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Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making 8 Opportunities to Generate Evidence KEY MESSAGES The evidence base to inform decisions about obesity prevention is extremely limited, even taking into account the expanded view of evidence proposed in this report (Chapter 5). Each step in the L.E.A.D. framework can provide important insights about the types of evidence that are needed. In obesity prevention–related research, the evaluation of ongoing and emerging initiatives is of particular priority. Future research on obesity prevention, and in public health more generally, can employ a broad array of study designs that support valid inferences about the effects of policies and programs without experimentation, promoting transdisciplinary exchange. Published peer-reviewed reports on the results of obesity prevention efforts often lack useful information related to generalizability to other individuals, settings, contexts, and time frames, adding to the problem of incomplete evidence for decision making. If obesity prevention actions must be taken when evidence is limited, the L.E.A.D. framework calls for developing credible evidence about those actions for use in decision making about future efforts, including the use of natural experiments, and emerging and ongoing programs through “evaluability assessments” and continuous quality assessments. The preceding chapter outlines a standard for assembling evidence that requires access to potentially useful sources of information; the blending of theory, expert opinion, experience, and local wisdom; the availability of scientifically trained staff or colleagues skilled in using these resources; a time window that allows for the process
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Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making of locating, evaluating, and assembling evidence; and, most important, the existence of relevant evidence. Use of the L.E.A.D. framework to broaden what is considered useful, high-quality evidence and to gradually increase the availability of such evidence will help in attaining this standard. Together with the importance of taking a systems approach and making the best possible use of diverse types of evidence that are relevant to the user’s perspective and the question being asked, a major emphasis of this report is the urgent need to generate more evidence to inform efforts to address obesity prevention and other complex public health problems. Much of what is called for in the framework will meet the expectations and needs of decision makers when sufficient evidence exists, although clearly such is not yet the case (see Chapter 3). On the other hand, failure to find relevant evidence may either (erroneously) reinforce the perception that effective interventions cannot be identified, or increase skepticism or resistance on the part of decision makers—who must proceed in the interim—with respect to the utility of incorporating evidence into their decision-making process. As described in Chapter 7, when decision makers face choices that must be made and actions that must be taken in the relative absence of evidence, or at least on the basis of inconclusive, inconsistent, or incomplete evidence, the L.E.A.D. framework calls for critical evaluation and systematic building on experience in a continuous translation, action, monitoring, surveillance, and feedback loop (i.e., matching, mapping, pooling, and patching; see Appendix E for more detail). The purpose of this chapter is to motivate researchers and others whose primary role is to generate (i.e., support, fund, publish) evidence to adopt the L.E.A.D. framework as a guide to identifying and generating the types of evidence that are needed to support decision making on obesity prevention and other complex public health problems (see Figure 8-1). The focus is on evidence related to “What” and “How” questions, that is, evidence demonstrating the types of interventions that are effective in various contexts; what their impacts are; and which are relatively more or less effective, whether they are associated with unexpected benefits or problems, and what practical issues are involved in their implementation. As noted in Chapter 3 and defined in Chapter 5, these concerns point to areas in which a lack of evidence is most likely to be problematic. This chapter also anticipates a cycle that begins with planning from incomplete evidence, blended with theory, expert opinion, experience, and local wisdom (see Chapter 7), and ends with evaluating the consequences of interventions. Such a cycle, in turn, may produce the most credible evidence for other jurisdictions seeking practical models to emulate. The chapter begins by briefly reviewing existing evidence needs and outlining the need for new directions in evidence generation and transdisciplinary exchange. It then addresses the limitations in the way evidence is reported in scientific journals and the need to take advantage of natural experiments and emerging and ongoing interventions as sources of practice-based evidence to fill the gaps in the best available evidence. The chapter concludes with a discussion of alternatives to randomized
