title: Pseudo cluster randomization: balancing the disadvantages of cluster and individual randomization.
abstract: While designing a trial to evaluate a complex intervention, one may be confronted with the dilemma that randomization at the level of the individual patient risks contamination bias, whereas cluster randomization risks incomparability of study arms and recruitment problems. Literature provides only few solutions to this dilemma and these are not always feasible. As an alternative solution for this dilemma, we developed a new two-stage randomization method called pseudo cluster randomization. In the first stage, the clusters (e.g., recruiting physicians) are randomized into two groups: one group of clusters in which the majority of the participants (e.g., 80%) will receive the experimental treatment; one group of clusters in which the majority will receive the control condition. Following this, the second stage of the randomization involves randomly assigning participants within clusters in the proportions determined by the first stage. This has important advantages. Compared with cluster randomization the potential occurrence of baseline incomparability of treatment arms and poor recruitment is reduced, because the physicians who recruit the participants are unable to know in advance which treatment condition the next participant they recruit will be assigned to. Limiting the exposure of half of the physicians to the innovative intervention lowers risk of contamination bias. When this type of contamination bias is present, pseudo cluster randomization can be more efficient than individual or cluster randomization in that smaller number of study participants is needed to achieve a predefined power.
authors.affiliation: Department of Geriatric Medicine/Nijmegen Alzheimer Centre, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, The Netherlands. r.melis@ger.umcn.nl
authors.forename: René J F
authors.initials: RJ
authors.lastname: Melis
authors.forename: S
authors.initials: S
authors.lastname: Teerenstra
authors.forename: M G M
authors.initials: MG
authors.lastname: Olde Rikkert
authors.forename: G F
authors.initials: GF
authors.lastname: Borm
mesh_terms: D016000:Cluster Analysis; D035061:Control Groups; D006801:Humans; D011897:Random Allocation; D012107:Research Design; D015983:Selection Bias