Randomization, design and analysis for interdependency in aging research: no person or mouse is an island

Abstract

Investigators traditionally use randomized designs and corresponding analysis procedures to make causal inferences about the effects of interventions, assuming independence between an individual's outcome and treatment assignment and the outcomes of other individuals in the study. Often, such independence may not hold. We provide examples o f i nt erdependency in model organism studies and human trials and group effects in aging research and then discuss methodologic issues and solutions. We group methodologic issues as they pertain to (1) singlestage individually randomized trials; (2) clusterrandomized controlled trials; (3) pseudo clusterrandomized trials; (4) individually randomized group treatment; and(5) twostage randomized designs. Although we present possible strategies for design and analysis to improve the rigor, accuracy and reproducibility of the science, we also acknowledge realworld constraints. Consequences of nonadherence, differential attrition or missing data, unintended exposure to multiple treatments and other practical realities can be reduced with careful planning, proper study designs and best practices.Investigators traditionally use randomized trials, or experiments, and corresponding analysis to make causal inferences about the effects of interventions, assuming independence between an individual's out come and treatment assignment and other individuals' outcomes in the study. In aging research, however, this assumption of independence is not always valid. Examples of interdependency include interference 1 , group composition effects 2 and clusters and nesting 3 . These issues require attention because they may violate the assumptions of causal