arXiv subjects

Fatih Kızılaslan

Publications and source records attributed to Fatih Kızılaslan.

5 recordsLinked to original sources

arXiv2026

Reliability estimation in dependent stress-strength model with Clayton copula and modified Weibull margins

Stress-strength models are widely used to assess the reliability of systems under uncertain conditions. While most studies assume independence between stress and strength variables, such an assumption may be unrealistic in many practical situations where these components are inherently dependent. In this study, we investigate stress-strength reliability under a dependent framework, where both stress and strength variables follow modified Weibull distributions and their dependence is modeled via a Clayton copula. The proposed model allows distinct parameter sets, resulting in a flexible seven-parameter structure that extends Weibull-based models. We consider several estimation procedures for the model parameters and reliability, including two-step maximum likelihood, least squares, weighted least squares, and maximum product of spacings, with interval estimation obtained via asymptotic and bootstrap confidence intervals. The performance of the proposed estimators is evaluated through an extensive Monte Carlo simulation study under various parameter configurations and sample sizes. Finally, the applicability of the proposed model is illustrated using monthly occupancy data from Istanbul's two largest dams, with the Clayton copula capturing their dependence structure. This application demonstrates how stress-strength reliability can inform water management decisions and mitigate inter-regional operational risks.

stat.ME↗

arXiv2025

Bayesian Semiparametric Mixture Cure (Frailty) Models

In recent years, mixture cure models have gained increasing popularity in survival analysis as an alternative to the Cox proportional hazards model, particularly in settings where a subset of patients is considered cured. The proportional hazards mixture cure model is especially advantageous when the presence of a cured fraction can be reasonably assumed, providing a more accurate representation of long-term survival dynamics. In this study, we propose a novel hierarchical Bayesian framework for the semiparametric mixture cure model, which accommodates both the inclusion and exclusion of a frailty component, allowing for greater flexibility in capturing unobserved heterogeneity among patients. Samples from the posterior distribution are obtained using a Markov chain Monte Carlo method, leveraging a hierarchical structure inspired by Bayesian Lasso. Comprehensive simulation studies are conducted across diverse scenarios to evaluate the performance and robustness of the proposed models. Bayesian model comparison and assessment are performed using various criteria. Finally, the proposed approaches are applied to two well-known datasets in the cure model literature: the E1690 melanoma trial and a colon cancer clinical trial.

stat.ME↗

arXiv2025

Generalized promotion time cure model: A new modeling framework to identify cell-type-specific genes and improve survival prognosis

Single-cell technologies provide an unprecedented opportunity for dissecting the interplay between the cancer cells and the associated tumor microenvironment, and the produced high-dimensional omics data should also augment existing survival modeling approaches for identifying tumor cell type-specific genes predictive of cancer patient survival. However, there is no statistical model to integrate multiscale data including individual-level survival data, multicellular-level cell composition data and cellular-level single-cell omics covariates. We propose a class of Bayesian generalized promotion time cure models (GPTCMs) for the multiscale data integration to identify cell-type-specific genes and improve cancer prognosis. We demonstrate with simulations in both low- and high-dimensional settings that the proposed Bayesian GPTCMs are able to identify cell-type-associated covariates and improve survival prediction.

stat.ME↗

arXiv2025

A note on promotion time cure models with a new biological consideration

We introduce a generalized promotion time cure model motivated by a new biological consideration. The new approach is flexible to model heterogeneous survival data, in particular for addressing intra-sample heterogeneity. We also indicate that the new approach is suited to model a series or parallel system consisting of multiple subsystems in reliability analysis.

stat.ME↗

arXiv2024

A Weibull Mixture Cure Frailty Model for High-dimensional Covariates

A novel mixture cure frailty model is introduced for handling censored survival data. Mixture cure models are preferable when the existence of a cured fraction among patients can be assumed. However, such models are heavily underexplored: frailty structures within cure models remain largely undeveloped, and furthermore, most existing methods do not work for high-dimensional datasets, when the number of predictors is significantly larger than the number of observations. In this study, we introduce a novel extension of the Weibull mixture cure model that incorporates a frailty component, employed to model an underlying latent population heterogeneity with respect to the outcome risk. Additionally, high-dimensional covariates are integrated into both the cure rate and survival part of the model, providing a comprehensive approach to employ the model in the context of high-dimensional omics data. We also perform variable selection via an adaptive elastic-net penalization, and propose a novel approach to inference using the expectation-maximization (EM) algorithm. Extensive simulation studies are conducted across various scenarios to demonstrate the performance of the model, and results indicate that our proposed method outperforms competitor models. We apply the novel approach to analyze RNAseq gene expression data from bulk breast cancer patients included in The Cancer Genome Atlas (TCGA) database. A set of prognostic biomarkers is then derived from selected genes, and subsequently validated via both functional enrichment analysis and comparison to the existing biological literature. Finally, a prognostic risk score index based on the identified biomarkers is proposed and validated by exploring the patients' survival.

stat.ME↗