title: Within- and between-group regression for improving the robustness of causal claims in cross-sectional analysis
creator: Genser, Bernd
creator: Teles, Carlos A.
creator: Barreto, Mauricio L.
creator: Fischer, Joachim E.
subject: 610
subject: 610 Medical sciences Medicine
description: Background: A major objective of environmental epidemiology is to elucidate exposure-health outcome associations. To increase the variance of observed exposure concentrations, researchers recruit individuals from different geographic areas. The common analytical approach uses multilevel analysis to estimate individual-level associations adjusted for individual and area covariates. However, in cross-sectional data this approach does not differentiate between residual confounding at the individual level and at the area level. An approach allowing researchers to distinguish between within-group effects and between-group effects would improve the robustness of causal claims.   Methods: We applied an extended multilevel approach to a large cross-sectional study aimed to elucidate the hypothesized link between drinking water pollution from perfluoroctanoic acid (PFOA) and plasma levels of C-reactive protein (CRP) or lymphocyte counts. Using within- and between-group regression of the individual PFOA serum concentrations, we partitioned the total effect into a within- and between-group effect by including the aggregated group average of the individual exposure concentrations as an additional predictor variable.   Results: For both biomarkers, we observed a strong overall association with PFOA blood levels. However, for lymphocyte counts the extended multilevel approach revealed the absence of a between-group effect, suggesting that most of the observed total effect was due to individual level confounding. In contrast, for CRP we found consistent between- and within-group effects, which corroborates the causal claim for the association between PFOA blood levels and CRP.   Conclusion: Between- and within-group regression modelling augments cross-sectional analysis of epidemiological data by supporting the unmasking of non-causal associations arising from hidden confounding at different levels. In the application example presented in this paper, the approach suggested individual confounding as a probable explanation for the first observed association and strengthened the robustness of the causal claim for the second one.  
publisher: BioMed Central
date: 2015
type: Article
type: info:eu-repo/semantics/article
type: NonPeerReviewed
format: application/pdf
identifier: https://archiv.ub.uni-heidelberg.de/volltextserverhttps://archiv.ub.uni-heidelberg.de/volltextserver/19377/1/12940_2015_Article_47.pdf
identifier: DOI:
identifier: urn:nbn:de:bsz:16-heidok-193778
identifier:   Genser, Bernd ; Teles, Carlos A. ; Barreto, Mauricio L. ; Fischer, Joachim E.  (2015) Within- and between-group regression for improving the robustness of causal claims in cross-sectional analysis.  Environmental health, 14 (60).  pp. 1-10.  ISSN 1476-069X     
relation: https://archiv.ub.uni-heidelberg.de/volltextserver/19377/
rights: info:eu-repo/semantics/openAccess
rights: Please see front page of the work (Sorry, Dublin Core plugin does not recognise license id)
language: eng