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Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making FIGURE 8-1 The Locate Evidence, Evaluate Evidence, Assemble Evidence, Inform Decisions (L.E.A.D.) framework for obesity prevention decision making. NOTE: The element of the framework addressed in this chapter is highlighted. experiments, with a focus on the level of certainty of the causal relationship between an intervention and the observed outcomes. EXISTING EVIDENCE NEEDS The generation of evidence related to interventions and their effectiveness can be approached through evaluation research, where evaluation is defined as “a systematic assessment of the quality and effectiveness of an initiative, program, or policy…” (IOM, 2007, p. 26). As explained in Chapter 1, the 2007 Institute of Medicine (IOM) report Progress in Preventing Childhood Obesity, which is current through 2006, strongly emphasizes the need for evaluation of ongoing and emerging initiatives. The committee that produced the report found that, in response to the urgency of the problem of childhood obesity with respect to prevalence and economic costs, numerous efforts were being undertaken on the basis of what was already known from theory or practice. Given the inherent relevance of implemented programs to natural settings, such spontaneous or endogenous interventions may be of most interest to
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Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making decision makers. However, many of these programs are not evaluated. Issues for consideration in increasing the use and appropriateness of such evaluation, taken from the 2007 IOM committee’s assessment, are shown in Box 8-1. A working group convened by the National Heart, Lung, and Blood Institute of the National Institutes of Health (NIH) also has made several recommendations for research needs related to the prevention of child obesity, many of which are directly relevant to the types of population-based approaches addressed in this report (Pratt et al., 2008). Several of these recommendations are listed in Box 8-2. Consistent with the issues highlighted by the 2007 IOM committee, the NIH recommendations include the evaluation of existing promising programs, as well as the conduct of studies of multilevel and multicomponent interventions. The examples in Box 8-2 relate specifically to child obesity. However, the need for more evidence relevant to decision making is also recognized with respect to obesity in adults and other complex public health problems. The Congressional Budget Office (CBO), for example, has identified several areas in which having more evidence would be helpful. CBO found some evidence on net cost reductions for certain disease management programs, but it was unclear whether these strategies could be replicated or applied in a broader population. In general, CBO found the availability of clinical and economic research assessing and comparing treatments for preventive services to be limited (CBO, 2008). Box 8-1 Considerations for Increasing Evaluation of Obesity Prevention Initiatives Evaluation is often not a priority for individuals and organizations that are developing a new policy, program, or intervention. The time and resources needed to ensure appropriate baseline and outcome measures may not be available or allocated. Evaluation may seem too technically complex. Evaluation may not be identified as a responsibility of those undertaking the initiative. Evaluation outcomes are not always matched to the nature and stage of the program; e.g., intermediate outcomes such as change in food intake, physical activity, or TV viewing may be more appropriate than BMI (body mass index) for short-term programs. Evaluations of multiple, linked programs require collaborative efforts and systems approaches. SOURCE: IOM, 2007, pp. 27-28.
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Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making Box 8-2 Selected Recommendations for Research in Childhood Obesity Prevention Settings Test interventions with physicians and other health care providers, combined with community involvement (e.g., train physicians to screen, nurses to be coaches, and health care settings to refer to community resources) (for young children). Conduct interventions in a variety of settings (e.g., home; child care; the U.S. Department of Agriculture’s Special Supplemental Nutrition Program for Women, Infants, and Children [WIC]; and health care settings) (for young children). Examine multilevel and multicomponent, community-based interventions in multiple settings (e.g., schools, health care, home, community, built environment, public policy, social marketing, diet, physical activity behaviors). Test a multilevel, comprehensive intervention that targets minority and low-income populations (e.g., culturally appropriate ways to reach Latino, African-American, Native American, and Asian/Pacific Islander children). Develop and test interventions that can be incorporated effectively into existing school and community infrastructures (e.g., curriculum, physical activity, school lunch programs) to maximize effectiveness and minimize cost. Conduct intervention studies that address issues related to the interface between individual behaviors and the environment. Implementation, Dissemination, Translation, Evaluation Identify and test approaches for community partnerships in the dissemination and implementation of evidence-based obesity-prevention programs. Evaluate the effectiveness of existing promising programs. Methodology Support methodological research on study designs and analytic approaches (identify optimal study designs and analytic approaches for various types of research questions). Use appropriate study designs and methods, including natural experiments, quasi-experimental designs, and randomized designs; develop time-sensitive funding mechanisms for natural experiments. High-Risk Populations Study a diversity of high-risk and understudied subgroups, including low-income families, ethnically and socioeconomically diverse populations, boys, and children in rural communities, as well as immigrants. Conduct environmental and policy intervention research to improve access to healthy foods and the opportunity for physical activity in low-income communities. Other Recommendations Analyze effectiveness–intervention studies for their cost-effectiveness. SOURCE: Adapted and reprinted from Pratt et al., Copyright © 2008, with permission from Elsevier.
