|Grant Number:||5R01CA125081-03 Interpret this number|
|Primary Investigator:||Haneuse, Sebastien|
|Organization:||Group Health Cooperative|
|Project Title:||Design and Inference for Hybrid Ecological Studies|
DESCRIPTION (provided by applicant): Ecological studies may be defined examining associations at the group level. They are appealing in that they make use of routinely available data, and also offer the potential of high power due to large populations and broad exposure contrasts. However, they are also susceptible to a range of biases with respect to individual-level associations, collectively termed ecological bias, and may lead to the ecological fallacy. In epidemiology, the fundamental difficulty is the inability of ecological data to characterize within-group variability in exposures and confounders. This results in an inability to control for confounding, and general non-identifiability of the individual-level model. The only solution to the ecological inference problem is to supplement ecological data with individual-level samples; in this proposal we describe and develop a variety of hybrid studies that pursue this solution. Specifically, we develop a hybrid design in which a case-control study is embedded within an ecological study. The intuitive appeal is that the individual-level data provide the basis for the control of bias, while the ecological data provide efficiency gains. In addition, we extend current methods, including the aggregate data design and two-phase method, to the ecological setting. This will be based on the development of Bayesian methods for these designs, which have not been explored. Further, we will compare performance of the various methods in a variety of data/sampling scenarios. A key research question is whether the group-level data provide useful information for the collection of individuals. We will explore optimal study design in terms of how many individuals to sample and from which groups. The methods are illustrated with two cancer data sets and one influenza data set.
Bayesian inference for two-phase studies with categorical covariates.
Authors: Ross M, Wakefield J
Source: Biometrics, 2013 Jun;69(2), p. 469-77.
EPub date: 2013 Apr 22.
A two-stage strategy to accommodate general patterns of confounding in the design of observational studies.
Authors: Haneuse S, Schildcrout J, Gillen D
Source: Biostatistics, 2012 Apr;13(2), p. 274-88.
EPub date: 2011 Nov 30.
Designs for the combination of group- and individual-level data.
Authors: Haneuse S, Bartell S
Source: Epidemiology, 2011 May;22(3), p. 382-9.
Bayes computation for ecological inference.
Authors: Wakefield J, Haneuse S, Dobra A, Teeple E
Source: Stat Med, 2011 May 30;30(12), p. 1381-96.
EPub date: 2011 Feb 22.
A multiphase design strategy for dealing with participation bias.
Authors: Haneuse S, Chen J
Source: Biometrics, 2011 Mar;67(1), p. 309-18.
On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses.
Authors: Koehler E, Brown E, Haneuse SJ
Source: Am Stat, 2009 May 1;63(2), p. 155-162.
Overcoming ecologic bias using the two-phase study design.
Authors: Wakefield J, Haneuse SJ
Source: Am J Epidemiol, 2008 Apr 15;167(8), p. 908-16.
EPub date: 2008 Feb 12.