Developing multicomponent interventions using fractional factorial designs.
TL;DR: This work considers the use of a screening study in the development of a multicomponent smoking cessation intervention, and addresses common criticisms and misconceptions regarding theUse of factorial designs in screening studies.
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Abstract: SUMMARY Multicomponent interventions composed of behavioral, delivery, or implementation factors in addition to medications are becoming increasingly common in health sciences. A natural experimental approach to developing and refining such multicomponent interventions is to start with a large number of potential components and screen out the least active ones. Factorial designs can be used efficiently in this endeavor. We address common criticisms and misconceptions regarding the use of factorial designs in these screening studies. We also provide an operationalization of screening studies. As an example, we consider the use of a screening study in the development of a multicomponent smoking cessation intervention. Simulation results are provided to support the discussions. Copyright q 2009 John Wiley & Sons, Ltd.
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Citations
From ideas to efficacy: The ORBIT model for developing behavioral treatments for chronic diseases.
Susan M. Czajkowski,Lynda H. Powell,Nancy E. Adler,Sylvie Naar-King,Kim D. Reynolds,Christine M. Hunter,Barbara A. Laraia,Deborah H. Olster,Frank M. Perna,Janey C. Peterson,Elissa S. Epel,Josephine E.A. Boyington,Mary E. Charlson +12 more
TL;DR: The ORBIT model provides a progressive, clinically relevant approach to increasing the number of evidence-based behavioral treatments available to prevent and treat chronic diseases.
963
Microrandomized trials: An experimental design for developing just-in-time adaptive interventions.
Predrag Klasnja,Eric B. Hekler,Saul Shiffman,Audrey Boruvka,Daniel Almirall,Ambuj Tewari,Susan A. Murphy +6 more
TL;DR: Microrandomized trials can help researchers understand whether their interventions are having intended effects, when and for whom they are effective, and what factors moderate the interventions' effects, enabling creation of more effective JITAIs.
Taxonomy of approaches to developing interventions to improve health: a systematic methods overview
Alicia O’Cathain,Liz Croot,Katie Sworn,Edward Duncan,Nikki Rousseau,Katrina M Turner,Lucy Yardley,Pat Hoddinott +7 more
TL;DR: An overview of approaches to intervention development can help researchers to understanding the variety of existing approaches, and to understand the range of possible actions involved in intervention development, prior to assessing feasibility or piloting the intervention.
Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research
TL;DR: An introduction to adaptive interventions and SMARTs is provided and the development of an adaptive intervention for optimizing weight loss among adult individuals who are overweight is discussed.
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Factorial Experiments: Efficient Tools for Evaluation of Intervention Components
TL;DR: Investigators in preventive medicine and related areas should begin considering factorial experiments alongside other approaches, and experimental designs should be chosen from a resource management perspective.
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Incomplete factorial designs for randomized clinical trials
TL;DR: Sometimes it may not be reasonable, interesting, feasible, or ethical to study all treatment combinations required in a complete or fractional factorial design, yet one may want to preserve some of the factorial structure to increase efficiency and to aid understanding.
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The Use and Analysis of Staggered Nested Factorial Designs
James R. Smith,Jackie M. Beverly +1 more
TL;DR: The classical nested design called for balanced replication at each level of the hierarchy, thus distributing the degrees of freedom of variance components as mentioned in this paper, is the one used in the present paper.
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•Journal Article
Optimal designs for mixed-effects models with two random nested factors
TL;DR: In this paper, a general class of designs for mixed-effects models with random nested factors, called assem-bled designs, where the nested factors are nested under the treatment combinations of the crossed factors, are presented.
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