Collaborative Research: Case-Control Studies, New Directions and Applications

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

Case-control studies mark possibly the single most important and far-reaching contribution that statisticians have made in the domain of Public Health and Epidemiology. The prime objective of this research is to make some new contributions to case-control studies, both in methodology and in application. In particular, the PIs propose a semiparametric Bayesian method to incorporate longitudinal data in a case-control analysis using penalized splines. New Bayesian functional data-analytic tools are needed here. The methods will be applied to biomarker based screening procedures, and will provide a critical appraisal of the association between prostate cancer and the past trajectory of prostate-specific antigen measurements. A second component of the proposed research is the analysis of case-control data generated from two-phase sampling with non-monotone missingness in covariates. Such designs have many potential applications in case-control studies that explore interplay of genetic and environmental risk factors. Exploiting assumptions like gene-gene and gene-environment independence adds to the complexity of the inference. The methods are motivated by an immediate application to a large population based case-control study of colorectal cancer. The final aspect of this proposal deals with a united methodology which provides equivalent inference for odds ratio parameters based on prospective and retrospective models. Both frequentist and Bayesian methods will be considered. The PIs have a track record of successful collaboration in this domain, and want to advance/ extend their work further in these new directions. Two scientific streams are currently dominating clinical medicine and public health: the molecular biology approach with an emphasis on genetics and discovery of novel biomarkers, and the quantitative approach with an emphasis on epidemiology. The developments in these areas jointly are making fundamental contributions to the study of etiology, diagnosis, prognosis and treatment of complex diseases. Though the standard unmatched case-control study design still remains one of the most popular epidemiologic tools, phenomenal advancement of medical science and genetic technology is giving rise to many complex design and analysis issues which statisticians and epidemiologists have never confronted before. This proposal lies in that new interface of epidemiology and statistics. To understand the mechanism of complex diseases and to design targeted intervention strategies for high-risk individuals is one of the major areas of scientific research in this century. The current proposal is not a mere academic pursuit but an effort to contribute to this scientific process.