DOCSLIB.ORG

Methods for Evaluating Natural Experiments in Obesity: Systematic Evidence Review

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Methods for Evaluating Natural Experiments in Obesity: Systematic Evidence Review Comparative Effectiveness Review Number 204 Methods for Evaluating Natural Experiments in Obesity: Systematic Evidence Review e Comparative Effectiveness Review Number 204 Methods for Evaluating Natural Experiments in Obesity: Systematic Evidence Review Prepared for: Agency for Healthcare Research and Quality U.S. Department of Health and Human Services 5600 Fishers Lane Rockville, MD 20857 www.ahrq.gov Contract No. 290-2012-00007-I Prepared by: Johns Hopkins University Evidence-based Practice Center Baltimore, MD Investigators: Wendy L. Bennett, M.D., M.P.H. Lawrence J. Cheskin, M.D. Renee F. Wilson, M.S. Allen Zhang, B.S. Eva Tseng, M.D., M.P.H. Oluwaseun Shogbesan, M.D. Emily A. Knapp, M.H.S. Elizabeth A. Stuart, Ph.D. Eric B. Bass, M.D., M.P.H. Hadi Kharrazi, M.D. AHRQ Publication No. 18-EHC006-EF December 2017 Purpose of Review To characterize studies of programs and policies in obesity prevention and control in terms of data sources, data linkages, measures reported, study designs, and analytic approaches, and to identify needed methodological advances. Key Messages • Relevant programs, policies, or built environment changes were evaluated in 156 natural experiments, 118 experimental studies, and 20 other studies. • Criteria for a data system (source exists, is available for research, is sharable, and has outcomes of interest) were met by 106 data sources. • Thirty-seven percent of U.S. data systems were linked to secondary data. • Outcome measures included dietary behavior (148 studies), physical activity (152 studies), childhood weight (112 studies), and adult weight (32 studies). • Natural experiments most commonly used regression models comparing exposed and unexposed groups at one time. • Natural experiments generally had moderate risk of selection bias and high risk of bias for losses to follow-up. • Research could be advanced by more use of data dictionaries, reporting standards on data linkage, long-term obesity-related outcomes, and study designs with multiple pre- and post- exposure time points. ii This report is based on research conducted by the Johns Hopkins University Evidence-based Practice Center (EPC) under contract to the Agency for Healthcare Research and Quality (AHRQ), Rockville, MD (Contract No. 290-2012-00007-I). The findings and conclusions in this document are those of the authors, who are responsible for its contents; the findings and conclusions do not necessarily represent the views of AHRQ. Therefore, no statement in this report should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services. None of the investigators have any affiliations or financial involvement that conflicts with the material presented in this report. The information in this report is intended to help health care decisionmakers—patients and clinicians, health system leaders, and policymakers, among others—make well-informed decisions and thereby improve the quality of health care services. This report is not intended to be a substitute for the application of clinical judgment. Anyone who makes decisions concerning the provision of clinical care should consider this report in the same way as any medical reference and in conjunction with all other pertinent information, i.e., in the context of available resources and circumstances presented by individual patients. This report is made available to the public under the terms of a licensing agreement between the author and the Agency for Healthcare Research and Quality. This report may be used and reprinted without permission except those copyrighted materials that are clearly noted in the report. Further reproduction of those copyrighted materials is prohibited without the express permission of copyright holders. AHRQ or U.S. Department of Health and Human Services endorsement of any derivative products that may be developed from this report, such as clinical practice guidelines, other quality enhancement tools, or reimbursement or coverage policies, may not be stated or implied. This report may periodically be assessed for the currency of conclusions. If an assessment is done, the resulting surveillance report describing the methodology and findings will be found on the Effective Health Care Program Web site at www.effectivehealthcare.ahrq.gov. Search on the title of the report. Persons using assistive technology may not be able to fully access information in this report. For assistance contact [email protected]. Suggested citation: Bennett WL, Cheskin LJ, Wilson RF, Zhang A, Tseng E, Shogbesan O, Knapp EA, Stuart EA, Bass EB, Kharazzi H. Methods for Evaluating Natural Experiments in Obesity: Systematic Evidence Review. Comparative Effectiveness Review No. 204. (Prepared by the Johns Hopkins University Evidence-based Practice Center under Contract No. 290-2012- 00007-I.) AHRQ Publication No. 