A maximum penalised likelihood approach for semiparametric accelerated failure time models with time-varying covariates and partly interval censoring
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866912162359803904 |
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| author | Bhaskaran, Aishwarya Ma, Ding Liquet, Benoit Hong, Angela Heritier, Stephane Lo, Serigne N Ma, Jun |
| author_facet | Bhaskaran, Aishwarya Ma, Ding Liquet, Benoit Hong, Angela Heritier, Stephane Lo, Serigne N Ma, Jun |
| contents | Accelerated failure time (AFT) models are frequently used to model survival data, providing a direct quantification of the relationship between event times and covariates. These models allow for the acceleration or deceleration of failure times through a multiplicative factor that accounts for the effect of covariates. While existing literature provides numerous methods for fitting AFT models with time-fixed covariates, adapting these approaches to scenarios involving both time-varying covariates and partly interval-censored data remains challenging. Motivated by a randomised clinical trial dataset on advanced melanoma patients, we propose a maximum penalised likelihood approach for fitting a semiparametric AFT model to survival data with partly interval-censored failure times. This method also accommodates both time-fixed and time-varying covariates. We utilise Gaussian basis functions to construct a smooth approximation of the non-parametric baseline hazard and fit the model using a constrained optimisation approach. The effectiveness of our method is demonstrated through extensive simulations. Finally, we illustrate the relevance of our approach by applying it to a dataset from a randomised clinical trial involving patients with advanced melanoma. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12332 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A maximum penalised likelihood approach for semiparametric accelerated failure time models with time-varying covariates and partly interval censoring Bhaskaran, Aishwarya Ma, Ding Liquet, Benoit Hong, Angela Heritier, Stephane Lo, Serigne N Ma, Jun Methodology Accelerated failure time (AFT) models are frequently used to model survival data, providing a direct quantification of the relationship between event times and covariates. These models allow for the acceleration or deceleration of failure times through a multiplicative factor that accounts for the effect of covariates. While existing literature provides numerous methods for fitting AFT models with time-fixed covariates, adapting these approaches to scenarios involving both time-varying covariates and partly interval-censored data remains challenging. Motivated by a randomised clinical trial dataset on advanced melanoma patients, we propose a maximum penalised likelihood approach for fitting a semiparametric AFT model to survival data with partly interval-censored failure times. This method also accommodates both time-fixed and time-varying covariates. We utilise Gaussian basis functions to construct a smooth approximation of the non-parametric baseline hazard and fit the model using a constrained optimisation approach. The effectiveness of our method is demonstrated through extensive simulations. Finally, we illustrate the relevance of our approach by applying it to a dataset from a randomised clinical trial involving patients with advanced melanoma. |
| title | A maximum penalised likelihood approach for semiparametric accelerated failure time models with time-varying covariates and partly interval censoring |
| topic | Methodology |
| url | https://arxiv.org/abs/2403.12332 |