A New Estimation Approach for Combining Epidemiological Data from Multiple Sources

• Activity Number: 48
• Type: Invited
• Date/Time: Sunday, August 9, 2015 : 4:00 PM to 5:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Yongtao Guan* and Hui Huang and Xiaomei Ma and Rasmus Waagepetersen and Theodore Holford and Rong Wang and Harvey Risch and Lloyd Mueller
• Companies: University of Miami and Peking University and Yale University and Aalborg University and Yale University and Yale University and Yale University and Connecticut Department of Public Health
• Keywords: Estimating equation ; Spatial epidemiology ; Spatial point process

Abstract

We propose a novel two-step procedure to combine epidemiological data obtained from diverse sources with the aim to quantify risk factors affecting the probability that an individual develops certain disease such as cancer. In the first step, we derive all possible unbiased estimating functions based on a group of cases and a group of controls each time. In the second step, we combine these estimating functions efficiently to make full use of the information contained in data. Our approach is computationally simple and flexible. We illustrate its efficacy through simulation and apply it to investigate pancreatic cancer risks based on data obtained from the Connecticut Tumor Registry, a population-based case-control study, and the Behavioral Risk Factor Surveillance System which is a state-based system of health surveys. Supplementary materials for this article are available online.