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标题：Screening Experiments and the Use of Fractional Factorial Designs in Behavioral Intervention Research
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作者：Vijay Nair ; Victor Strecher ; Angela Fagerlin 等
期刊名称：American journal of public health
印刷版ISSN：0090-0036
出版年度：2008
卷号：98
期号：8
页码：1354-1359
DOI：10.2105/AJPH.2007.127563
语种：English
出版社：American Public Health Association
摘要：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. THE LANDSCAPE IN HEALTH behavior intervention studies is changing rapidly. Recent developments in science and technology have resulted in a dramatic increase in the available types and formulations of feasible interventions and in the ways in which interventions are delivered, messages are presented, data are collected, and so on. These advances, in turn, are leading to an explosion in the number of possible treatment components (or design factors) that can be studied. Traditional behavioral intervention studies are typically large-scale randomized controlled trials (RCTs) in which the goal is to confirm the superiority of a new program over an existing one. For example, such a trial might assess whether prostate cancer patients who receive a decision aid (e.g., an extensive online presentation about the disease) are better informed about their treatment options and more involved in their health care decisions than are patients not receiving a decision aid. Often in such trials, the new program consists of a combination of many interventions. Decision aids, for instance, contain many different components, each of which may influence the primary outcome variables. These confirmation trials do not provide direct information on which components are active and whether they have been set at optimal levels. Post hoc analyses based on non-randomized data are usually conducted to tease out this additional information. When RCTs are used to obtain this information, they usually involve adding or subtracting components one at a time or, at most, in small groups (e.g., 2 × 2 factorial designs). These studies can assess the impact of only a limited number of treatment components. By the time these findings are disseminated, the population of interest may have changed or the technology may be different (e.g., new communications media are in place or the population of interest has become more sophisticated), and as a result the conclusions may no longer be valid. All of these considerations suggest the need for alternative methodologies in health behavior research. Over the past 5 years, the Center for Health Communications Research, funded by the National Cancer Institute, has developed and implemented a multiphase experimentation strategy for systematically studying new interventions and confirming their superiority over existing ones. Adapted from a similar framework that has been successfully used in engineering applications for many years, 1 this “multiphase optimization strategy,” 2 as we have labeled it, consists of 3 phases—screening, refining, and confirming—involving separate randomized trials. The goal in the first phase is to “screen” a large set of potentially important treatment components quickly and efficiently and identify components that are in fact important. This is done through a screening experiment in which the effects of all components are examined simultaneously. Two-level fractional factorial designs (FFDs) are useful in accomplishing this goal economically. The Pareto principle—according to which only a small subset of the components and their interactions will be important—underlies the screening phase. Thus, many interactions can be excluded a priori, increasing the efficiency of the design. The second phase is aimed at refining understanding of the effects of the important components identified in the first phase. Existing knowledge or working assumptions need to be further examined and verified in follow-up experiments, which can untangle important effects, determine optimal “dosage” levels (i.e., appropriate levels of quantitative factors) via experiments with 3 or more levels, and so on. An optimal treatment program can be formulated from the information gained from this phase. The final phase consists of a confirmation trial designed to compare the new program with the gold standard and assess its advantages. Although this phase is similar to RCTs with 2 arms, the multiphase approach allows inclusion of only important components at their optimized levels. We focus on screening experiments and the use of FFDs in public health intervention research. We discuss the role of screening experiments in this context and illustrate the usefulness of FFDs. Factorial designs and FFDs have a long history. 3 ^– 6 They were originally developed in the context of agricultural applications and have since found widespread use in engineering. Here we provide an overview of FFDs and use 2 projects from our center to demonstrate their usefulness (more information about FFDs is available from standard textbooks 1 ^, 7 ^, 8 ). Successful use of FFDs relies on the principle of effect sparsity. There are 2 types of sparsity, one in which few factors are active and one in which higher order interactions are negligible. One can use existing knowledge (theory, experience, or empirical evidence) in formulating working assumptions about interactions. Results from the screening experiment will suggest which of these assumptions are critical, and suitable follow-up experiments must be conducted in the refining phase to determine which groups of interactions are “aliased” (as described later).