As discussed in previous chapters, randomized experiments are generally viewed as the gold standard for research designs. A prototype is a RCT of a drug treatment compared with a placebo or other drug. In a prevention context, an RCT would be a study in a community or broader setting in which individuals or groups would be assigned by the researchers to experience or be exposed to different interventions. When a randomized experiment is properly implemented and its assumptions can be met, it produces results that are unrivaled in the certainty with which they support causal inferences in the specific research context in which the trial was conducted. According to Shadish and colleagues (2002), the advantages of RCTs are that they protect against threats to the level of certainty of the relationship between an intervention and the observed outcomes due to history, maturation, selection, testing and instrumentation biases, ambiguous temporal precedence, and the tendency of measurement to regress to the mean. As discussed here, however, the RCT has several limitations, some inherent and some associated with typical implementations, that can decrease its value as a tool for informing decision makers addressing complex public health problems. (See also Mercer and colleagues , who summarize discussions at a symposium of experts that weighed the strengths, limitations, and trade-offs of alternative designs for studying the effectiveness and translation of complex, multilevel health interventions.)
RCTs require that a pool of subjects be identified who are willing to be randomized and that providers be available who are willing to deliver the identical treatment or control intervention to each subject according to the randomization protocol. Randomization has typically been conducted at the level of individual participants. It may also be conducted with interventions being delivered to groups or communities (termed “cluster randomization”; see Donner and Klar, 2000; Kriemler et al., 2010; Murray, 1998). Or it may be conducted at multiple sites, often in different geographic regions, using parallel RCTs in which individual participants or groups are assigned to the treatment or control intervention at each site, with the results being combined (see Raudenbush and Liu, 2000). Cluster randomization designs offer new possibilities to study new types of interventions, potentially with different interventions occurring at multiple levels (e.g., individual, group, small community). Multisite designs offer possibilities to study the effects of interventions with more diverse populations, settings, and even sets of treatment providers. At the same time, cluster and multisite randomized trials raise new challenges for design and implementation, as well as statistical analysis and interpretation (e.g., Varnell et al., 2004).
With all of these types of randomized trials, moreover, practical problems commonly arise when structural or policy interventions are being studied (Bonell et al., 2006). Subjects may not want to be randomized; randomization may not be accepted, or may not be ethical or practical in the research context; or only atypical participants may be willing to be randomized.