18-EHC006-EF. Rockville, MD: Agency for Healthcare Research and Quality; December 2017. Posted final reports are located on the Effective Health Care Program search page. DOI: http://doi.org/10.23970/AHRQEPCCER204. iii Preface The Agency for Healthcare Research and Quality (AHRQ), through its Evidence-based Practice Centers (EPCs), sponsors the development of systematic reviews to assist public- and private-sector organizations in their efforts to improve the quality of health care in the United States. These reviews provide comprehensive, science-based information on common, costly medical conditions, and new health care technologies and strategies. Systematic reviews are the building blocks underlying evidence-based practice; they focus attention on the strength and limits of evidence from research studies about the effectiveness and safety of a clinical intervention. In the context of developing recommendations for practice, systematic reviews can help clarify whether assertions about the value of the intervention are based on strong evidence from clinical studies. For more information about AHRQ EPC systematic reviews, see www.effectivehealthcare.ahrq.gov/reference/purpose.cfm. AHRQ expects that these systematic reviews will be helpful to health plans, providers, purchasers, government programs, and the health care system as a whole. Transparency and stakeholder input are essential to the Effective Health Care Program. Please visit the Web site (www.effectivehealthcare.ahrq.gov) to see draft research questions and reports or to join an email list to learn about new program products and opportunities for input. If you have comments on this systematic review, they may be sent by mail to the Task Order Officer named below at: Agency for Healthcare Research and Quality, 5600 Fishers Lane, Rockville, MD 20857, or by email to [email protected]. Gopal Khanna, M.B.A. Arlene S. Bierman, M.D., M.S. Director Director Agency for Healthcare Research and Quality Center for Evidence and Practice Improvement Agency for Healthcare Research and Quality Stephanie Chang, M.D., M.P.H. Lionel L. Bañez, M.D. Director Task Order Officer Evidence-based Practice Center Program Center for Evidence and Practice Center for Evidence and Practice Improvement Improvement Agency for Healthcare Research and Quality Agency for Healthcare Research and Quality iv Acknowledgments The authors gratefully acknowledge the following individuals for their contributions to this project: Jessica Gayleard, Ritu Sharma, Catalina Suarez, and Lisa Wilson. Technical Expert Panel In designing the study questions and methodology at the outset of this report, the EPC consulted several technical and content experts. Broad expertise and perspectives were sought. Divergent and conflicted opinions are common and perceived as healthy scientific discourse that results in a thoughtful, relevant systematic review. Therefore, in the end, study questions, design, methodological approaches, and/or conclusions do not necessarily represent the views of individual technical and content experts. Technical Experts must disclose any financial conflicts of interest greater than $10,000 and any other relevant business or professional conflicts of interest. Because of their unique clinical or content expertise, individuals with potential conflicts may be retained. The TOO and the EPC work to balance, manage, or mitigate any potential conflicts of interest identified. The list of Technical Experts who participated in developing this report follows: David Allison, Ph.D.* Lisa Sanbonmatsu, Ph.D.* Dean and Provost Professor National Bureau of Economic Research Indiana University School of Public Health Cambridge, MA Bloomington, IN Brandon Simms Cheryl Anderson, Ph.D., M.P.H.* Greater Gethsemane Missionary Baptist Associate Professor Church Department of Family Medicine and Public Baltimore, MD Health University of California, San Diego Xuemei Zhu, Ph.D.* La Jolla, CA Associate Professor University of Texas College of Architecture Donna Coffman, Ph.D.* Texas A&M University Dept. of Epidemiology and Biostatistics College Station, TX Temple University Philadelphia, PA Trina Histon, Ph.D. Senior Principal Consultant Kaiser Permanente Oakland, CA *Provided input on Draft Report. v Peer Reviewers Prior to publication of the final evidence report, EPCs sought input from independent Peer Reviewers without financial conflicts of interest. However, the conclusions and synthesis of the scientific literature presented in this report do not necessarily represent the views of individual reviewers. Peer Reviewers must disclose any financial conflicts of interest greater than $10,000 and any other relevant business or professional
Load more

Recommended publications
  • Natural Experiments
