Bridging the Evidence Gap in Obesity Prevention A Framework to Inform Decision Making (2010) / Chapter Skim
Currently Skimming:
Appendix E: An In-Depth Look at Study Designs and Methodologies
Appendix E: An In-Depth Look at Study Designs and Methodologies
Pages 277-300
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 277... ...
Reichardt considers all possible designs that can be created TABLE E-1 A Typology of Research Designs Prominent Assignment to Treatment Size-of Nonrandom Effect Factor Random Explicit Quantitative Ordering No Explicit Quantitative Ordering Recipients Randomized recipient design Regression discontinuity design Nonequivalent recipients design Times Randomized time design Interrupted time series design Nonequivalent times design Settings Randomized setting design Discontinuity across settings design Nonequivalent settings design Outcome Randomized outcome Discontinuity across outcome Nonequivalent outcome variables variables variable design variables design design SOURCE: Reichardt, 2006.
|
From page 278... ...
First, two designs involving nonrandom, quan titative assignment rules -- the regression discontinuity design and the interrupted time series design -- are discussed. Next, the observational study (also known as the nonequivalent control group design or nonequivalent recipients design)
|
From page 279... ...
Given assessment of the outcome fol lowing the intervention, comparison of the outcomes at the threshold for the interven tion and in control groups permits strong causal inferences to be drawn. To understand the RD design, consider the example of evaluating the effective ness of school lunch programs on health, which is illustrated in Figure E-1.
|
From page 280... ...
discussed in this report. When an existing program uses quantitative assignment rules, the RD design permits strong evaluation of the program without the need to create a pool of participants willing to be randomized.
|
From page 281... ...
Second, in some RD designs, the quantitative assignment variable does not fully determine treat ment assignment. Econometricians make a distinction between "sharp" RD designs, in which the quantitative assignment variable fully determines treatment assignment, and "fuzzy" RD designs, in which a more complex treatment selection model determines assignment.
|
From page 282... ...
From a design standpoint, causal inferences from the simple ITS perspective need to be tempered because the basic design fails to address three major threats to the certainty of the causal relationship between an intervention and the observed out comes (internal validity) (Shadish et al., 2002; West et al., 2000)
|
From page 283... ...
These threats must be addressed if strong causal inferences are to be drawn. To illustrate this design, consider an evaluation of a campaign to increase sales of lottery tickets (Reynolds and West, 1987)
|
From page 284... ...
in this example observational study could interact with selection and under mine causal inference regarding the intervention and the observed outcomes (Shadish et al., 2002) : • Selection × history interaction -- Some other event unrelated to the treatment could occur during the lottery game that would affect sales.
|
From page 285... ...
Within the treatment stores, sales of lottery tickets increased substantially following the introduction of the treatment. Sales of other major categories (gasoline, cigarettes, nontaxable groceries, and taxable groceries)
|
From page 286... ...
Figure E-3 shows that the distribution of the retained and promoted groups on propensity scores became closely balanced following the use of the optimal matching procedures. The use of propensity scores helps rule out the possibility that preexisting differences (selection bias)
|
From page 287... ...
Such designs are attractive because of their ease of implementation, but they are far weaker in terms of causal inference than experimental and quasi-experimental designs. Campbell and Stanley (1966)
|
From page 288... ...
The design element approach does not enable the certainty about causal inference provided by the RCT, but it can often greatly improve the evidence base on which decision makers make choices about implementing interventions. Economic Cost Analysis Studies that assess the economic costs of obesity can differ in terms of their breadth and perspective.
|
From page 289... ...
Given the lags between poor diets, physical inactivity, obesity, and resulting diseases, cost estimates based on the prevalence approach reflect historical behavior patterns and cannot be used to predict the short-term impact of interventions aimed at reducing obesity and its consequences. Nevertheless, this is the most common approach used in economic cost studies given its relatively simpler methodology and the availability of necessary data.
|
From page 290... ...
In contrast, the incidence-based approach produces an estimate of net costs, reflect ing the trade-offs between higher average annual costs for an obese individual and the extra costs that result from a nonobese individual's living longer. When one is compar ing gross and net costs, a particularly controversial issue relates to what has come to be known as the "death benefit," that is, whether the "savings" that result from lower pension and social security payments that an obese individual who dies prematurely will not collect should be included in the cost accounting.
|
From page 291... ...
Yet there is widespread agreement that further progress in controlling obesity will require policy changes, organizational changes, changes in the built environment, and changes in social norms, all of which require interventions and measurement of change at levels beyond the individual. An example of an ecological model that illustrates the matching process is the Multilevel Approach to Community Health (MATCH)
|
From page 292... ...
Long Grove, IL: Waveland Press, Inc., 1995, All rights reserved. Bridging the Evidence Gap in Obesity Prevention 292
|
From page 293... ...
describe their development of an HIV prevention program using theory to guide their selection of determinants of behavior and the environment (e.g., knowledge, risk perceptions, attitudes, social influences, self-efficacy) relevant to the health problem (e.g., safe sex)
|
From page 294... ...
. They want to weigh what the experimental evidence shows, with its strong level of certainty of the causal relationship between the inter vention and the observed outcomes (internal validity)
|
From page 295... ...
and Racial and Ethnic Approaches to Community Health, (REACH; CDC, 2009b) ; community-based participatory research (CBPR; Cargo and Mercer, 2008; Horowitz et al., 2009; Minkler and Wallerstein, 2008)
|
From page 296... ...
2006. Planning health promotion programs: An intervention mapping approach.
|
From page 297... ...
2001. Identification and estimation of treatment effects with a regression-discontinuity design.
|
From page 298... ...
2007. The impact of a smoking ban on hospital admissions for coronary heart disease.
|
From page 299... ...
In Planning health promotion programs: An intervention mapping approach, edited by L
|
From page 300... ...
:85-105. Bridging the Evidence Gap in Obesity Prevention 00
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.