Book contents
- Dynamic Pathways to Recovery from Alcohol Use Disorder
- Dynamic Pathways to Recovery from Alcohol Use Disorder
- Copyright page
- Dedication
- Contents
- Figures
- Tables
- Contributors
- Foreword
- Preface
- Acknowledgments
- Introduction
- Part I Micro Level
- Part II Meso Level
- Part III Macro Level
- 16 Recovery Communities
- 17 Disparities in Alcohol Treatment Access in Rural Areas
- 18 Recovery, Communities, and the Organized Recovery Movement
- 19 Alcohol Control Policy and Regulations to Promote Recovery from Alcohol Use Disorder
- 20 Causal Inference Approaches to Studying Recovery from Alcohol Use Disorder
- 21 Building on Collaborative Research to Co-Design SURE Recovery, a Mobile Application for People with Experience of Alcohol and Other Drug Problems
- 22 Economic Methods Used to Evaluate Recovery Programs for Alcohol Use Disorder
- Conclusions and Future Directions
- Index
- References
20 - Causal Inference Approaches to Studying Recovery from Alcohol Use Disorder
from Part III - Macro Level
Published online by Cambridge University Press: 23 December 2021
Book contents
- Dynamic Pathways to Recovery from Alcohol Use Disorder
- Dynamic Pathways to Recovery from Alcohol Use Disorder
- Copyright page
- Dedication
- Contents
- Figures
- Tables
- Contributors
- Foreword
- Preface
- Acknowledgments
- Introduction
- Part I Micro Level
- Part II Meso Level
- Part III Macro Level
- 16 Recovery Communities
- 17 Disparities in Alcohol Treatment Access in Rural Areas
- 18 Recovery, Communities, and the Organized Recovery Movement
- 19 Alcohol Control Policy and Regulations to Promote Recovery from Alcohol Use Disorder
- 20 Causal Inference Approaches to Studying Recovery from Alcohol Use Disorder
- 21 Building on Collaborative Research to Co-Design SURE Recovery, a Mobile Application for People with Experience of Alcohol and Other Drug Problems
- 22 Economic Methods Used to Evaluate Recovery Programs for Alcohol Use Disorder
- Conclusions and Future Directions
- Index
- References
Summary
This chapter highlights methods for estimating causality in the health and behavioral sciences, with an emphasis on methods that have been utilized in the study of recovery from alcohol use disorder. Emphasis is placed on the role of design as a necessary component in teasing out causal relationships, with the ideal approach being an experimental approach with a randomization component. In the absence of experimental design, researchers often turn to observational studies. In such cases, it is necessary to turn to quasi-experimental designs, two of which are highlighted herein: regression discontinuity and interrupted time series designs. Additionally, disadvantages of propensity scores are discussed, psychometric network modeling is described, and software packages for implementing these methods are highlighted.
