Marwa M. Mohie El-Din Statistical modelling for Bladder cancer disease using the NLT-W distribution AIMS Mathematics parametric model weibull distribution remission time survival data analysis information criteria |
| title | Statistical modelling for Bladder cancer disease using the NLT-W distribution |
| title_full | Statistical modelling for Bladder cancer disease using the NLT-W distribution |
| title_fullStr | Statistical modelling for Bladder cancer disease using the NLT-W distribution |
| title_full_unstemmed | Statistical modelling for Bladder cancer disease using the NLT-W distribution |
| title_short | Statistical modelling for Bladder cancer disease using the NLT-W distribution |
| title_sort | statistical modelling for bladder cancer disease using the nlt w distribution |
| topic | parametric model weibull distribution remission time survival data analysis information criteria |
| url | https://www.aimspress.com/article/doi/10.3934/math.2021538?viewType=HTML |
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