Implementing Clinical Research Using Factorial Designs: A Primer

Abstract

Factorial experiments have rarely been used in the development or evaluation of clinical interventions. However, factorial designs offer advantages over randomized controlled trial designs, the latter being much more frequently used in such research. Factorial designs are highly efficient (permitting evaluation of multiple intervention components with good statistical power) and present the opportunity to detect interactions amongst intervention components. Such advantages have led methodologists to advocate for the greater use of factorial designs in research on clinical interventions (Collins, Dziak, & Li, 2009). However, researchers considering the use of such designs in clinical research face a series of choices that have consequential implications for the interpretability and value of the experimental results. These choices include: whether to use a factorial design, selection of the number and type of factors to include, how to address the compatibility of the different factors included, whether and how to avoid confounds between the type and number of interventions a participant receives, and how to interpret interactions. The use of factorial designs in clinical intervention research poses choices that differ from those typically considered in randomized clinical trial designs. However, the great information yield of the former encourages clinical researchers’ increased and careful execution of such designs.

Effects of motivation phase intervention components on quit attempts in smokers unwilling to quit: A factorial experiment

“…It is unclear why receipt of both components suppressed quit attempts. An advantage of the factorial experiment is that it can reveal such interactions; the causes of such interactions remain unclear (Baker, 2017;Baker et al, 2017).…”

Effects of nutritional supplements on the re-infection rate of soil-transmitted helminths in school-age children: A systematic review and meta-analysis

“…Hence, future studies could benefit by comparing the outcomes reported in truly RCT, cluster RCTs and factorial designs, as they are feasible to use on larger sample sizes [ 47 ]. While cluster RCTs have been reported to have more potential to detect treatment effects in the most affected groups within the clusters [ 48 , 49 ], the risk of bias in such studies should be detailed, exploring the differences in outcomes reported. Furthermore, each type of STH species is likely to react to nutritional supplements in distinctive ways.…”

Factorial Designs Help to Understand How Psychological Therapy Works

“…We propose the use of factorial and fractional factorial designs as an alternative methodological approach to standard comparative RCTs and component designs, which has advantages over both for resolving the active components of psychotherapy. Factorial experiments allow one to explore main effects of factors and interactions among factors (23)(24)(25)(26)(27).…”