Keywords
- Type
- Chapter
- Information
- Dynamic Pathways to Recovery from Alcohol Use DisorderMeaning and Methods, pp. 364 - 379Publisher: Cambridge University PressPrint publication year: 2022
References
Atkins, B. Z., & Aldea, G. S. (2018). The paradox between randomized controlled trials and propensity score-matched real-world data: Moving dissonance to dialog? The Journal of Thoracic and Cardiovascular Surgery, 156(3), 1026–1027. https://doi.org/10.1016/j.jtcvs.2018.06.003CrossRefGoogle ScholarPubMed
Beard, E., Marsden, J., Brown, J., Tombor, I., Stapleton, J., Michie, S., & West, R. (2019). Understanding and using time series analyses in addiction research. Addiction, 114(10), 1866–1884. https://doi.org/10.1111/add.14643CrossRefGoogle ScholarPubMed
Biglan, A., Ary, D., & Wagenaar, A. C. (2000). The value of interrupted time-series experiments for community intervention research. Prevention Science, 1(1), 31–49. https://doi.org/10.1023/a:1010024016308CrossRefGoogle ScholarPubMed
Borsboom, D. (2008). Psychometric perspectives on diagnostic systems. Journal of Clinical Psychology, 64(9), 108–109. https://doi.org/10.1002/jclp.20503CrossRefGoogle ScholarPubMed
Brusco, M. J., Steinley, D., Hoffman, M., Davis-Stober, C., & Wasserman, S. W. (2019). On Ising models and algorithms for the construction of symptom networks in psychopathological research. Psychological Methods, 24(6), 735–753. https://doi.org/10.1037/met0000207Google Scholar
Epskamp, S., Cramer, A. O., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). Qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48(4), 1–18. https://doi.org/10.18637/jss.v048.i04Google Scholar
Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23(4), 617–634. https://doi.org/10.1037/met0000167CrossRefGoogle ScholarPubMed
Fisher, R. A. (1926). The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33 , 505–513.Google Scholar
Fisher, R. A., & Yates, F. (1953). Statistical tables for biological, agricultural, and medical research (4th ed.). Oliver & Boyd.Google Scholar
Flam-Zalcman, R., Mann, R. E., Studoto, G., Nochajski, T. H., Rush, B. R., Koski-Jannes, A., Wickens, C. M., Thomas, R. K., & Rehm, J. (2013). Evidence from regression-discontinuity analyses for beneficial effects of a criterion-based increase in alcohol treatment. International Journal of Methods in Psychiatry Research, 22(1), 59–70. https://doi.org/10.1002/mpr.1374CrossRefGoogle ScholarPubMed
Fried, E. I., van Borkulo, C. D., Cramer, A. O., Boschloo, L., Schoevers, R. A., & Borsboom, D. (2017). Mental disorders as networks of problems: A review of recent insights. Social Psychiatry and Psychiatric Epidemiology, 52(1), 1–10. https://doi.org/10.1007/s00127–016-1319-zGoogle Scholar
Gromping, U. (2015). Variable importance in regression models. WIREs Computational Statistics, 7(2), 137–152. https://doi.org/10.1002/wics.1346Google Scholar
Hernan, M. A., & Robins, J. M. (2006). Instruments for causal inference: An epidemiologists dream? Epidemiology, 17(4), 360–372. https://doi.org/10.1097/01.ede.0000222409.00878.37Google Scholar
Hester, R. K., & Miller, W. R. (1988). Empirical guidelines for optimal client-treatment matching. NIDA Research Monograph, 77 , 27–38.Google Scholar
Humphreys, D. K., Eisner, M. P.., & Wieber, D. J. (2013). Evaluating the impact of flexible alcohol trading hours on violence: An interrupted time series analysis. PLoS One, 8(2), e55581. https://doi.org/10.1371/journal.pone.0055581Google Scholar
Irvin, K. M., Bell, D. J., Steinley, D., & Bartholow, B. (2020). The thrill of victory: Savoring positive affect, psychophysiological reward processing, and symptoms of depression. Emotion. Advance online publication. https://doi.org/10.1037/emo0000914Google Scholar
Lash, T. L., VanderWeele, T. J., Haneuse, S., & Rothman, K. J. (2021). Modern epidemiology (4th ed.). Wolters Kluwer.Google Scholar
Lee, M.-J. (2016). Matching, regression discontinuity, differences in differences, and beyond. Oxford University Press.Google Scholar
