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A New Estimation Approach for Combining Epidemiological Data From Multiple Sources

Author

Listed:
  • Hui Huang
  • Xiaomei Ma
  • Rasmus Waagepetersen
  • Theodore R. Holford
  • Rong Wang
  • Harvey Risch
  • Lloyd Mueller
  • Yongtao Guan

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.

Suggested Citation

  • Hui Huang & Xiaomei Ma & Rasmus Waagepetersen & Theodore R. Holford & Rong Wang & Harvey Risch & Lloyd Mueller & Yongtao Guan, 2014. "A New Estimation Approach for Combining Epidemiological Data From Multiple Sources," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 11-23, March.
    • Handle: RePEc:taf:jnlasa:v:109:y:2014:i:505:p:11-23
    DOI: 10.1080/01621459.2013.870904
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    References listed on IDEAS

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    5. Diggle, Peter J. & Guan, Yongtao & Hart, Anthony C. & Paize, Fauzia & Stanton, Michelle, 2010. "Estimating Individual-Level Risk in Spatial Epidemiology Using Spatially Aggregated Information on the Population at Risk," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1394-1402.
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