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Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making THE NEED FOR NEW DIRECTIONS AND TRANSDISCIPLINARY EXCHANGE The two methodological recommendations in Box 8-2 highlight one of the major problems encountered by those who wish to generate policy- or program-relevant evidence regarding population health problems such as obesity. These recommendations refer to the need to identify optimal and appropriate study designs, including designs that will allow for timely assessment of initiatives that constitute “natural experiments” (detailed later in this chapter); quasi-experimental designs are mentioned in addition to randomized designs. One major question raised by these recommendations relates to the prevailing preference for randomized controlled trials (RCTs) in biomedical science because of their advantages for drawing causal inferences: Are there good alternatives to randomized designs that will accomplish the same thing but can be implemented more flexibly in natural or field settings? (West et al., 2008). A related question, also reflected in the considerations from the 2007 IOM report and the NIH recommendations, is the complexity of many interventions conducted in the community: To what degree do the multiple components of interventions need to be separated to isolate their causal effects? In some, perhaps many, cases, the separation may be artificial—the effectiveness of each component may be dependent on the others, and the full treatment package may be of primary interest (West and Aiken, 1997). In other cases, components may need to be deleted because of ineffectiveness or iatrogenic effects or for cost-effectiveness reasons. Chapter 4 provides a fuller discussion of these conceptual issues. A more general issue is how experts who are trained in a particular research approach can expand their capabilities for addressing issues that can be well studied only with different, less familiar approaches. For example, how do biomedical researchers, who have traditionally conducted the research on obesity, become conversant with methods for evaluating evidence that may be available from other fields and with the experts, or expertise, in implementing those methods? How do researchers in fields other than biomedicine—for example, education, community design, or economics—become involved and expert in studying problems, such as obesity, some aspects of which do not lend themselves to their typical methods? And how can obesity researchers benefit from the scholarship of colleagues who have focused on different, similarly complex public health problems, such as tobacco control? Transdisciplinary exchange refers to researchers’ use of a “shared conceptual framework drawing together disciplinary-specific theories, concepts, and approaches to address a common problem” (Rosenfield, 1992, p. 1351). Both the medical and social sciences are challenged to expand their notion of methods and study designs that can inform the study of obesity prevention and other population health problems by considering contextual concerns (social, economic, environmental, institutional, and political factors) that influence health outcomes. For example, NIH’s National Institute on Aging has supported a network or team approach to studying the “biological, psychological and social pathways to positive and not-so-positive health” (Kessel
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Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making and Rosenfield, 2008, p. S229); the result has been enhanced quality of published research that crosses disciplinary lines. Funding agencies such as NIH have therefore been encouraged to continue to support this type of research and urge researchers to value collaboration and partnerships in a variety of fields. Stimulated by a 2006 National Cancer Institute conference that highlighted the need for transdisciplinary research in health, Kessel and Rosenfield (2008) describe a number of factors that have both facilitated and constrained transdisciplinary science (Table 8-1). The authors identified these factors from research programs that have successfully crossed disciplinary boundaries. Reflecting the type of transdisciplinary TABLE 8-1 Factors Facilitating and Constraining Transdisciplinary Team Science Factor Facilitating Constraining Focus on major problems PIs able to bring researchers together across disciplines and program-unifying themes Some areas seen as unrealistic Lack of integrative research framework Few “how-to” models Team members Possess complementary and intersecting skills Able to develop common language Positive open attitude Appreciative of others’ knowledge Shared understanding of scientific problem Mutual trust and respect Open to mentoring See skills as competitive Tension between solo and collaborative work Power–prestige differences social and medical sciences Worry about diffusion of focus and loss of identity Research seen as time-consuming/multiple projects Disincentive for practitioners Sharing credit affects promotion, tenure, publications, funding Training Complementary training Mentored graduate students to participate in transdisciplinary research team SERCA grants for training in new field Historical barriers across fields Location of departments Funding limited Institutions Support, promote, and fund centers, networks, and teams across disciplines, departments, and medical and social science facilities on same campus Rigid university policies Centers lack funds Technology Facilitate communication even when teams and researchers physically dispersed Funding Foundations and government support network/team approach (e.g., MacArthur, NIH) Grant applications more challenging, time-consuming Publication Journals discourage multiple authors Peer review hard to judge Need to frame more narrowly NOTE: NIH = National Institutes of Health; PI = principal investigator; SERCA = Special Emphasis Research Career Award. SOURCE: Reprinted from Kessel and Rosenfield, Copyright © 2008, with permission of Elsevier; and Kessel and Rosenfield, page 401—Table 19.3, “Fostering interdisciplinary innovation: The way forward” from “Expanding the boundaries of health and social science: Case studies of interdisciplinary innovation” (2003), by permission of Oxford University Press, Inc.
