In the analysis of censored survival data,it is crucial to consider the heterogeneity of treatment effects in order to avoid biased inferences.The deep neural network-based heterogeneous partially linear Cox model off...In the analysis of censored survival data,it is crucial to consider the heterogeneity of treatment effects in order to avoid biased inferences.The deep neural network-based heterogeneous partially linear Cox model offers a flexible and robust solution by incorporating both heterogeneous linear and homogeneous nonlinear components.The authors propose a novel approach to subgroup detection for this model under right-censoring,using deep neural networks to approximate nonlinear effects.To simultaneously estimate parameters and identify subgroups,the authors employ a concave pairwise penalty and the alternating direction method of multipliers(ADMM)algorithm.Furthermore,the authors demonstrate that the proposed estimator possesses oracle properties and achieves model selection consistency.Through simulation studies and empirical data analysis on breast cancer,the authors illustrate the effectiveness of the proposed method.展开更多
目的制订基于Cox健康行为互动模式(Cox interaction model of client health behavior,CoxIMCHB)的老年慢性阻塞性肺疾病(COPD)稳定期患者家庭肺康复健康教育方案,并将方案应用于老年COPD稳定期患者中,以探讨其提高患者家庭肺康复依从...目的制订基于Cox健康行为互动模式(Cox interaction model of client health behavior,CoxIMCHB)的老年慢性阻塞性肺疾病(COPD)稳定期患者家庭肺康复健康教育方案,并将方案应用于老年COPD稳定期患者中,以探讨其提高患者家庭肺康复依从性、改善生活质量的效果。方法选取2023年5—10月贵州中医药大学第二附属医院呼吸与危重症医学科收治的96例老年COPD稳定期患者作为研究对象。按照组间基线资料具有可比性的原则将其分为对照组和观察组,每组48例。对照组实施常规健康教育,观察组实施基于Cox-IMCHB模式的老年COPD稳定期患者家庭肺康复健康教育方案。比较两组患者干预后家庭肺康复依从性、布里斯托尔COPD认知问卷(BCKQ)评分、肺功能[第一秒用力呼气容积(FEV_(1)%)预计值、FEV_(1)/用力肺活量(FVC)]、英国医学研究会改良呼吸困难指数分级(mMRC)评分、COPD评估测试(CAT)评分及患者满意度调查表(CST)。结果95例患者完成研究(对照组47例、观察组48例)。干预后,观察组患者家庭肺康复依从性及BCKQ、mMRC、CAT、CST评分等均优于对照组,差异具有统计学意义(P<0.05);干预后,两组患者肺功能(FEV_(1)%预计值、FEV_(1)/FVC)比较差异无统计学意义(P>0.05)。结论基于Cox-IMCHB模式的老年COPD稳定期患者家庭肺康复健康教育方案能改善患者家庭肺康复依从性、疾病认知水平、呼吸困难程度、生活质量及满意度。展开更多
Background: In-hospital mortality is a key indicator of the quality of care. Studies so far have demonstrated the influence of patient and hospital-related factors on in-hospital mortality. Currently, new variables, s...Background: In-hospital mortality is a key indicator of the quality of care. Studies so far have demonstrated the influence of patient and hospital-related factors on in-hospital mortality. Currently, new variables, such as components of metabolic syndrome as comorbid conditions, are being incorporated as independent risk factors. We aimed to identify which individual, clinical and hospital characteristics are related to hospital mortality. Objectives: Demonstrate that the Cox proportional hazard model is not appropriate for the analysis of hospital mortality data when diagnostic-related groups are incorporated in the covariate structure. Methods: A retrospective single-center observational study design was used. Sampling was conducted between January 2016 and December 2018. Patients over 10 years, admitted to the emergency department with a precited stay of at least 1 hour were included. Multivariate Cox regression for survival data analyses was employed to analyze the data. Results: The sample consisted of 5897 patients. The mean age of all patients was 32.21 ± 0.29 years old, and the mean length of stay (LOS) was 9.47 ± 0.16 hours. We also categorized patients according to five Diagnosis Related Groups (DGR). Among the patients,1308 suffered from acute leukemia, 1127 had endocrine diseases, 1173 with kidney diseases, and 1016 had respiratory problems. At least one component of metabolic syndrome was present in 27.5% of the patients. During the observation period, 2299 (39%) died in hospital, and 3598 (61%) were discharged alive. We used the multivariate Cox regression non-proportional hazard model to evaluate the joint effect of these factors on the “Length of Stay” or LOS (the dependent variable of Cox regression). Age at admission, the presence of metabolic syndrome, and the DRG were significantly associated with the LOS.展开更多
Modeling HIV/AIDS progression is critical for understanding disease dynamics and improving patient care. This study compares the Exponential and Weibull survival models, focusing on their ability to capture state-spec...Modeling HIV/AIDS progression is critical for understanding disease dynamics and improving patient care. This study compares the Exponential and Weibull survival models, focusing on their ability to capture state-specific failure rates in HIV/AIDS progression. While the Exponential model offers simplicity with a constant hazard rate, it often fails to accommodate the complexities of dynamic disease progression. In contrast, the Weibull model provides flexibility by allowing hazard rates to vary over time. Both models are evaluated within the frameworks of the Cox Proportional Hazards (Cox PH) and Accelerated Failure Time (AFT) models, incorporating critical covariates such as age, gender, CD4 count, and ART status. Statistical evaluation metrics, including Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), log-likelihood, and Pseudo-R2, were employed to assess model performance across diverse patient subgroups. Results indicate that the Weibull model consistently outperforms the Exponential model in dynamic scenarios, such as younger patients and those with co-infections, while maintaining robustness in stable contexts. This study highlights the trade-off between flexibility and simplicity in survival modeling, advocating for tailored model selection to balance interpretability and predictive accuracy. These findings provide valuable insights for optimizing HIV/AIDS management strategies and advancing survival analysis methodologies.展开更多
基金partially supported by the National Nature Science Foundation of China under Grant Nos.12171328,12326613,and 12031016Beijing Natural Science Foundation under Grant No.Z210003Beijing Outstanding Young Scientist Program under Grant No.JWZQ20240101027。
文摘In the analysis of censored survival data,it is crucial to consider the heterogeneity of treatment effects in order to avoid biased inferences.The deep neural network-based heterogeneous partially linear Cox model offers a flexible and robust solution by incorporating both heterogeneous linear and homogeneous nonlinear components.The authors propose a novel approach to subgroup detection for this model under right-censoring,using deep neural networks to approximate nonlinear effects.To simultaneously estimate parameters and identify subgroups,the authors employ a concave pairwise penalty and the alternating direction method of multipliers(ADMM)algorithm.Furthermore,the authors demonstrate that the proposed estimator possesses oracle properties and achieves model selection consistency.Through simulation studies and empirical data analysis on breast cancer,the authors illustrate the effectiveness of the proposed method.
