FIGURE E-1 Illustration of the regression discontinuity design using the example of an evaluation of the effect of school lunch programs on children’s health.

NOTE: All children whose family income was below the threshold, here $20,000 (dotted line), received the treatment program (school lunch program); all children whose family income was above the threshold did not receive the program. The difference between the regression lines for the program and no-program groups at the threshold represents the treatment effect.

SOURCE: West et al., 2008. Reprinted with permission. West et al., Alternatives to the randomized controlled trial, American Journal of Public Health, 98(8):1364, Copyright © 2008 by the American Public Health Association.

found results demonstrating lower mortality rates in children aged 5 to 9 from diseases addressed by the program (e.g., measles, anemia, diabetes).

The RD design overcomes several of the objections to the randomized controlled trial (RCT) discussed in this report. When an existing program uses quantitative assignment rules, the RD design permits strong evaluation of the program without the need to create a pool of participants willing to be randomized. Sometimes outcome data may be collected routinely from large samples of individuals in the program. As illustrated by the Ludwig and Miller study, the design can be used when individual participants, neighborhoods, cities, or counties are the unit of assignment. When new programs are implemented, assignment on the basis of need or risk may be more acceptable to communities that may be resistant to RCTs. The use of a clinically meaningful quantitative assignment variable (e.g., risk level) may help overcome ethical or political objections when a promising potential treatment is being evaluated. Given that the design can often be implemented with the full population of interest—a state, community, school, or hospital—it provides direct evidence of population-level effects.