The Information in Aggregate Data

Steel, David G; Beh, Eric J.; Chambers, Ray

doi:10.1017/cbo9780511510595.004

Cited by 24 publications

(42 citation statements)

References 5 publications

(2 reference statements)

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“…For a continuous outcome, Raghunathan et al (54) show that moment and maximum likelihood estimates of a common within-group correlation coefficient will improve when aggregate data are combined with individual data within groups, and Glynn et al (24) derive optimal design strategies for the collection of individual-level data when the model is linear. With a binary nonrare outcome, the benefits have also been illustrated (68,74).…”

Section: Combining Ecologic and Individual Datamentioning

confidence: 99%

Ecologic Studies Revisited

Wakefield

2008

Annu. Rev. Public Health

187

145

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Ecologic studies use data aggregated over groups rather than data on individuals. Such studies are popular because they use existing databases and can offer large exposure variation if the data arise from broad geographical areas. Unfortunately, the aggregation of data that define ecologic studies results in an information loss that can lead to ecologic bias. Specifically, ecologic bias arises from the inability of ecologic data to characterize within-area variability in exposures and confounders. We describe in detail particular forms of ecologic bias so that their potential impact on any particular study may be assessed. The only way to overcome such bias, while avoiding uncheckable assumptions concerning the missing information, is to supplement the ecologic with individual-level information, and we outline a number of proposals that may achieve this aim.

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Section: Combining Ecologic and Individual Datamentioning

confidence: 99%

Ecologic Studies Revisited

Wakefield

2008

Annu. Rev. Public Health

187

145

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“…In the case of 2×2 tables, Wakefield (2004) described the joint likelihood for the ecological data and subsample data and showed that a combined approach reduces ecological bias. Steel et al. (2004) developed the observed information for this same case, but with the data sources treated as independent.…”

Section: Introductionmentioning

confidence: 99%

Alleviating Linear Ecological Bias and Optimal Design with Subsample Data

Glynn

Wakefield

Handcock

et al. 2007

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary. In this paper, we illustrate that combining ecological data with subsample data in situations in which a linear model is appropriate provides three main benefits. First, by including the individual level subsample data, the biases associated with linear ecological inference can be eliminated. Second, by supplementing the subsample data with ecological data, the information about parameters will be increased. Third, we can use readily available ecological data to design optimal subsampling schemes, so as to further increase the information about parameters. We present an application of this methodology to the classic problem of estimating the effect of a college degree on wages. We show that combining ecological data with subsample data provides precise estimates of this value, and that optimal subsampling schemes (conditional on the ecological data) can provide good precision with only a fraction of the observations.

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“…For a continuous outcome, Raghunathan et al [44] show that moment and maximum likelihood estimates of a common within group correlation coefficient will improve when aggregate data are combined with individual data within groups, and Glynn et al [18] derive optimal design strategies for the collection of individual-level data when the model is linear. With a binary non-rare outcome the benefits have also been illustrated [66, 58]. …”

Section: 6 Combining Ecological and Individual Datamentioning

confidence: 99%