Semiparametric marginal promotion time cure model for clustered survival data
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918020283105280 |
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| author | Xiao, Fei Peng, Yingwei Bandyopadhyayd, Dipankar Niu, Yi |
| author_facet | Xiao, Fei Peng, Yingwei Bandyopadhyayd, Dipankar Niu, Yi |
| contents | Modeling clustered/correlated failure time data has been becoming increasingly important in clinical trials and epidemiology studies. In this paper, we consider a semiparametric marginal promotion time cure model for clustered right-censored survival data with a cure fraction. We propose two estimation methods based on the generalized estimating equations and the quadratic inference functions and prove that the regression estimates from the two proposed methods are consistent and asymptotic normal and that the estimates from the quadratic inference functions are optimal. The simulation study shows that the estimates from both methods are more efficient than those from the existing method no matter whether the correlation structure is correctly specified. The estimates based on the quadratic inference functions achieve higher efficiency compared with those based on the generalized estimating equations under the same working correlation structure. An application of the proposed methods is demonstrated with periodontal disease data and new findings are revealed in the analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09247 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semiparametric marginal promotion time cure model for clustered survival data Xiao, Fei Peng, Yingwei Bandyopadhyayd, Dipankar Niu, Yi Methodology 62N02 (Primary) 62H12 (Secondary) Modeling clustered/correlated failure time data has been becoming increasingly important in clinical trials and epidemiology studies. In this paper, we consider a semiparametric marginal promotion time cure model for clustered right-censored survival data with a cure fraction. We propose two estimation methods based on the generalized estimating equations and the quadratic inference functions and prove that the regression estimates from the two proposed methods are consistent and asymptotic normal and that the estimates from the quadratic inference functions are optimal. The simulation study shows that the estimates from both methods are more efficient than those from the existing method no matter whether the correlation structure is correctly specified. The estimates based on the quadratic inference functions achieve higher efficiency compared with those based on the generalized estimating equations under the same working correlation structure. An application of the proposed methods is demonstrated with periodontal disease data and new findings are revealed in the analysis. |
| title | Semiparametric marginal promotion time cure model for clustered survival data |
| topic | Methodology 62N02 (Primary) 62H12 (Secondary) |
| url | https://arxiv.org/abs/2505.09247 |