文摘目的制订基于Cox健康行为互动模式(Cox interaction model of client health behavior,CoxIMCHB)的老年慢性阻塞性肺疾病(COPD)稳定期患者家庭肺康复健康教育方案,并将方案应用于老年COPD稳定期患者中,以探讨其提高患者家庭肺康复依从性、改善生活质量的效果。方法选取2023年5—10月贵州中医药大学第二附属医院呼吸与危重症医学科收治的96例老年COPD稳定期患者作为研究对象。按照组间基线资料具有可比性的原则将其分为对照组和观察组,每组48例。对照组实施常规健康教育,观察组实施基于Cox-IMCHB模式的老年COPD稳定期患者家庭肺康复健康教育方案。比较两组患者干预后家庭肺康复依从性、布里斯托尔COPD认知问卷(BCKQ)评分、肺功能[第一秒用力呼气容积(FEV_(1)%)预计值、FEV_(1)/用力肺活量(FVC)]、英国医学研究会改良呼吸困难指数分级(mMRC)评分、COPD评估测试(CAT)评分及患者满意度调查表(CST)。结果95例患者完成研究(对照组47例、观察组48例)。干预后,观察组患者家庭肺康复依从性及BCKQ、mMRC、CAT、CST评分等均优于对照组,差异具有统计学意义(P<0.05);干预后,两组患者肺功能(FEV_(1)%预计值、FEV_(1)/FVC)比较差异无统计学意义(P>0.05)。结论基于Cox-IMCHB模式的老年COPD稳定期患者家庭肺康复健康教育方案能改善患者家庭肺康复依从性、疾病认知水平、呼吸困难程度、生活质量及满意度。
文摘Background: In-hospital mortality is a key indicator of the quality of care. Studies so far have demonstrated the influence of patient and hospital-related factors on in-hospital mortality. Currently, new variables, such as components of metabolic syndrome as comorbid conditions, are being incorporated as independent risk factors. We aimed to identify which individual, clinical and hospital characteristics are related to hospital mortality. Objectives: Demonstrate that the Cox proportional hazard model is not appropriate for the analysis of hospital mortality data when diagnostic-related groups are incorporated in the covariate structure. Methods: A retrospective single-center observational study design was used. Sampling was conducted between January 2016 and December 2018. Patients over 10 years, admitted to the emergency department with a precited stay of at least 1 hour were included. Multivariate Cox regression for survival data analyses was employed to analyze the data. Results: The sample consisted of 5897 patients. The mean age of all patients was 32.21 ± 0.29 years old, and the mean length of stay (LOS) was 9.47 ± 0.16 hours. We also categorized patients according to five Diagnosis Related Groups (DGR). Among the patients,1308 suffered from acute leukemia, 1127 had endocrine diseases, 1173 with kidney diseases, and 1016 had respiratory problems. At least one component of metabolic syndrome was present in 27.5% of the patients. During the observation period, 2299 (39%) died in hospital, and 3598 (61%) were discharged alive. We used the multivariate Cox regression non-proportional hazard model to evaluate the joint effect of these factors on the “Length of Stay” or LOS (the dependent variable of Cox regression). Age at admission, the presence of metabolic syndrome, and the DRG were significantly associated with the LOS.
文摘Modeling HIV/AIDS progression is critical for understanding disease dynamics and improving patient care. This study compares the Exponential and Weibull survival models, focusing on their ability to capture state-specific failure rates in HIV/AIDS progression. While the Exponential model offers simplicity with a constant hazard rate, it often fails to accommodate the complexities of dynamic disease progression. In contrast, the Weibull model provides flexibility by allowing hazard rates to vary over time. Both models are evaluated within the frameworks of the Cox Proportional Hazards (Cox PH) and Accelerated Failure Time (AFT) models, incorporating critical covariates such as age, gender, CD4 count, and ART status. Statistical evaluation metrics, including Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), log-likelihood, and Pseudo-R2, were employed to assess model performance across diverse patient subgroups. Results indicate that the Weibull model consistently outperforms the Exponential model in dynamic scenarios, such as younger patients and those with co-infections, while maintaining robustness in stable contexts. This study highlights the trade-off between flexibility and simplicity in survival modeling, advocating for tailored model selection to balance interpretability and predictive accuracy. These findings provide valuable insights for optimizing HIV/AIDS management strategies and advancing survival analysis methodologies.
