Titre : | Screening Experiments and the Use of Fractional Factorial Designs in Behavioral Intervention Research. (2008) |
Auteurs : | Vijay NAIR ; . AIJUN ZHANG ; Bibhas CHAKRABORTY ; Angela FAGERLIN ; LITTLE (Roderick) : USA. Department of Biostatistics. School of Public Health. University of Michigan. Ann Arbor. ; Susan MURPHY ; Kenneth RESNICOW ; STRECHER (Victor) : USA. Department of Health Behavior and Health Education. School of Public Health. University of Michigan. Ann Arbor. ; Peter UBEL ; Center for Behavioral and Decision Sciences in Medicine. Division of General Internal Medicine. University of Michigan. Ann Arbor. USA ; Department of Statatics. University of Michigan. Ann Arbor. USA ; Va Health Services Research and Development. Center for Practice Management and Outcomes Research. Va Ann Arbor Healthcare System. Ann Arbor. USA |
Type de document : | Article |
Dans : | American journal of public health (vol. 98, n° 8, 2008) |
Pagination : | 1354-1359 |
Langues: | Anglais |
Mots-clés : | Dépistage ; Utilisation ; Comportement ; Homme |
Résumé : | [BDSP. Notice produite par INIST-CNRS D97R0xCm. Diffusion soumise à autorisation]. Health behavior intervention studies have focused primarily on comparing new programs and existing programs via randomized controlled trials. However, numbers of possible components (factors) are increasing dramatically as a result of developments in science and technology (e.g., Web-based surveys). These changes dictate the need for alternative methods that can screen and quickly identify a large set of potentially important treatment components. We have developed and implemented a multiphase experimentation strategy for accomplishing this goal. We describe the screening phase of this strategy and the use of fractional factorial designs (FFDs) in studying several components economically. We then use 2 ongoing behavioral intervention projects to illustrate the usefulness of FFDs. FFDs should be supplemented with follow-up experiments in the refining phase so any critical assumptions about interactions can be verified. |