A hybrid hazard-based model using two-piece distributions
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Worku Biyadgie Ewnetu
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Anneleen Verhasselt
Abstract
Cox proportional hazards model is widely used to study the relationship between the survival time of an event and covariates. Its primary objective is parameter estimation assuming a constant relative hazard throughout the entire follow-up time. The baseline hazard is thus treated as a nuisance parameter. However, if the interest is to predict possible outcomes like specific quantiles of the distribution (e.g. median survival time), survival and hazard functions, it may be more convenient to use a parametric baseline distribution. Such a parametric model should however be flexible enough to allow for various shapes of e.g. the hazard function. In this paper we propose flexible hazard-based models for right censored data using a large class of two-piece asymmetric baseline distributions. The effect of covariates is characterized through time-scale changes on hazard progression and on the relative hazard ratio; and can take three possible functional forms: parametric, semi-parametric (partly linear) and non-parametric. In the first case, the usual full likelihood estimation method is applied. In the semi-parametric and non-parametric settings a general profile (local) likelihood estimation approach is proposed. An extensive simulation study investigates the finite-sample performances of the proposed method. Its use in data analysis is illustrated in real data examples.
Funding source: Onderzoeksraad, KU Leuven
Award Identifier / Grant number: C16/20/002
Acknowledgments
The authors thank the reviewers for their valuable comments that led to an improvement of the manuscript.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Leanring Tools: None of these tools were used. Nothing to declare.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: The second author gratefully acknowledges support from Research Grant C16/20/002 project of the Research Fund KU Leuven.
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Data availability: The veteran cancer and primary biliary cirrhosis data are freely available in the R package survival. The R-code of the developed methods are available at https://github.com/Ewnetu-github/tpaHH.git.
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© 2025 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Prognostic adjustment with efficient estimators to unbiasedly leverage historical data in randomized trials
- Homogeneity test and sample size of response rates for AC 1 in a stratified evaluation design
- A review of survival stacking: a method to cast survival regression analysis as a classification problem
- DsubCox: a fast subsampling algorithm for Cox model with distributed and massive survival data
- A hybrid hazard-based model using two-piece distributions
- Regression analysis of clustered current status data with informative cluster size under a transformed survival model
- Bayesian covariance regression in functional data analysis with applications to functional brain imaging
- Risk estimation and boundary detection in Bayesian disease mapping
- An improved estimator of the logarithmic odds ratio for small sample sizes using a Bayesian approach
- Short Communication
- A multivariate Bayesian learning approach for improved detection of doping in athletes using urinary steroid profiles
- Research Articles
- Guidance on individualized treatment rule estimation in high dimensions
- Weighted Euclidean balancing for a matrix exposure in estimating causal effect
- Penalized regression splines in Mixture Density Networks
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Prognostic adjustment with efficient estimators to unbiasedly leverage historical data in randomized trials
- Homogeneity test and sample size of response rates for AC 1 in a stratified evaluation design
- A review of survival stacking: a method to cast survival regression analysis as a classification problem
- DsubCox: a fast subsampling algorithm for Cox model with distributed and massive survival data
- A hybrid hazard-based model using two-piece distributions
- Regression analysis of clustered current status data with informative cluster size under a transformed survival model
- Bayesian covariance regression in functional data analysis with applications to functional brain imaging
- Risk estimation and boundary detection in Bayesian disease mapping
- An improved estimator of the logarithmic odds ratio for small sample sizes using a Bayesian approach
- Short Communication
- A multivariate Bayesian learning approach for improved detection of doping in athletes using urinary steroid profiles
- Research Articles
- Guidance on individualized treatment rule estimation in high dimensions
- Weighted Euclidean balancing for a matrix exposure in estimating causal effect
- Penalized regression splines in Mixture Density Networks
