Tao Hu

The growing availability of large‐scale survival data streams present significant challenges for statistical inference particularly in the presence of a cure fraction, which complicates estimation and increases computational burden. To address these challenges, we propose an online updating mixture cure model framework. Our approach employs a batchwise renewable estimation procedure that aggregates historical information into summary statistics to update the model without retaining historical individual‐level data, and thus eliminating the need for full data storage. A renewable penalized expectation‐maximization algorithm is developed to enable simultaneous parameter estimation and variable selection. Furthermore, a tuning‐free strategy is incorporated to enhance computational efficiency. The asymptotic properties of the proposed method are established. Simulation studies and an application to breast cancer data from the Surveillance, Epidemiology, and End Results database demonstrate the method's estimation accuracy, predictive performance, and computational scalability.

Modern biomedical and epidemiological studies increasingly combine data from multiple institutions, where differences in study populations and designs can induce cross‐source heterogeneity. To address this issue, we propose an integrative analysis framework for multisource partly interval‐censored data. The proposed method adopts a decomposition strategy to share information across studies while distinguishing common homogeneous effects from source‐specific heterogeneous effects. It also incorporates an MIC‐based ‐penalized procedure for tuning‐free variable selection, thereby enhancing selection stability and computational efficiency. In particular, this MIC‐based implementation avoids the joint tuning parameter search required by conventional penalties in the proposed two‐penalty framework. The estimation consistency, selection consistency, and asymptotic normality of the proposed estimators are established. Simulation studies and an application to the Alzheimer's Disease Neuroimaging Initiative and the National Alzheimer's Coordinating Center databases demonstrate the effectiveness and practical utility of the proposed method.

Assessing causal treatment effect on a time-to-event outcome and identifying important risk factors that contribute to the outcome of interest are crucial in many scientific studies. Although existing instrumental variable (IV) methods can address the endogenous treatment selection and yield an unbiased causal treatment effect estimate in the presence of censoring, the corresponding variable selection technique has not been investigated. In this paper, we propose a variable selection method for a wide class of causal semiparametric transformation models with all-or-nothing treatment compliance and right-censored data. Specifically, the minimum information criterion is embedded in the optimization step of the proposed expectation-maximization algorithm, rendering sparse estimators of the complier causal treatment effect and other regression parameters. The asymptotic properties of our method are established, including consistency and oracle property. Extensive simulation studies are conducted to evaluate the finite sample performance of the proposed method. An application to a colorectal cancer screening dataset is provided.

Causal mediation analysis is an effective method for understanding the mechanism between the exposure and the outcome, often assuming that the mediation model is consistent for each individual in the target population. In practice, however, the natural indirect effect (NIE) may vary across individuals due to their distinct characteristics. As a result, the population can be partitioned into subgroups according to the varying sizes of the NIEs. Distinguishing subgroups within the study population enables the development of more precise and targeted treatment strategies. In this paper, we propose an identifiable mixture mediation model with latent subgroups for the survival data, where the outcome follows an accelerated failure time model and the mediator is Gaussian distributed. We further employ three information criteria including the AIC, BIC, and singular BIC (sBIC) to select the number of subgroups, followed by the expectation–maximization (EM) algorithm to estimate the model parameters and NIEs. Simulation study shows that the sBIC is the most robust and efficient criterion for selecting the number of subgroups; therefore, we recommend the sBIC‐EM algorithm for practical use. Lastly, we apply our algorithm to the lung cancer data and discover two latent groups with opposing NIEs.

This paper considers survival analysis of large-scale data distributed across heterogeneous network nodes. We propose a novel method, the Distributed Spanning-Tree-Based Fused Lasso (DSTFL), for Cox regression in distributed settings. By employing a minimum spanning tree-based fusion framework, the method can reduce computational and communication burdens, facilitating scalability to large datasets. Additionally, we develop an efficient alternating direction method of multipliers algorithm for the optimization with privacy protection. We establish large-sample properties and clustering consistency for the proposed estimator. Simulation studies demonstrate that DSTFL improves computational efficiency, clustering performance, and robustness compared to existing methods. An application to Surveillance, Epidemiology, and End Results gastric cancer data illustrates how DSTFL identifies geographically structured survival heterogeneity and heterogeneous covariate effects across regions.

The objective of this study is to perform variable selection and parameter estimation for analyzing partly interval‐censored data based on a proportional hazards model that incorporates spatial effects. To broaden the model's applicability across diverse scenarios, we consider two types of spatial structures: adjacency and distance information. Leveraging the differentiable properties of the ‐ball prior developed through projection‐based methods, we have devised an efficient Bayesian algorithm by introducing latent variables and applying stochastic gradient Langevin dynamics principles. This algorithm can rapidly deliver results without resorting to complex sampling steps. Through simulations encompassing various scenarios, we have validated the performance of this method in both variable selection and parameter estimation. In our real data application, the proposed approach selects important variables associated with the emergence time of permanent teeth. Additionally, it identifies the spatial structure that best fits these data characteristics. This selection and identification are based on two Bayesian model selection criteria: the log pseudo‐marginal likelihood and the deviance information criterion.