Abstract
It has been stated repeatedly in this book that the design of the optimization trial must be selected based on the resource management principle. This means that both the cost and scientific yield of a design must be evaluated and compared with that of other experimental designs under consideration. Every situation is different. In some situations, recruiting or retaining subjects may be very expensive, whereas in others, this is a relatively minor expense. Similarly, in some situations it is not difficult to implement a host of experimental conditions, whereas in others implementing a large number of conditions would be very costly. Experimental designs also differ with respect to the kind of resources they require. Some experimental designs require relatively few experimental conditions, but may require more subjects; others make very economical use of subjects, but require implementation of many experimental conditions. To add to the complexity, different experimental designs provide estimates of subtly different effects. This chapter attempts to provide the reader with the background needed to compare several different experimental designs that could be used in an optimization trial and to select the best one. Readers of this chapter should be familiar with the material in all previous chapters, particularly Chaps. 3 and 5.
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References
Almirall, D., Nahum-Shani, I., Wang, L., & Kasari, C. (2018). Experimental designs for research on adaptive interventions: Singly and sequentially randomized trials. In L. M. Collins & K. C. Kugler (Eds.), Optimization of behavioral, biobehavioral, and biomedical interventions: Advanced topics (forthcoming). New York, NY: Springer.
Baker, T. B., Collins, L. M., Mermelstein, R., Piper, M. E., Schlam, T. R., Cook, J. W., … Fiore, M. C. (2016). Enhancing the effectiveness of smoking treatment research: Conceptual bases and progress. Addiction, 111, 107–116.
Collins, L. M., Dziak, J. R., & Li, R. (2009). Design of experiments with multiple independent variables: A resource management perspective on complete and reduced factorial designs. Psychological Methods, 14, 202–224.
Collins, L. M., & Kugler, K. C. (Eds.). (2018). Optimization of multicomponent behavioral, biobehavioral, and biomedical interventions: Advanced topics. New York, NY: Springer.
Cook, J. W., Collins, L. M., Fiore, M. C., Smith, S. S., Fraser, D., Bolt, D. M., … Mermelstein, R. (2016). Comparative effectiveness of motivation phase intervention components for use with smokers unwilling to quit: A factorial screening experiment. Addiction, 111, 117–128.
Dziak, J. (2018). Optimizing the cost-effectiveness of a multicomponent intervention using data from a factorial experiment: Considerations, open questions, and tradeoffs among multiple outcomes. In L. M. Collins & K. C. Kugler (Eds.), Optimization of multicomponent behavioral, biobehavioral, and biomedical interventions: Advanced topics (forthcoming). New York, NY: Springer.
Dziak, J. D., Nahum-Shani, I., & Collins, L. M. (2012). Multilevel factorial experiments for developing behavioral interventions: Power, sample size, and resource considerations. Psychological Methods, 17, 153–175.
Gwadz, M. V., Collins, L. M., Cleland, C. M., Leonard, N. R., Wilton, L., Gandhi, M., … Ritchie, A. S. (2017). Using the multiphase optimization strategy (MOST) to optimize an HIV care continuum intervention for vulnerable populations: A study protocol. BMC Public Health, 17, 383.
Karaca-Mandic, P., Norton, E. C., & Dowd, B. (2012). Interaction terms in nonlinear models. Health Services Research, 47, 255–274.
Kugler, K. C., Wyrick, D. L., Tanner, A. E., Milroy, J. J., Chambers, B. D., Ma, A., ... & Collins, L. M. (2018). Using the multiphase optimization strategy (MOST) to develop an optimized online STI preventive intervention aimed at college students: Description of conceptual model and iterative approach to optimization. In L. M. Collins & K. C. Kugler (Eds.), Optimization of multicomponent behavioral, biobehavioral, and biomedical interventions: Advanced topics (forthcoming). New York, NY: Springer.
Nahum-Shani, I., & Dziak, J. J. (2018). Multilevel factorial designs in intervention development. In L. M. Collins & K. C. Kugler (Eds.), Optimization of multicomponent behavioral, biobehavioral, and biomedical interventions: Advanced topics (forthcoming). New York, NY: Springer.
Nahum-Shani, I., Dziak, J. J., & Collins, L. M. (2017). Multilevel factorial designs with experiment-induced clustering. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000128
Pellegrini, C. A., Hoffman, S. A., Collins, L. M., & Spring, B. (2014). Optimization of remotely delivered intensive lifestyle treatment for obesity using the multiphase optimization strategy: Opt-IN study protocol. Contemporary Clinical Trials, 38, 251–259.
Pellegrini, C. A., Hoffman, S. A., Collins, L. M., & Spring, B. (2015). Corrigendum to “optimization of remotely delivered intensive lifestyle treatment for obesity using the multiphase optimization strategy: Opt-IN study protocol”. Contemporary Clinical Trials, 45, 468–469.
Piper, M. E., Fiore, M. C., Smith, S. S., Fraser, D., Bolt, D. M., Collins, L. M., … Baker, T. B. (2016). Identifying effective intervention components for smoking cessation: A factorial screening experiment. Addiction, 111, 129–141.
Piper, M. E., Schlam, T. R., Fraser, D., Oguss, M., & Cook, J. W. (2018). Implementing factorial experiments in real-world settings: Lessons learned while engineering an optimized smoking cessation treatment. In L. M. Collins & K. C. Kugler (Eds.), Optimization of multicomponent behavioral, biobehavioral, and biomedical interventions: Advanced topics (forthcoming). New York, NY: Springer.
Rivera, D. E., Hekler, E. B., Savage, J. S., & Downs, D. S. (2018). Intensively adaptive interventions using control systems engineering: Two illustrative examples. In L. M. Collins & K. C. Kugler (Eds.), Optimization of multicomponent behavioral, biobehavioral, and biomedical interventions: Advanced topics (forthcoming). New York, NY: Springer.
Schlam, T. R., Fiore, M. C., Smith, S. S., Fraser, D., Bolt, D. M., Collins, L. M., … Baker, T. B. (2016). Comparative effectiveness of intervention components for producing long-term abstinence from smoking: A factorial screening experiment. Addiction, 111, 142–155.
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton, Boston, MA: Mifflin and Company.
Strecher, V. J., McClure, J. B., Alexander, G. W., Chakraborty, B., Nair, V. N., Konkel, J. M., … Pomerleau, O. F. (2008). Web-based smoking cessation programs: Results of a randomized trial. American Journal of Preventive Medicine, 34, 373–381.
Wu, C. F. J., & Hamada, M. S. (2009). Experiments: Planning, analysis, and optimization (2nd ed.). Hoboken, NJ: Wiley.
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Collins, L.M. (2018). Gathering Information for Decision-Making in the Optimization Phase: Resource Management and Practical Issues. In: Optimization of Behavioral, Biobehavioral, and Biomedical Interventions. Statistics for Social and Behavioral Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-72206-1_6
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