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Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making exchange that is needed among researchers, the final section of this chapter examines alternatives to randomized experiments from the perspective of evaluation experts in the behavioral sciences, economometrics, education, public health, and statistics and explains why these alternatives are particularly important for obesity prevention (a more detailed discussion of selected study designs is offered in Appendix E). LIMITATIONS IN THE WAY EVIDENCE IS REPORTED IN SCIENTIFIC JOURNALS Decision makers (e.g., policy makers, professional caregivers, public health officials, and advocates) have concerns beyond scientists’ certainty of causal relationships in judging the utility and persuasiveness of evidence (see Chapter 7). All of these concerns fall to some degree under the broad rubric of generalizability (Garfield et al., 2003; Glasgow et al., 2003, 2006; Green, 2001; Green and Glasgow, 2006; Green and Lewis, 1981; Green et al., 1980; Shadish et al., 2002, Chapters 11-13). As described in Chapter 5, the generalizability of a study refers to the degree to which its results can be expected to apply equally to other individuals, settings, contexts, and time frames. The generalizability of evidence-based practices is seldom considered in individual studies or systematic reviews of the evidence, but is often decisive in decision makers’ adoption of research evidence for practical purposes such as obesity prevention. Some authors have begun to make a regular practice of noting the “applicability” of findings, with caveats when the range of populations or settings in which the evidence from trials was derived is notably limited (Shepherd and Moore, 2008). Others have proposed combining reviews of clinical and community evidence in a more “ecologically comprehensive” (multimethod, multilevel) approach to the use of evidence in such areas as tobacco and obesity control (Ockene et al., 2007). And some journals have begun to make the generalizability of a study more of an issue in considering manuscripts (e.g., Eriksen, 2006; Steckler and McLeroy, 2008), on occasion with qualifications and noted constraints (Patrick et al., 2008). The usual manner of reporting results of obesity prevention efforts in journals often adds to the problem of incomplete evidence because useful aspects of an intervention and research related to its generalizability are not discussed. At a meeting of the editors of 13 medical and health journals, several agreed to make individual and joint efforts to devote more attention to issues of a study’s generalizability and to press for more reporting of (1) recruitment and selection procedures, participation rates, and representativeness at the levels of individuals, intervention staff, and delivery settings; (2) the level of consistency with which the intervention being tested was implemented in the study; (3) the impact on a variety of outcomes, especially those important to patients, clinicians, program cost, and adverse consequences; and (4) in follow-up reports, attrition of subjects in the study at all levels, long-term effects on outcomes, and program institutionalization, modification, or discontinuation (Green et al., 2009; supported by Cook and Campbell, 1979, pp. 74-80; Glasgow et al., 2007; Shadish et al., 2002). These moves by journal editors relevant to obesity control
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Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making hold promise for the greater relevance and usefulness of the published scientific literature for other decision makers, responding to some of their major concerns. Specifically in childhood obesity prevention, Klesges and colleagues (2008) examined studies published between 1980 and 2004 that were controlled, long-term research trials with a behavioral target of either physical activity or healthful eating or both, together with at least one anthropometric outcome. Using review criteria for a study’s generalizability to other individuals, settings, contexts, and time frames (external validity) developed by Green and Glasgow (2006), they found that all of the 19 publications that met their selection criteria lacked full reporting on the 24 dimensions of external validity expected in an optimal paper to enable users to judge potential generalizability (see Box 8-3). Median reporting over all elements was 34.5 percent; the mode was 0 percent with a range of 0 percent to 100 percent. Only four dimensions (descriptions of the target audience and target setting, inclusion–exclusion criteria, and attrition rate) were reported in at least 90 percent of the studies. Most infrequent were reports of setting-level selection criteria and representativeness, characteristics of intervention staff, implementation of intervention content, costs, and program sustainability. These limitations of individual studies are also seen and sometimes multiplied in systematic reviews, such as meta-analyses, of whole bodies of literature. The cumulative problems of inadequate reporting of sampling, settings, and interventions have been noted, for example, in meta-analyses of the patient education literature on preventive health interventions in clinical settings (Simons-Morton et al., 1992; Tabak et al., 1991). These findings provide strong support for the conclusion of Klesges and colleagues (2008) that the aspects of generalizability that potential users need most to see reported more thoroughly in the published evidence are the “3 Rs”: the representativeness of participants, settings, and intervention staff; the