title: A method to detect residual confounding in spatial and other observational studies.
abstract: BACKGROUND
  Residual confounding is challenging to detect. Recently, we described a method for detecting confounding and justified it primarily for time-series studies. The method depends on an indicator with 2 key characteristics: (1) it is conditionally independent (given measured exposures and covariates) of the outcome, in the absence of confounding, misspecification, and measurement errors; and (2) like the exposure, it is associated with confounders, possibly unmeasured. We proposed using future exposure levels as the indicator to detect residual confounding. This choice seems natural for time-series studies because future exposure cannot have caused the event, yet they could be spuriously related to it. A related question addressed here is whether an analogous indicator can be used to identify residual confounding in a study based on spatial, rather than temporal, contrasts.
  
  
  METHODS
  Using directed acyclic graphs, we show that future air pollution levels may have the characteristics appropriate for an indicator of residual confounding in spatial studies of environmental exposures. We empirically evaluate performance for spatial studies using simulations.
  
  
  RESULTS
  In simulations based on a spatial study of ambient air pollution levels and birth weight in Atlanta, and using ambient air pollution 1 year after conception as the indicator, we were able to detect residual confounding. The discriminatory ability approached 100% for some factors intentionally omitted from the model, but was very weak for others.
  
  
  CONCLUSIONS
  The simulations illustrate that an indicator based on future exposures can have excellent ability to detect residual confounding in spatial studies, although performance varied by situation.
authors.affiliation: Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA. flanders@sph.emory.edu
authors.forename: W Dana
authors.initials: WD
authors.lastname: Flanders
authors.forename: Mitchel
authors.initials: M
authors.lastname: Klein
authors.forename: Lyndsey A
authors.initials: LA
authors.lastname: Darrow
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Strickland
authors.forename: Stefanie E
authors.initials: SE
authors.lastname: Sarnat
authors.forename: Jeremy A
authors.initials: JA
authors.lastname: Sarnat
authors.forename: Lance A
authors.initials: LA
authors.lastname: Waller
authors.forename: Andrea
authors.initials: A
authors.lastname: Winquist
authors.forename: Paige E
authors.initials: PE
authors.lastname: Tolbert
mesh_terms: D000397:Air Pollution / Q000009:adverse effects; D001724:Birth Weight / Q000187:drug effects; D015984:Causality; D004194:Disease / Q000209:etiology; D004781:Environmental Exposure / Q000009:adverse effects; D015991:Epidemiologic Measurements; D006801:Humans; D007231:Infant, Newborn; D015233:Models, Statistical; D018401:Sample Size