Masked analysis for small-scale cluster randomized controlled trials

Researchers conducting small-scale cluster randomized controlled trials (RCTs) during the pilot testing of an intervention often look for evidence of promise to justify an efficacy trial. We developed a method to test for intervention effects that is adaptive (i.e., responsive to data exploration), requires few assumptions, and is statistically valid (i.e., controls the type I error rate), by adapting masked visual analysis techniques to cluster RCTs. We illustrate the creation of masked graphs and their analysis using data from a pilot study in which 15 high school programs were randomly assigned to either business as usual or an intervention developed to promote psychological and academic well-being in 9th grade students in accelerated coursework. We conclude that in small-scale cluster RCTs there can be benefits of testing for effects without a priori specification of a statistical model or test statistic.

Automated summary

The researchers developed an adaptive, low-assumption, and statistically valid method to test for intervention effects. By applying masked visual analysis techniques to cluster RCTs, they demonstrated the benefits of testing effects in small-scale cluster RCTs without a priori specification of a statistical model or test statistic.