robustness of the intervention across varied populations and staffing or delivery approaches; and the replicability of study results in other places. The specific questions most decision makers will have within these broad categories relate to cost (affordability); scalability; and acceptability in particular populations, times, and settings. Even with more complete reporting on these issues of a study’s generalizability to other populations, gaps will inevitably remain. RCTs can never fill all of the cells in a matrix of potentially relevant evidence representing all combinations of a study’s dimensions of generalizability: population × setting × intervention × time × staffing × other resources. The empty cells in such a matrix require potential users of evidence to make inferential leaps or more studied extrapolations from the existing coverage of the evidence to their own population, setting, intervention, time, staffing, and other resources. In short, users should bring to bear on their decisions their own theories or assumptions about the fit of the evidence to their situation, which will vary along each of the above dimensions. A particular challenge for obesity prevention, as in some other areas of chronic disease control, is the multiplicity and complexity of these dimensions. For each
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Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making Box 8-3 Percentage of 19 Studies Reporting External Validitya Dimensions, 1980-2004 External Validity Dimensionb Percent Reporting Reach and Representativeness Individual Participants Description of targets 100 Individual inclusion/exclusion criteria 90 Participation rate 63 Representativeness 10 Setting Description of targets 95 Setting inclusion/exclusion criteria 11 Participation rate 22 Representativeness 0 Delivery Staff Participation rate 5 Implementation and Adaptation Consistency of implementation 26 Staff expertise or training 89 Variations in implementation by staff 5 Program adaptation 42 Outcomes of Decision Making Outcomes compared with goal 37 Adverse consequences 32 Moderation of effect by participant characteristic(s) 53 Moderation of effect by staff/setting 10 Number of sessions or time needed to deliver intervention 68 Costs 0 Maintenance and Institutionalization Long-term effects (at least 12 months) 74 Program sustainability 0 Attrition rate 100 Differential attrition by condition tested 21 Drop-out representativeness 42 a External validity is defined according to Leviton (2001). b See Green and Glasgow (2006) for a detailed description of coding dimensions. SOURCE: Reprinted from Klesges et al., Copyright © 2008, with permission from Elsevier.
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Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making intervention, for example, there will be a multiplicity of points of intervention, from individuals to families, social groups, organizations, whole communities, regions, or states, each calling for different sources or types of evidence. Complexity will further confound the translation of evidence with respect to the projected time frame, even across the lifespan, to account for variations in interventions that make them age-appropriate and account for such relevant background variables as media coverage of the issue, social discourse, and changing social norms. WAYS TO FILL THE GAPS IN THE BEST AVAILABLE EVIDENCE This section examines the need to take advantage of natural experiments and emerging and ongoing interventions as sources of practice-based evidence to compensate for the fundamental limitations of even the best available evidence. Natural Experiments as Sources of Practice-Based Evidence Rather than waiting for the funding, vetting, implementation, and publication of RCTs (and other forms of research) to answer practical and locally specific questions, one alternative is to treat the myriad programs and policies being implemented across the country as natural experiments (Ramanathan et al., 2008). Applying more systematic evaluation to interventions as they emerge, even with limited experimental control over their implementation, will yield more immediate and practice-based evidence for what is possible, acceptable, and effective in real-world settings and populations. What makes such evaluation of these natural experiments even more valuable is that it produces data from settings that decision makers in other jurisdictions can view as more like their own than the settings of the typical published trials. Seeing how other state and local jurisdictions are performing in a given sphere of public concern also may activate competitive instincts that spur state and local decision makers to take action. The Centers for Disease Control and Prevention’s (CDC’s) Office on Smoking and Health used this strategy of making comparisons across jurisdictions when California and Massachusetts raised cigarette taxes and launched tax-based, comprehensive statewide tobacco control programs (see Chapter 4). Noting the accelerated rates of reduced tobacco consumption in those states compared with the other 48 states, CDC collaborated with the two states to evaluate these natural experiments and analyze their components (e.g., mass media, school programs) and the associated costs. CDC then offered the per capita costs of each component as a recommended basis for budgeting for other states wishing to emulate these successful programs (CDC, 1999). Many of the states used these budgetary allocations, at least temporarily before the tobacco settlement funds from the tobacco industry were diverted to general revenue or other purposes.