    Natural Experiments Jason Seawright [email protected] August 11, 2010 J. Seawright (PolSci) Essex 2010 August 11, 2010 1 / 31 Typology of Natural Experiments J. Seawright (PolSci) Essex 2010 August 11, 2010 2 / 31 Typology of Natural Experiments Classic Natural Experiment J. Seawright (PolSci) Essex 2010 August 11, 2010 2 / 31 Typology of Natural Experiments Classic Natural Experiment Instrumental Variables-Type Natural Experiment J. Seawright (PolSci) Essex 2010 August 11, 2010 2 / 31 Typology of Natural Experiments Classic Natural Experiment Instrumental Variables-Type Natural Experiment Regression-Discontinuity Design J. Seawright (PolSci) Essex 2010 August 11, 2010 2 / 31 Bolstering Understand assumptions. J. Seawright (PolSci) Essex 2010 August 11, 2010 3 / 31 Bolstering Understand assumptions. Explore role of qualitative evidence. J. Seawright (PolSci) Essex 2010 August 11, 2010 3 / 31 Classic Natural Experiment J. Seawright (PolSci) Essex 2010 August 11, 2010 4 / 31 Classic Natural Experiment 1 “Nature” randomizes the treatment. J. Seawright (PolSci) Essex 2010 August 11, 2010 4 / 31 Classic Natural Experiment 1 “Nature” randomizes the treatment. 2 All (observable and unobservable) confounding variables are balanced between treatment and control groups. J. Seawright (PolSci) Essex 2010 August 11, 2010 4 / 31 Classic Natural Experiment 1 “Nature” randomizes the treatment. 2 All (observable and unobservable) confounding variables are balanced between treatment and control groups. 3 No discretion is involved in assigning treatments, or the relevant information is unavailable or unused. J. Seawright (PolSci) Essex 2010 August 11, 2010 4 / 31 Classic Natural Experiment 1 “Nature” randomizes the treatment. 2 All (observable and unobservable) confounding variables are balanced between treatment and control groups.
    [Show full text]
  • Toward an Ethical Experiment
    TOWARD AN ETHICAL EXPERIMENT By Yusuke Narita April 2018 COWLES FOUNDATION DISCUSSION PAPER NO. 2127 COWLES FOUNDATION FOR RESEARCH IN ECONOMICS YALE UNIVERSITY Box 208281 New Haven, Connecticut 06520-8281 http://cowles.yale.edu/ ∗ Toward an Ethical Experiment y Yusuke Narita April 17, 2018 Abstract Randomized Controlled Trials (RCTs) enroll hundreds of millions of subjects and in- volve many human lives. To improve subjects' welfare, I propose an alternative design of RCTs that I call Experiment-as-Market (EXAM). EXAM Pareto optimally randomly assigns each treatment to subjects predicted to experience better treatment effects or to subjects with stronger preferences for the treatment. EXAM is also asymptotically incentive compatible for preference elicitation. Finally, EXAM unbiasedly estimates any causal effect estimable with standard RCTs. I quantify the welfare, incentive, and information properties by applying EXAM to a water cleaning experiment in Kenya (Kremer et al., 2011). Compared to standard RCTs, EXAM substantially improves subjects' predicted well-being while reaching similar treatment effect estimates with similar precision. Keywords: Research Ethics, Clinical Trial, Social Experiment, A/B Test, Market De- sign, Causal Inference, Development Economics, Spring Protection, Discrete Choice ∗I am grateful to Dean Karlan for a conversation that inspired this project; Joseph Moon for industrial and institutional input; Jason Abaluck, Josh Angrist, Tim Armstrong, Sylvain Chassang, Naoki Egami, Peter Hull, Costas Meghir, Bobby Pakzad-Hurson,
    [Show full text]
  • The Distribution of Information in Speculative Markets: a Natural Experiment∗
    The Distribution of Information in Speculative Markets: A Natural Experiment∗ Alasdair Brown† University of East Anglia June 9, 2014 ∗I would like to thank an associate editor, two anonymous referees, Fuyu Yang, and seminar participants at UEA for comments on this work. All remaining errors are my own. †School of Economics, University of East Anglia, Norwich, NR4 7TJ. Email: [email protected] 1 1 Introduction Abstract We use a unique natural experiment to shed light on the distribution of information in speculative markets. In June 2011, Betfair - a U.K. betting exchange - levied a tax of up to 60% on all future profits accrued by the top 0.1% of profitable traders. Such a move appears to have driven at least some of these traders off the exchange, taking their information with them. We investigate the effect of the new tax on the forecasting capacity of the exchange (our measure of the market’s incorporation of information into the price). We find that there was scant decline in the forecasting capacity of the exchange - relative to a control market - suggesting that the bulk of information had hitherto been held by the majority of traders, rather than the select few affected by the rule change. This result is robust to the choice of forecasting measure, the choice of forecasting interval, and the choice of race type. This provides evidence that more than a few traders are typically involved in the price discovery process in speculative markets. JEL Classification: G14, G19 Keywords: informed trading, natural experiment, betting markets, horse racing 1 Introduction There is a common perception that the benefits of trading in speculative assets are skewed towards those on the inside of corporations, or those that move in the same circles as the corporate elite.
