  Propensity score matching is a common tool for adjusting for observed
confounding in observational studies, but is known to have limitations in the
presence of unmeasured confounding. In many settings, researchers are
confronted with spatially-indexed data where the relative locations of the
observational units may serve as a useful proxy for unmeasured confounding that
varies according to a spatial pattern. We develop a new method, termed Distance
Adjusted Propensity Score Matching (DAPSm) that incorporates information on
units' spatial proximity into a propensity score matching procedure. We show
that DAPSm can adjust for both observed and some forms of unobserved
confounding and evaluate its performance relative to several other reasonable
alternatives for incorporating spatial information into propensity score
adjustment. The method is motivated by and applied to a comparative
effectiveness investigation of power plant emission reduction technologies
designed to reduce population exposure to ambient ozone pollution. Ultimately,
DAPSm provides a framework for augmenting a "standard" propensity score
analysis with information on spatial proximity and provides a transparent and
principled way to assess the relative trade offs of prioritizing observed
confounding adjustment versus spatial proximity adjustment.