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Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making TABLE 8-2 Key Assumptions/Threats to Internal Validitya and Example Remedies for Randomized Controlled Trials and Alternatives Assumption or Threat to Internal Validity Approaches to Mitigating the Threat to Internal Validity Design Approach Statistical Approach Randomized controlled experiment Independent units Temporal or geographical isolation of units Multilevel analysis (other statistical adjustment for clustering) Full treatment adherence Incentives for adherence Instrumental variable analysis (assume exclusion restriction) No attrition Sample retention procedures Missing data analysis (assume missing at random) Other treatment conditions do not affect participant’s outcome (SUTVA) Temporal or geographical isolation of treatment groups Statistical adjustment for measured exposure to other treatments Randomized encouragement design Exclusion restriction No design approach yet available Sensitivity analysis Regression discontinuity design Functional form of the relationship between assignment variable and outcome is properly specified Replication with different threshold; nonequivalent dependent variable Nonparametric regression; sensitivity analysis Interrupted time series analysis Functional form of the relationship for the time series is properly specified; another historical event, a change in population (selection), or a change in measures coincides with the introduction of the intervention Nonequivalent control series in which intervention is not introduced; switching replication in which intervention is introduced at another time point; nonequivalent dependent measure Diagnostic plots (autocorrelogram; spectral density); sensitivity analysis Observational study Measured baseline variables equated; unmeasured baseline variables equated; differential maturation; baseline variables reliably measured Multiple control groups; nonequivalent dependent measures; additional pre- and postintervention measurements Propensity score analysis; sensitivity analysis; subgroup analysis; correction for measurement error a Internal validity is defined as a study’s level of certainty of the causal relationship between an intervention and the observed outcomes. NOTE: SUTVA = stable unit treatment value assumption. The list of assumptions and threats to internal validity identifies issues that commonly occur in each of the designs. The alternative designs may be subject to each of the issues listed for the randomized conrolled trial in addition to the issues listed for the specific design. The examples of statistical and design approaches for mitigating the threat to internal validity illustrate some commonly used approaches and are not exhaustive. For the observational study design, the potential outcomes and Campbellian frameworks study differ so that the statistical and design approaches do not map 1-to-1 onto the assumptions or threats to internal validity that are listed. SOURCE: West et al., 2008. Reprinted with permission. West et al., Alternatives to the randomized controlled trial, American Journal of Public Health, 98(8):1364, Copyright © 2008 by the American Public Health Association.
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Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making ness of individual components in multiple-component designs. West and Aiken (1997) and MacKinnon (2008) also consider the statistical method of mediational analysis that permits researchers to probe these issues, although in a less definitive manner. Rubin’s Perspective Rubin’s potential outcomes model takes a formal mathematical/statistical approach to causal inference. Building on earlier work by Splawa-Neyman (1990), it emphasizes precise definition of the desired causal effect and specification of explicit, ideally verifiable assumptions that are sufficient to draw causal inferences for each research design. Rubin defines a causal effect as the difference between the outcomes for a single unit (e.g., person, community) given two different well-defined treatments at the identical time and in the same context. This definition represents a conceptually useful ideal that cannot be realized in practice. Holland (1986) notes that three approaches, each with its own assumptions, can be taken to approximate this ideal. First, a within-subjects design can be used in which the two treatments (e.g., intervention, control) are given to the same unit. This design assumes (1) temporal stability in which the same outcome will be observed regardless of when the treatment is delivered and (2) causal transience in which the administration of the first treatment has no effect on the outcome of the second treatment. These assumptions will frequently be violated in research on obesity. Second, homogeneous units can be selected or created so that each unit can be expected to have the same response to the treatment. This strategy is commonly used in engineering applications, but raises concern about the comparability of units in human research—even monozygotic twins raised in similar environments can differ in important ways in some research contexts. The matching procedures used in the potential outcomes approach discussed below rely on this approach; they assume that the units can indeed be made homogeneous on all potentially important background variables. Third, units can be randomly assigned to treatment and control conditions. This strategy creates groups that are, on average, equal on all possible background variables at pretest so that the difference between the means of the two groups now represents the average causal effect. This strategy makes several assumptions (see Table 8-2; Holland, 1986; West and Thoemmes, 2010), including full treatment adherence, independence of units, no attrition from posttest measurement, and the nondependence of the response of a unit to a treatment on the treatment received by other units (or SUTVA, the stable unit treatment value assumption). The SUTVA highlights the challenges in community research of considering possible dependence between units and possible variation in each treatment across sites (Rubin, 2010). Well-defined treatment (and no-treatment) conditions that are implemented identically across units are a key feature of strong causal inference in Rubin’s perspective. Hernan and Traubman (2008) discuss the importance of this assumption in the context of obesity research. Beyond requiring this set of foundational assumptions, randomization has another subtle effect: it shifts the focus from a causal effect defined at the level of the individual to an