    [Show full text]
  • Using Randomized Evaluations to Improve the Efficiency of US Healthcare Delivery
    Using Randomized Evaluations to Improve the Efficiency of US Healthcare Delivery Amy Finkelstein Sarah Taubman MIT, J-PAL North America, and NBER MIT, J-PAL North America February 2015 Abstract: Randomized evaluations of interventions that may improve the efficiency of US healthcare delivery are unfortunately rare. Across top journals in medicine, health services research, and economics, less than 20 percent of studies of interventions in US healthcare delivery are randomized. By contrast, about 80 percent of studies of medical interventions are randomized. The tide may be turning toward making randomized evaluations closer to the norm in healthcare delivery, as the relevant actors have an increasing need for rigorous evidence of what interventions work and why. At the same time, the increasing availability of administrative data that enables high-quality, low-cost RCTs and of new sources of funding that support RCTs in healthcare delivery make them easier to conduct. We suggest a number of design choices that can enhance the feasibility and impact of RCTs on US healthcare delivery including: greater reliance on existing administrative data; measuring a wide range of outcomes on healthcare costs, health, and broader economic measures; and designing experiments in a way that sheds light on underlying mechanisms. Finally, we discuss some of the more common concerns raised about the feasibility and desirability of healthcare RCTs and when and how these can be overcome. _____________________________ We are grateful to Innessa Colaiacovo, Lizi Chen, and Belinda Tang for outstanding research assistance, to Katherine Baicker, Mary Ann Bates, Kelly Bidwell, Joe Doyle, Mireille Jacobson, Larry Katz, Adam Sacarny, Jesse Shapiro, and Annetta Zhou for helpful comments, and to the Laura and John Arnold Foundation for financial support.
    [Show full text]
  • Social Psychology
    Social Psychology OUTLINE OF RESOURCES Introducing Social Psychology Lecture/Discussion Topic: Social Psychology’s Most Important Lessons (p. 853) Social Thinking The Fundamental Attribution Error Lecture/Discussion Topic: Attribution and Models of Helping (p. 856) Classroom Exercises: The Fundamental Attribution Error (p. 854) Students’ Perceptions of You (p. 855) Classroom Exercise/Critical Thinking Break: Biases in Explaining Events (p. 855) NEW Feature (Short) Film: The Lunch Date (p. 856) Worth Video Anthology: The Actor-Observer Difference in Attribution: Observe a Riot in Action* Attitudes and Actions Lecture/Discussion Topics: The Looking Glass Effect (p. 856) The Theory of Reasoned Action (p. 857) Actions Influence Attitudes (p. 857) The Justification of Effort (p. 858) Self-Persuasion (p. 859) Revisiting the Stanford Prison Experiment (p. 859) Abu Ghraib Prison and Social Psychology (p. 860) Classroom Exercise: Introducing Cognitive Dissonance Theory (p. 858) Worth Video Anthology: Zimbardo’s Stanford Prison Experiment* The Stanford Prison Experiment: The Power of the Situation* Social Influence Conformity: Complying With Social Pressures Lecture/Discussion Topics: Mimicry and Prosocial Behavior (p. 861) Social Exclusion and Mimicry (p. 861) The Seattle Windshield Pitting Epidemic (p. 862) Classroom Exercises: Suggestibility (p. 862) Social Influence (p. 863) Student Project: Violating a Social Norm (p. 863) Worth Video Anthology: Social Influence* NEW Liking and Imitation: The Sincerest Form of Flattery* Obedience: Following Orders Lecture/Discussion Topic: Obedience in Everyday Life (p. 865) Classroom Exercises: Obedience and Conformity (p. 864) Would You Obey? (p. 864) Wolves or Sheep? (p. 866) * Titles in the Worth Video Anthology are not described within the core resource unit.
