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J Am Stat Assoc. 2014 Jan 1;109(505):11-23.

A new estimation approach for combining epidemiological data from multiple sources.

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Department of Management Science, University of Miami, Coral Gables, FL 33124.
Yale School of Public Health, New Haven, CT 06520.
Department of Mathematical Sciences, Aalborg University, Fredrik Bajersvej 7G, DK-9220 Aalborg, Denmark.
Connecticut Department of Public Health, 410 Capitol Avenue, MS# 11HCQ, Hartford, CT 06134.


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 in order 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.


Spatial epidemiology; estimating equation; spatial point process

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