Balancing Contamination and Referral Bias in a Randomized Clinical Trial: An Application of Pseudo-Cluster Randomization

Am J Epidemiol. 2015 Dec 15;182(12):1039-46. doi: 10.1093/aje/kwv132. Epub 2015 Dec 1.


In randomized trials of provider-focused clinical interventions, treatment allocation often cannot be blinded to participants, study staff, or providers. The choice of unit of randomization (patient, provider, or clinic) entails tradeoffs in cost, power, and bias. Provider- or clinic-level randomization can minimize contamination, but it incurs the equally problematic potential for referral bias; that is, because arm assignment of future participants generally cannot be concealed, differences between arms may arise in the types of patients enrolled. Pseudo-cluster randomization is a novel study design that balances these competing validity threats. Providers are randomly assigned to an imbalanced proportion of intervention-arm participants (e.g., 80% or 20%). Providers can be masked to the imbalance, avoiding referral bias. Contamination is reduced because only a minority of control-arm participants are treated by majority-intervention providers. Pseudo-cluster randomization was implemented in a randomized trial of a decision support intervention to manage depression among patients receiving human immunodeficiency virus care in the southern United States in 2010-2014. The design appears successful in avoiding referral bias (participants were comparable between arms on important characteristics) and contamination (key depression treatment indicators were comparable between usual care participants managed by majority-intervention and majority-usual care providers and were markedly different compared with intervention participants).

Keywords: clinical trials; contamination; pseudo-cluster randomization; referral bias; study design.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Cluster Analysis*
  • Humans
  • Randomized Controlled Trials as Topic / methods*
  • Referral and Consultation*
  • Selection Bias