    [Show full text]
  • Synthetic Control Method: Inference, Sensitivity Analysis and Confidence Sets∗
    Synthetic Control Method: Inference, Sensitivity Analysis and Confidence Sets∗ Sergio Firpo† Vitor Possebom‡ Insper Yale University September 1st, 2017 Abstract We extend the inference procedure for the synthetic control method in two ways. First, we impose a parametric form to the treatment assignment probabilities that includes, as a particular case, the uniform distribution that is assumed by Abadie et al.(2015). By changing the value of this parameter, we can analyze the sensitiveness of the test's decision to deviations from the usual uniform distribution assumption. Second, we modify the RMSPE statistic to test any sharp null hypothesis, including, as a specific case, the null hypothesis of no effect whatsoever analyzed by Abadie et al.(2015). Based on this last extension, we invert the test statistic to estimate confidence sets that quickly show the point-estimates' precision, and the test's significance and robustness. We also extend these two tools to other test statistics and to problems with multiple outcome variables or multiple treated units. Furthermore, in a Monte Carlo experiment, we find that the RMSPE statistic has good properties with respect to size, power and robustness. Finally, we illustrate the usefulness of our proposed tools by reanalyzing the economic impact of ETA's terrorism in the Basque Country, studied first by Abadie & Gardeazabal(2003) and Abadie et al.(2011). Keywords: Synthetic Control Estimator, Hypothesis Testing, Sensitivity Analysis, Con- fidence Sets JEL Codes: C21, C23, C33 ∗We are grateful to FAPESP that provided financial aid through grant number 2014/23731-3. We are in debt to the editor and two anonymous referees for useful suggestions.
    [Show full text]
  • Randomized Controlled Trials, Development Economics and Policy Making in Developing Countries
    Randomized Controlled Trials, Development Economics and Policy Making in Developing Countries Esther Duflo Department of Economics, MIT Co-Director J-PAL [Joint work with Abhijit Banerjee and Michael Kremer] Randomized controlled trials have greatly expanded in the last two decades • Randomized controlled Trials were progressively accepted as a tool for policy evaluation in the US through many battles from the 1970s to the 1990s. • In development, the rapid growth starts after the mid 1990s – Kremer et al, studies on Kenya (1994) – PROGRESA experiment (1997) • Since 2000, the growth have been very rapid. J-PAL | THE ROLE OF RANDOMIZED EVALUATIONS IN INFORMING POLICY 2 Cameron et al (2016): RCT in development Figure 1: Number of Published RCTs 300 250 200 150 100 50 0 1975 1980 1985 1990 1995 2000 2005 2010 2015 Publication Year J-PAL | THE ROLE OF RANDOMIZED EVALUATIONS IN INFORMING POLICY 3 BREAD Affiliates doing RCT Figure 4. Fraction of BREAD Affiliates & Fellows with 1 or more RCTs 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 1980 or earlier 1981-1990 1991-2000 2001-2005 2006-today * Total Number of Fellows and Affiliates is 166. PhD Year J-PAL | THE ROLE OF RANDOMIZED EVALUATIONS IN INFORMING POLICY 4 Top Journals J-PAL | THE ROLE OF RANDOMIZED EVALUATIONS IN INFORMING POLICY 5 Many sectors, many countries J-PAL | THE ROLE OF RANDOMIZED EVALUATIONS IN INFORMING POLICY 6 Why have RCT had so much impact? • Focus on identification of causal effects (across the board) • Assessing External Validity • Observing Unobservables • Data collection • Iterative Experimentation • Unpack impacts J-PAL | THE ROLE OF RANDOMIZED EVALUATIONS IN INFORMING POLICY 7 Focus on Identification… across the board! • The key advantage of RCT was perceived to be a clear identification advantage • With RCT, since those who received a treatment are randomly selected in a relevant sample, any difference between treatment and control must be due to the treatment • Most criticisms of experiment also focus on limits to identification (imperfect randomization, attrition, etc.
