title: Invited commentary: repeated measures, selection bias, and effect identification in neighborhood effect studies.
abstract: Research on neighborhood effects faces enormous methodological challenges, with selection bias being near the top of the list. In this issue of the Journal (Am J Epidemiol. 2014;180(8):776-784), Professor Jokela addresses this issue with novel repeated measures data and models that decompose putative effects into those within and between persons. His contribution shows that within-person neighborhood effects are quite modest and that there is evidence of selection bias between persons. Like all research, the work rests on assumptions. Unfortunately, such assumptions are difficult to substantiate or validate in this context. A consequentialist epidemiologic perspective compels further innovation and a larger social epidemiologic imagination.
authors.forename: J Michael
authors.initials: JM
authors.lastname: Oakes
mesh_terms: D005260:Female; D054624:Health Status Disparities; D006801:Humans; D008297:Male; D008603:Mental Health / Q000706:statistics & numerical data*; D011157:Population Dynamics / Q000706:statistics & numerical data*; D012111:Residence Characteristics / Q000706:statistics & numerical data*
keywords: causal; counterfactual; dynamic; methodology