  For survival data with high-dimensional covariates, results generated in the
analysis of a single dataset are often unsatisfactory because of the small
sample size. Integrative analysis pools raw data from multiple independent
studies with comparable designs, effectively increases sample size, and has
better performance than meta-analysis and single-dataset analysis. In this
study, we conduct integrative analysis of survival data under the accelerated
failure time (AFT) model. The sparsity structures of multiple datasets are
described using the homogeneity and heterogeneity models. For variable
selection under the homogeneity model, we adopt group penalization approaches.
For variable selection under the heterogeneity model, we use composite
penalization and sparse group penalization approaches. As a major advancement
from the existing studies, the asymptotic selection and estimation properties
are rigorously established. Simulation study is conducted to compare different
penalization methods and against alternatives. We also analyze four lung cancer
prognosis datasets with gene expression measurements.
