Consider, for example, the difficulty of using an RCT design to study the effects on health of secondhand tobacco smoke or the removal of physical education classes from schools. Both of these exposures are unlikely to be within the control of a researcher. In other cases, only atypical participants would be willing to participate. An example is an RCT of a faith-based or spiritually oriented wellness group compared with a non-faith-based group because people cannot be assigned to be or not be spiritually oriented. Highly religious participants might refuse to be assigned to a non-faith-based program, while nonreligious participants might be unable to participate sincerely in a faith-based program. Or the faith-based treatment providers might believe that all people desiring the intervention should receive it. A cardiovascular disease prevention study among African American faith-based organizations in Baltimore, Maryland, encountered both of these challenges in a randomized trial (Yanek et al., 2001). Many pastors of potentially participating churches were uncomfortable with randomization, wanting to know their intervention assignment before agreeing to participate. In addition, an intended comparison between a spiritually oriented and standard version of the program could not be implemented because those in the standard condition spontaneously incorporated spirituality. These issues decrease the likelihood that an RCT can be implemented successfully and potentially decrease the likelihood that its results can ultimately be generalized to important policy contexts (Green and Glasgow, 2006; Shadish and Cook, 2009; Weisz et al., 1992).

In addition, the need to identify a pool of subjects willing to be randomized subtley privileges certain types of interventions. RCTs involving interventions designed to influence individuals or small groups (cluster randomization) are far easier to implement than those involving larger units (e.g., cities, states), given political and cost issues and methodological concerns about both the comparability of units and the ability to study a sufficient number of units to achieve adequate statistical power. That is, when aggregate units rather than individuals are randomly assigned, sample size and statistical power are based on the number of units, even if measurements are taken from individuals within each unit.

Finally, given the length of time required to plan and carry out an RCT, a trial may yield results long after the time frame in which the information is needed by decision makers. Or the results may no longer be as relevant in the context in which they would be applied when the trial has been completed. In the Women’s Health Initiative Dietary Modification Trial, for example, the nature of the low-fat dietary intervention did not address important issues, related to the types of fat, that were identified while the trial was in progress and could have influenced the results (Anderson and Appel, 2006).

The above limitations can result in RCTs having less impact in research on public policy relative to other, nonrandomized designs. Consider the changes in policy that led to what is viewed as one of the major recent public health triumphs—the reduction of the amount and prevalence of tobacco smoking in the United States. None of the key large-scale interventions that were utilized—a ban on cigarette advertising,



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