Propensity-score matching is increasingly being used to reduce the impact of treatment-selection bias when estimating causal treatment effects using observational data.  Matching on the propensity score creates sets of treated and untreated subjects who have a similar distribution of baseline covariates.

Propensity-score matching frequently relies upon calipers, such that matched treated and untreated subjects must have propensity scores that lie within a specified caliper distance of each other.  The authros define the 'marginally matched' subject as a subject who would be matched using the specified caliper width, but who would not have been matched had calipers with a narrower width been employed.

Using patients hospitalized with an acute myocardial infarction (or heart attack) and with exposure to a statin prescription at discharge, the authors demonstrate that the inclusion of marginally matched subjects can have both a quantitative and qualitative impact upon the estimated treatment effect.  Furthermore, marginally matched treated subjects can differ from marginally matched untreated subjects to a substantially greater degree than the differences between non-marginally matched treated and untreated subjects in the propensity-score matched sample.

The concept of the marginally matched subject can be used as a sensitivity analysis to examine the impact of the matching method on the estimates of treatment effectiveness.