Integrative Analysis of Heterogeneous Multisource Partly Interval‐Censored Data

ABSTRACT 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.