Cluster-weighted modeling of lifetime hierarchical data for profiling COVID-19 heart failure patients
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908452958240768 |
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| author | Caldera, Luca Cappozzo, Andrea Masci, Chiara Forlani, Marco Antonelli, Barbara Leoni, Olivia Paganoni, Anna Maria Ieva, Francesca |
| author_facet | Caldera, Luca Cappozzo, Andrea Masci, Chiara Forlani, Marco Antonelli, Barbara Leoni, Olivia Paganoni, Anna Maria Ieva, Francesca |
| contents | This study investigates the heterogeneity in survival times among COVID-19 patients with Heart Failure (HF) hospitalized in the Lombardy region of Italy during the pandemic. To address this, we propose a novel mixture model for right-censored lifetime data that incorporates random effects and allows for local distributions of the explanatory variables. Our approach identifies latent clusters of patients while estimating component-specific covariate effects on survival, taking into account the hierarchical structure induced by the healthcare facility. Specifically, a shared frailty term, unique to each cluster, captures hospital-level variability enabling a twofold decoupling of survival heterogeneity across both clusters and hierarchies. Two EM-based algorithms, namely a Classification EM (CEM) and a Stochastic EM (SEM), are proposed for parameter estimation. The devised methodology effectively uncovers latent patient profiles, evaluates within-cluster hospital effects, and quantifies the impact of respiratory conditions on survival. Our findings provide new information on the complex interplay between the impacts of HF, COVID-19, and healthcare facilities on public health, highlighting the importance of personalized and context-sensitive clinical strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12230 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cluster-weighted modeling of lifetime hierarchical data for profiling COVID-19 heart failure patients Caldera, Luca Cappozzo, Andrea Masci, Chiara Forlani, Marco Antonelli, Barbara Leoni, Olivia Paganoni, Anna Maria Ieva, Francesca Applications This study investigates the heterogeneity in survival times among COVID-19 patients with Heart Failure (HF) hospitalized in the Lombardy region of Italy during the pandemic. To address this, we propose a novel mixture model for right-censored lifetime data that incorporates random effects and allows for local distributions of the explanatory variables. Our approach identifies latent clusters of patients while estimating component-specific covariate effects on survival, taking into account the hierarchical structure induced by the healthcare facility. Specifically, a shared frailty term, unique to each cluster, captures hospital-level variability enabling a twofold decoupling of survival heterogeneity across both clusters and hierarchies. Two EM-based algorithms, namely a Classification EM (CEM) and a Stochastic EM (SEM), are proposed for parameter estimation. The devised methodology effectively uncovers latent patient profiles, evaluates within-cluster hospital effects, and quantifies the impact of respiratory conditions on survival. Our findings provide new information on the complex interplay between the impacts of HF, COVID-19, and healthcare facilities on public health, highlighting the importance of personalized and context-sensitive clinical strategies. |
| title | Cluster-weighted modeling of lifetime hierarchical data for profiling COVID-19 heart failure patients |
| topic | Applications |
| url | https://arxiv.org/abs/2507.12230 |