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Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making Box 8-4 Design Elements Used in Constructing Quasi-Experiments Assignment (control of assignment strategies to increase group comparability) Cutoff-based assignment: controlled assignment to conditions based solely on one or more fully measured covariates; this yields an unbiased effect estimate. Other nonrandom assignment: various forms of “haphazard” assignment that sometimes approximate randomization (e.g., alternating assignment in a two-condition quasi-experiment whereby every other unit is assigned to one condition). Measurement (use of measures to learn whether threats to causal inference actually operate) Posttest observations Nonequivalent dependent variables: measures that are not sensitive to the causal forces of the treatment, but are sensitive to all or most of the confounding causal forces that might lead to false conclusions about treatment effects (if such measures show no effect, but the outcome measures do show an effect, the causal inference is bolstered because it is less likely due to the confounds). Multiple substantive posttests: used to assess whether the treatment affects a complex pattern of theoretical predicted outcomes. Pretest observations Single pretest: a pretreatment measure on the outcome variable, useful to help diagnose selection bias. Retrospective pretest: reconstructed pretests when actual pretests are not feasible—by itself, a very weak design feature, but sometimes better than nothing. Proxy pretest: when a true pretest is not feasible, a pretest on a variable correlated with the outcome—also often weak by itself. Multiple pretest time points on the outcome: helps reveal pretreatment trends or regression artifacts that might complicate causal inference. Pretests on independent samples: when a pretest is not feasible on the treated sample, one is obtained from a randomly equivalent sample. Complex predictions such as predicted interaction: successfully predicted interactions lend support to causal inference because alternative explanations become less plausible. average causal effect that characterizes the difference between the treatment and control groups. The ability to make statements about individual causal effects, important in many clinical and health contexts, is diminished without additional assumptions being made (e.g., the causal effect is constant for all individuals). A key idea in Rubin’s perspective is that of possible outcomes. The outcome of a single unit (participant) receiving a treatment is compared with the outcome that would have occurred if the same unit had received the alternative treatment. This idea has proven to be a remarkably generative way of thinking about how to obtain precise estimates of causal effects. It focuses the researcher on the precise nature of the comparison that needs to be made and clearly delineates the participants for whom the comparison is appropriate. It provides a basis for elegant solutions to such problems as treatment nonadherence and appropriate matching in nonrandomized studies (West and Thoemmes, 2010).
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Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making Measurement of threats to internal validity:a helps diagnose the presence of specific threats to the inference that A caused B, such as whether units actively sought out additional treatments outside the experiment. Comparison groups (selecting comparisons that are “less nonequivalent” or that bracket the treatment group at the pretest[s]) Single nonequivalent groups: compared with studies without control groups, using a nonequivalent control group helps identify many plausible threats to validity. Multiple nonequivalent groups: serve several functions; for instance, groups are selected that are as similar as possible to the treated group, but at least one outperforms it initially and at least one underperforms it, thus bracketing the treated group. Cohorts: comparison groups chosen from the same institute in a different cycle (e.g., sibling controls in families or last year’s students in schools). Internal (versus external) controls: plausibly chosen from within the same population (e.g., within the same school rather than from a different school). Treatment (manipulations of the treatment to demonstrate that treatment variability affects outcome variability) Removed treatments: shows that an effect diminishes if treatment is removed. Repeated treatments: reintroduces treatments after they have been removed from some group—common in laboratory science. Switched replications: treatment and control group roles are reversed so that one group is the control while the other receives treatment, but the controls receive treatment later, whereas the original treatment group receives no further treatment or has treatment removed. Reversed treatments: provides a conceptually similar treatment that reverses an effect—for example, reducing access for some students to a computer being studied by increasing access for others. Dosage variation (treatment partitioning): demonstrates that an outcome responds systematically to different levels of treatment. a Internal validity denotes the level of certainty of the causal relationship between an intervention and the observed outcomes. SOURCE: Reprinted, with permission. Shadish and Cook, 1999. Copyright © 1999 by the Institute of Mathematical Statistics. Other Perspectives on Causality Other perspectives on causal inference exist. In economics, Granger (1988; Granger and Newbold, 1977; see also Bollen, 1989, in sociology and Kenny, 1979, in psychology) argues that three conditions are necessary to infer that one variable X causes changes in another variable Y: Association—The two variables X and Y must be associated (nonlinear association is permitted). Temporal precedence—X must precede Y in time. Nonspuriousness—X contains unique information about Y that is not available elsewhere. Otherwise stated, with all other causes partialed out, X still predicts Y.
