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Optimization of Multicomponent Behavioral and Biobehavioral Interventions for the Prevention and Treatment of HIV/AIDS

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Abstract

To move society toward an AIDS-free generation, behavioral interventions for prevention and treatment of HIV/AIDS must be not only effective, but also cost-effective, efficient, and readily scalable. The purpose of this article is to introduce to the HIV/AIDS research community the multiphase optimization strategy (MOST), a new methodological framework inspired by engineering principles and designed to develop behavioral interventions that have these important characteristics. Many behavioral interventions comprise multiple components. In MOST, randomized experimentation is conducted to assess the individual performance of each intervention component, and whether its presence/absence/setting has an impact on the performance of other components. This information is used to engineer an intervention that meets a specific optimization criterion, defined a priori in terms of effectiveness, cost, cost-effectiveness, and/or scalability. MOST will enable intervention science to develop a coherent knowledge base about what works and does not work. Ultimately this will improve behavioral interventions systematically and incrementally.

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Notes

  1. To save space, we will use the word “effectiveness” to refer to either efficacy or effectiveness where it is not necessary to distinguish between the two.

  2. We subscribe to the definition of the role of pilot testing in intervention science that has been outlined by Leon et al. [68]: “The fundamental purpose of conducting a pilot study is to examine the feasibility of an approach that is intended to ultimately be used in a larger scale study… A pilot study… is not used for hypothesis testing” (p. 626).

  3. Everything said about factorial experiments in this article assumes that the data from the experiment are to be analyzed in a standard factorial analysis of variance (ANOVA) using effect coding rather than dummy coding. We strongly recommend the use of effect coding, which, unlike dummy coding, produces estimates of main effects and interactions that are always consistent with the classic definitions found in most statistics textbooks; see Collins et al. [3].

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Acknowledgments

The word described herein was supported by Grant R03 HD079711 from the Eunice Kennedy Shriver Institute for Child Health and Human Development; grants P50 DA010075, R01 DA032083, and P30 DA011041 from the National Institute on Drug Abuse; grant R01 DK097364 from the National Institute for Diabetes and Digestive and Kidney Diseases; and grant P01 CA180945 from the National Cancer Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the institutes mentioned above.

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Authors and Affiliations

  1. Department of Human Development and Family Studies and The Methodology Center, The Pennsylvania State University, 404 Health & Human Development Building, University Park, PA, 16802, USA

    Linda M. Collins

  2. The Methodology Center, The Pennsylvania State University, University Park, PA, USA

    Kari C. Kugler

  3. Center for Drug Use and HIV Research (CDUHR), College of Nursing, New York University, New York, NY, USA

    Marya Viorst Gwadz

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Collins, L.M., Kugler, K.C. & Gwadz, M.V. Optimization of Multicomponent Behavioral and Biobehavioral Interventions for the Prevention and Treatment of HIV/AIDS. AIDS Behav 20 (Suppl 1), 197–214 (2016). https://doi.org/10.1007/s10461-015-1145-4

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