Estimation and Selection of Complex Effects in Pooled Nested Case-control Studies with Heterogeneity

Abstract

[evdate 2013-12-19] HKUST International Forum on Probability and Statistics. Talk no. 10. Title from opening screen.

The Second HKUST International Forum on Probability and Statistics (2013), held 19 December, 2013, at the Hong Kong University of Science and Technology. Co-sponsored by the HKUST Jockey Club Institute for Advanced Study and the Center for Statistical Science.

Abstract: A major challenge in cancer epidemiologic studies, especially those of rare cancers, is observing enough cases. To address this, researchers often join forces by bringing multiple studies together to achieve large sample sizes, allowing for increased power in hypothesis testing, and improved efficiency in effect estimation. Combining studies, however, renders the analysis difficult owing to the presence of heterogeneity in the pooled data. In the talk, the speaker propose an adaptive group lasso (gLASSO) penalized likelihood approach to simultaneously identify important variables and estimate their effects, and a composite agLASSO penalized approach to identify variables with heterogeneous effects. Simulation studies and an application to the pooled ovarian cancer study will be presented to illustrate the performance of our proposed approaches.

Duration: 27 min.