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Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making This perspective can be particularly useful for thinking about causal inference when X cannot be manipulated (e.g., age), but is typically less useful for studying the effects of interventions. The primary challenge is how one can establish nonspuriousness, and Granger’s perspective provides few guidelines in this regard relative to the Campbell and Rubin perspectives. A second class of perspectives, founded in philosophical and computer science, takes a graph theoretic approach. In this approach, a complex model of the process is specified and is compared with data. If the model and its underlying assumptions are true, the approach can discern whether causal inferences can be supported. Within this approach, a computer program known as Tetrad (Spirtes et al., 2000) can identify any other models involving the set of variables in the system that provide an equally good account of the data, if they exist. In separate work, Pearl (2009) has also utilized the graph theoretic approach, developing a mathematical calculus for problems of causal inference. This approach offers great promise as a source of understanding of causal effects in complex systems in the future. Compared with the Campbell and Rubin approaches, however, to date it has provided little practical guidance for researchers attempting to strengthen the inferences about the effectiveness of interventions that can be drawn from evaluation studies. How Well Do Alternative Designs Work? Early attempts to compare the magnitude of the causal effects estimated from RCTs and nonrandomized designs used one of two approaches. First, the results from an RCT and a separate observational study investigating the same question were compared. For example, LaLonde (1986) found that an RCT and nonrandomized evaluations of the effectiveness of job training programs led to completely different results. Second, the results of interventions in a research area evaluated using randomized and nonrandomized designs were compared in a meta-analytic review. For example, Sacks and colleagues (1983) compared results of RCTs of medical interventions with results of nonrandomized designs using historical controls, finding that the nonrandomized designs overestimated the effectiveness of the interventions. A number of studies showing the noncomparability of results of RCTs and nonrandomized designs exist, although many of the larger meta-analyses in medicine (Ioannidis et al., 2001) and the behavioral sciences (Lipsey and Wilson, 1993) find no evidence of consistent bias. More recently, Cook and colleagues (2008) compared the small set of randomized and nonrandomized studies that shared the same treatment group and the same measurement of the outcome variable. All cases in which an RCT was compared with a regression discontinuity or interrupted time series study design (see Appendix E for discussion of these study designs) showed no differences in effect size. Observational studies produced more variable results, but the results did not differ from those of an RCT given that (1) a control group of similar participants was employed or (2) the mechanism for selection into treatment and control groups was known. Hernán
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Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making and colleagues (2008) considered the disparate results of an RCT (Women’s Health Initiative) and an observational study (Nurses’ Health Study) of the effectiveness of postmenopausal hormone replacement therapy. When the observational study was analyzed using propensity score methods with participants who met the same eligibility criteria and the same intention-to-treat causal effect was estimated, discrepancies were minimal. Finally, Shadish and colleagues (2008) randomly assigned student participants to an RCT or observational study (self-selection) of the effects of math or vocabulary training. They found little difference in the estimates of causal effects after adjusting for an extensive set of baseline covariates in the observational study. The results of the small number of focused comparisons of randomized and nonrandomized designs to date are encouraging. Additional research comparing treatment effect estimates for randomized and nonrandomized designs using similar treatments, populations of participants, and effect size estimates is needed to determine the generality of this finding. REFERENCES Anderson, C. A., and L. J. Appel. 2006. Dietary modification and CVD prevention: A matter of fat. Journal of the American Medical Association 295(6):693-695. Bollen, K. A. 1989. Structural equations with latent variables. New York: John Wiley and Sons, Inc. Bonell, C., J. Hargreaves, V. Strange, P. Pronyk, and J. Porter. 2006. Should structural interventions be evaluated using RCTs? The case of HIV prevention. Social Science and Medicine 63(5):1135-1142. Campbell, D. T. 1957. Factors relevant to the validity of experiments in social settings. Psychological Bulletin 54(4):297-312. Campbell, D. T. 1988. Can we be scientific in applied social science? In Methodology and epistemology for social science: Selected papers of Donald T. Campbell, edited by E. S. Overman. Chicago, IL: University of Chicago Press. Pp. 315-334. Campbell, D. T., and J. C. Stanley. 1966. Experimental and quasi-experimental designs for research. Chicago, IL: Rand McNally. CBO (Congressional Budget Office). 2008. Key issues in analyzing major health insurance proposals. Publication No. 3102. Washington, DC: U.S. Congress. CDC (Centers for Disease Control and Prevention). 1999. Best practices for comprehensive tobacco control programs—April 1999. Atlanta, GA: U.S. Department of Health and Human Services, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health. Collins, L. M., J. J. Dziak, and R. Li. 2009. Design of experiments with multiple independent variables: A resource management perspective on complete and reduced factorial designs. Psychological Methods 14(3):202-224. Cook, T. D., and D. T. Campbell. 1979. Quasi-experimentation: Design and analysis issues for field settings. Chicago, IL: Rand McNally College Publishing Co.
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