    [Show full text]
  • Reflections on Experimental Techniques in the Law*
    University of Chicago Law School Chicago Unbound Journal Articles Faculty Scholarship 1974 Reflections on Experimental echniquesT in the Law Hans Zeisel Follow this and additional works at: https://chicagounbound.uchicago.edu/journal_articles Part of the Law Commons Recommended Citation Hans Zeisel, "Reflections on Experimental echniquesT in the Law," 15 Jurimetrics Journal 256 (1974). This Article is brought to you for free and open access by the Faculty Scholarship at Chicago Unbound. It has been accepted for inclusion in Journal Articles by an authorized administrator of Chicago Unbound. For more information, please contact [email protected]. REFLECTIONS ON EXPERIMENTAL TECHNIQUES IN THE LAW* Hans Zeiselt Much of the law's reasoning turns on the effects of the rules it creates or enforces. Normally these effects are alleged or implied, but rarely proven. Yet during the last decades, first the natural sciences and, learn- ing from them, the social sciences have developed techniques of ek- perimentation that would allow firmer determinations of what a rule of law does or does not accomplish.' The basic tool (and also the paradigm for retrospective analysis) is the controlled experiment. Its essence is simple: The objects or people on which the experiment is to be performed are divided randomly, that is, by some lottery device, into two groups; the experimental treatment is then applied to the one group and withheld from the other, the control group. If an effect is found in the experimental group that is absent from the control group, it can be attributed to the experimental treatment, since at the time of the prior random selection the two groups had been strictly comparable, and in fact interchangeable.' And randomization assures that whatever the outcome of the experiment, it is possible to calculate the degree of certainty that can be attached to it.
    [Show full text]
  • Evidence of False Positives in Research Clearinghouses and Influential Journals: an Application of P-Curve to Policy Research
    Evidence of False Positives in Research Clearinghouses and Influential Journals: An Application of P-Curve to Policy Research Sean Tanner Selective reporting of results within studies, a practice known as “p-hacking,” presents a pernicious threat to the validity of published social science research. This form of publication bias is particularly troubling in research that influences public policy debates and programmatic funding. While recent analyses of political science, psychology, economic, and sociology research have identified considerable publication bias, the methods used are unable to distinguish p-hacking from accidents of random sampling. Using the novel “p-curve” estimation framework, which can distinguish p-hacking from other forms of false positives, I find little evidence of p-hacking in federal research clearinghouses and three rigorous, policy-oriented journals: the Journal of Policy Analysis and Management, The Journal of Human Resources, and Education Evaluation and Policy Analysis. The results demonstrate that most of the findings in this literature are likely to be true positives. I discuss methodological and substantive divergence from the prior literature and suggest future research that can more accurately assess the cross-disciplinary veracity of social science research. I. Introduction Recent work has uncovered a troubling pattern in social science research: statistically significant results can often be attributed to selective reporting of results within studies, or “p-hacking” (Ioannidis, 2005; Simonsohn, Nelson,
    [Show full text]
  • Using Natural Experiments to Study the Impact of Media on the Family
    JOSEPH PRICE Brigham Young University GORDON B. DAHL University of California, San Diego* Using Natural Experiments to Study the Impact of Media on the Family The randomized trial is the gold standard in including television, movies, print and digital scientific research and is used by several fields advertisements, and the Internet (Pergrams & to study the effects of media. Although useful Zaradic, 2006). Some of these media may have for studying the immediate response to media negative effects, whereas other types of media exposure, the experimental approach is not well may have positive effects. Social scientists from suited to studying long-term effects or behavior a variety of disciplines have used different outside the laboratory. The ‘‘natural experi- techniques to model and estimate these impacts. ment’’ approach, a quasi-experimental method In this paper, we discuss what economists commonly used by economists, exploits settings have learned about these questions through in which there is naturally occurring variation the use of ‘‘natural experiments.’’ We view that ‘‘randomly’’ influences the amount and type the economist’s approach as complementary to of media that individuals are exposed to. We dis- those in other disciplines (and also recognize cuss the methodological issues of natural exper- its occasional use by other disciplines). A iments and the ways in which findings based primary aim of this article is to encourage a on this approach can complement the findings more interdisciplinary approach to evaluating from laboratory or other experimental settings. the impact of the media on the family. The examples we discuss highlight the value of The gold standard in scientific research is interdisciplinary work in better understanding the randomized controlled trial.
