A central issue in nonexperimental studies is identifying comparable individuals to remove selection bias. One common way to address this selection bias is through propensity score (PS) matching. PS methods use a model of the treatment assignment to reduce the dimensionality of the covariate space and identify comparable individuals. parallel to the PS, recent literature has developed the prognosis score (PG) to construct models of the potential outcomes (Hansen, 2008). Whereas PSs summarize covariates' association with the treatment groups, PGs summarize covariates' associations with the potential outcomes. In observational studies where the treatment selection mechanism is not completely known, PGs can be seen as an alternative or, more likely, a complement to PSs (Hansen, 2008). In this study, the author explored the utility of combining adjustment on PGs and PSs by successively matching on these scores to examine the extent to which full matching on PSs within strata defined by PGs outperforms alternative PS and PS & PG matching schemes. To reduce bias and secondarily to reduce variance while maintaining both propensity and prognostic balance, he proposes stratification on PGs followed by within stratum matching on PSs. This approach trades coarser matching on PGs for finer matching on PSs. As a preliminary examination of the utility of this approach, the author performed multiple Monte Carlo simulations and highlights one herein for brevity. The results suggested that adjustment using both PSs and PGs provides two avenues by which to block bias and reduce variance. The gains demonstrated in this very limited simulation suggest that strategies which reduce estimator variation while maintaining bias reduction may improve treatment effect estimates in ways parallel to choices in experimental design. One table is appended.