TY - JOUR
T1 - Randomization, design and analysis for interdependency in aging research
T2 - no person or mouse is an island
AU - Chusyd, Daniella E.
AU - Austad, Steven N.
AU - Dickinson, Stephanie L.
AU - Ejima, Keisuke
AU - Gadbury, Gary L.
AU - Golzarri-Arroyo, Lilian
AU - Holden, Richard J.
AU - Jamshidi-Naeini, Yasaman
AU - Landsittel, Doug
AU - Mehta, Tapan
AU - Oakes, J. Michael
AU - Owora, Arthur H.
AU - Pavela, Greg
AU - Rojo, Javier
AU - Sandel, Michael W.
AU - Smith, Daniel L.
AU - Vorland, Colby J.
AU - Xun, Pengcheng
AU - Zoh, Roger
AU - Allison, David B.
N1 - Funding Information:
We thank N. Baidwan for contributions to an early version of the paper. This work was supported in part by the National Institute on Aging (grants P30 AG050886; U24 AG056053), the Gordon and Betty Moore Foundation and the National Institute of Diabetes and Digestive and Kidney Diseases (grant P30 DK056336).
Publisher Copyright:
© 2022, Springer Nature America, Inc.
PY - 2022/12
Y1 - 2022/12
N2 - 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 of interdependency 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) single-stage individually randomized trials; (2) cluster-randomized controlled trials; (3) pseudo-cluster-randomized trials; (4) individually randomized group treatment; and (5) two-stage randomized designs. Although we present possible strategies for design and analysis to improve the rigor, accuracy and reproducibility of the science, we also acknowledge real-world 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.
AB - 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 of interdependency 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) single-stage individually randomized trials; (2) cluster-randomized controlled trials; (3) pseudo-cluster-randomized trials; (4) individually randomized group treatment; and (5) two-stage randomized designs. Although we present possible strategies for design and analysis to improve the rigor, accuracy and reproducibility of the science, we also acknowledge real-world 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.
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U2 - 10.1038/s43587-022-00333-6
DO - 10.1038/s43587-022-00333-6
M3 - Article
C2 - 37063472
AN - SCOPUS:85144642860
SN - 2662-8465
VL - 2
SP - 1101
EP - 1111
JO - Nature Aging
JF - Nature Aging
IS - 12
ER -