Semiparametric estimation for accelerated failure time mixture cure model allowing non-curable competing risk
The mixture cure model is the most popular model used to analyse the major event with a potential cure fraction. But in the real world there may exist a potential risk from other non-curable competing events. In this paper, we study the accelerated failure time model with mixture cure model via kern...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2020-01-01
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| Series: | Statistical Theory and Related Fields |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/24754269.2019.1600123 |
| _version_ | 1827809268420575232 |
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| author | Yijun Wang Jiajia Zhang Yincai Tang |
| author_facet | Yijun Wang Jiajia Zhang Yincai Tang |
| author_sort | Yijun Wang |
| collection | DOAJ |
| description | The mixture cure model is the most popular model used to analyse the major event with a potential cure fraction. But in the real world there may exist a potential risk from other non-curable competing events. In this paper, we study the accelerated failure time model with mixture cure model via kernel-based nonparametric maximum likelihood estimation allowing non-curable competing risk. An EM algorithm is developed to calculate the estimates for both the regression parameters and the unknown error densities, in which a kernel-smoothed conditional profile likelihood is maximised in the M-step, and the resulting estimates are consistent. Its performance is demonstrated through comprehensive simulation studies. Finally, the proposed method is applied to the colorectal clinical trial data. |
| first_indexed | 2024-03-11T22:40:05Z |
| format | Article |
| id | doaj.art-a57a3c5c55874e5b916744d956550bd0 |
| institution | Directory Open Access Journal |
| issn | 2475-4269 2475-4277 |
| language | English |
| last_indexed | 2024-03-11T22:40:05Z |
| publishDate | 2020-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Statistical Theory and Related Fields |
| spelling | doaj.art-a57a3c5c55874e5b916744d956550bd02023-09-22T09:19:45ZengTaylor & Francis GroupStatistical Theory and Related Fields2475-42692475-42772020-01-01419710810.1080/24754269.2019.16001231600123Semiparametric estimation for accelerated failure time mixture cure model allowing non-curable competing riskYijun Wang0Jiajia Zhang1Yincai Tang2Key Laboratory of Advanced Theory and Application in Statistics and Data Science - MOE, School of Statistics, East China Normal UniversityDepartment of Epidemiology and Biostatistics, University of South CarolinaKey Laboratory of Advanced Theory and Application in Statistics and Data Science - MOE, School of Statistics, East China Normal UniversityThe mixture cure model is the most popular model used to analyse the major event with a potential cure fraction. But in the real world there may exist a potential risk from other non-curable competing events. In this paper, we study the accelerated failure time model with mixture cure model via kernel-based nonparametric maximum likelihood estimation allowing non-curable competing risk. An EM algorithm is developed to calculate the estimates for both the regression parameters and the unknown error densities, in which a kernel-smoothed conditional profile likelihood is maximised in the M-step, and the resulting estimates are consistent. Its performance is demonstrated through comprehensive simulation studies. Finally, the proposed method is applied to the colorectal clinical trial data.http://dx.doi.org/10.1080/24754269.2019.1600123aft mixture cure modelcompeting riskem algorithm |
| spellingShingle | Yijun Wang Jiajia Zhang Yincai Tang Semiparametric estimation for accelerated failure time mixture cure model allowing non-curable competing risk Statistical Theory and Related Fields aft mixture cure model competing risk em algorithm |
| title | Semiparametric estimation for accelerated failure time mixture cure model allowing non-curable competing risk |
| title_full | Semiparametric estimation for accelerated failure time mixture cure model allowing non-curable competing risk |
| title_fullStr | Semiparametric estimation for accelerated failure time mixture cure model allowing non-curable competing risk |
| title_full_unstemmed | Semiparametric estimation for accelerated failure time mixture cure model allowing non-curable competing risk |
| title_short | Semiparametric estimation for accelerated failure time mixture cure model allowing non-curable competing risk |
| title_sort | semiparametric estimation for accelerated failure time mixture cure model allowing non curable competing risk |
| topic | aft mixture cure model competing risk em algorithm |
| url | http://dx.doi.org/10.1080/24754269.2019.1600123 |
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