    [Show full text]
  • Nber Working Paper Series Regression Discontinuity
    NBER WORKING PAPER SERIES REGRESSION DISCONTINUITY DESIGNS IN ECONOMICS David S. Lee Thomas Lemieux Working Paper 14723 http://www.nber.org/papers/w14723 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 February 2009 We thank David Autor, David Card, John DiNardo, Guido Imbens, and Justin McCrary for suggestions for this article, as well as for numerous illuminating discussions on the various topics we cover in this review. We also thank two anonymous referees for their helpful suggestions and comments, and Mike Geruso, Andrew Marder, and Zhuan Pei for their careful reading of earlier drafts. Diane Alexander, Emily Buchsbaum, Elizabeth Debraggio, Enkeleda Gjeci, Ashley Hodgson, Yan Lau, Pauline Leung, and Xiaotong Niu provided excellent research assistance. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research. © 2009 by David S. Lee and Thomas Lemieux. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. Regression Discontinuity Designs in Economics David S. Lee and Thomas Lemieux NBER Working Paper No. 14723 February 2009, Revised October 2009 JEL No. C1,H0,I0,J0 ABSTRACT This paper provides an introduction and "user guide" to Regression Discontinuity (RD) designs for empirical researchers. It presents the basic theory behind the research design, details when RD is likely to be valid or invalid given economic incentives, explains why it is considered a "quasi-experimental" design, and summarizes different ways (with their advantages and disadvantages) of estimating RD designs and the limitations of interpreting these estimates.
    [Show full text]
  • Methods to Address Self-Selection and Reverse Causation in Studies of Neighborhood Environments and Brain Health
    International Journal of Environmental Research and Public Health Review Methods to Address Self-Selection and Reverse Causation in Studies of Neighborhood Environments and Brain Health Lilah M. Besser 1,*, Willa D. Brenowitz 2, Oanh L. Meyer 3, Serena Hoermann 4 and John Renne 4 1 Department of Urban and Regional Planning, Institute for Human Health and Disease Intervention (I-HEALTH), Florida Atlantic University, Boca Raton, FL 33431, USA 2 Departments of Psychiatry and Behavioral Sciences and Epidemiology & Biostatistics, University of California San Francisco, San Francisco, CA 94121, USA; [email protected] 3 Department of Neurology, University of California Davis, Sacramento, CA 95817, USA; [email protected] 4 Center for Urban and Environmental Solutions (CUES), Department of Urban and Regional Planning, Florida Atlantic University, Boca Raton, FL 33431, USA; [email protected] (S.H.); [email protected] (J.R.) * Correspondence: [email protected] Abstract: Preliminary evidence suggests that neighborhood environments, such as socioeconomic disadvantage, pedestrian and physical activity infrastructure, and availability of neighborhood desti- nations (e.g., parks), may be associated with late-life cognitive functioning and risk of Alzheimer’s disease and related disorders (ADRD). The supposition is that these neighborhood characteristics are associated with factors such as mental health, environmental exposures, health behaviors, and social determinants of health that in turn promote or diminish cognitive reserve and resilience in later life. However, observed associations may be biased by self-selection or reverse causation, such as when individuals with better cognition move to denser neighborhoods because they prefer many Citation: Besser, L.M.; Brenowitz, destinations within walking distance of home, or when individuals with deteriorating health choose W.D.; Meyer, O.L.; Hoermann, S.; residences offering health services in neighborhoods in rural or suburban areas (e.g., assisted living).
    [Show full text]