Accounting for Context in Randomized Trials after Assignment
Abstract
Many preventive trials randomize individuals to intervention condition which is then delivered in a group setting. Other trials randomize higher levels, say organizations, and then use learning collaboratives comprised of multiple organizations to support improved implementation or sustainment. Other trials randomize or expand existing social networks and use key opinion leaders to deliver interventions through these networks. We use the term contextually driven to refer generally to such trials (traditionally referred to as clustering, where groups are formed either pre-randomization or post-randomization — i.e., a cluster-randomized trial), as these groupings or networks provide fixed or time-varying contexts that matter both theoretically and practically in the delivery of interventions. While such contextually driven trials can provide efficient and effective ways to deliver and evaluate prevention programs, they all require analytical procedures that take appropriate account of non-independence, something not always appreciated. Published analyses of many prevention trials have failed to take this into account. We discuss different types of contextually driven designs and then show that even small amounts of non-independence can inflate actual Type I error rates. This inflation leads to rejecting the null hypotheses too often, and erroneously leading us to conclude that there are significant differences between interventions when they do not exist. We describe a procedure to account for non-independence in the important case of a two-arm trial that randomizes units of individuals or organizations in both arms and then provides the active treatment in one arm through groups formed after assignment. We provide sample code in multiple programming languages to guide the analyst, distinguish diverse contextually driven designs, and summarize implications for multiple audiences.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 09, 2022
- Source ID
- 10.1007/s11121-022-01426-9
Entities
People
- Alberto Valido
- Carlos Gallo
- Charles Hendricks Brown
- Daniel Almirall
- Donald Hedeker
- Guillermo Prado
- Inger Burnett-zeigler
- Naihua Duan
- Peter A. Wyman
- Robert D. Gibbons
- Sean D. Young
Organizations
- National Center for Advancing Translational Sciences
- National Institute of Arthritis and Musculoskeletal and Skin Diseases
- National Institute of Mental Health
- National Institute on Drug Abuse
- National Institute on Minority Health and Health Disparities
- United States Department of Defense