Linden, A. Adams, J. L., & Roberts, N. (2006). Evaluating disease management programme effectiveness: An introduction to the regression discontinuity design. Journal of Evaluation in Clinical Practice, 12(2), 124–131. https://doi.org/10.1111/j.1365-2753.2005.00573.xGoogle Scholar
Loux, T. M. (2015). Randomization, matching, and propensity scores in the design and analysis of experimental studies with measured baseline covariates. Statistics in Medicine, 34(4), 558–570. https://doi.org/10.1002/sim.6361Google Scholar
Magura, S., McKean, J., Kosten, S., & Tonigan, J. S. (2014). A novel application of propensity score matching to estimate Alcoholics Anonymous’ effects on drinking outcomes. Drug and Alcohol Dependence, 129(1–2), 54–59. https://doi.org/10.1016/j.drugalcdep.2012.09.011CrossRefGoogle Scholar
Mann, R. E., Stoduto, G., Flam-Zalcman, R., Nochajski, T. H., Hall, L., Dill, P., & Wells-Parker, E. (2009). Examining factors in the research institute on addictions self inventory (RIASI): Associations with alcohol use and problems at assessment and follow-up. International Journal of Environmental Research and Public Health, 6(11), 2898–2918. https://doi.org/10.3390/ijerph6112898Google Scholar
Maxwell, S. E., Delaney, H. D., & Kelley, K. (2017). Designing experiments and analyzing data: A model comparison perspective. Routledge.CrossRefGoogle Scholar
Mnatzaganian, G., Davidson, D. C., Hiller, J. E., & Ryan, P. (2015). Propensity score matching and randomization. Journal of Clinical Epidemiology, 68(7), 760–768. https://doi.org/10.1016/j.jclinepi.2015.01.002Google Scholar
Pearl, J. (2009). Causal inference in statistics: An overview. Statistics Surveys, 3 , 96–146. https://doi.org/10.1214/09-SS057Google Scholar
Pearl, J. (2017). A linear “microscope” for interventions and counterfactuals. Journal of Causal Inference, 5(1), 1–15. https://doi.org/10.1515/jci-2017-0003CrossRefGoogle Scholar
Rubin, D. B. (2001). Estimating the causal effects of smoking. Statistics in Medicine, 20(9-10), 1395–1414. https://doi.org/10.1002/sim.677CrossRefGoogle ScholarPubMed
Rubin, D. B. (2006). Matched sampling for causal effects. Cambridge University Press.CrossRefGoogle Scholar
Rubin, D. B. (2007). The design versus the analysis of observational studies for causal effects: Parallels with the design of randomized trials. Statistics in Medicine, 26(1), 20–30. https://doi.org/10.1002/sim.2739CrossRefGoogle ScholarPubMed
Rubin, D. B. (2008). For objective causal inference, design trumps analysis. The Annals of Applied Statistics, 2(3), 808–840. https://doi.org/10.1214/08-AOAS187Google Scholar
Salsburg, D. (2001). The lady tasting tea: How statistics revolutionized science in the twentieth century. Henry Holt & Co.Google Scholar
Saunders, J. B., Aasland, O. G., Babor, T. F., de la Fuente, J. R., & Grant, M. (1993). Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption–II. Addiction, 88(6), 791–804. https://doi.org/10.1111/j.1360-0443.1993.tb02093.xGoogle Scholar
Scutari, M. (2010). Learning Bayesian networks with the bnlearn R package. Journal of Statistical Software, 35(3), 1–22. https://doi.org/10.18637/jss.v035.i03Google Scholar
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Wadsworth.Google Scholar
Stein, J. H., Steinley, D., & Cropanzano, R. (2011). How and why terrorism corrupts the consistency of organizational justice. Journal of Organizational Behavior, 32(7), 984–1007. https://doi.org/10.1002/job.729Google Scholar
Steinley, D. (2006). Curse of dimensionality. In Salkind, N. J. (Ed.), Encyclopedia of Measurement and Statistics (pp. 209–211). Sage.Google Scholar
Tackett, J. L., & Miller, J. D. (2019). Introduction to the special section on increasing replicability, transparency, and openness in clinical psychology. Journal of Abnormal Psychology, 128(6), 487–492. https://doi.org/10.1037/abn0000455Google Scholar
van Borkulo, C. D., Borsboom, D., Epskamp, S., Blanken, T. F., Boschloo, L., Schoevers, R. A., & Waldrop, L. J. (2014). A new method for constructing networks from binary data. Scientific Reports, 4 , 5918. https://doi.org/10.1038/srep05918CrossRefGoogle ScholarPubMed
Vocht, F. de., Campbell, R., Brennan, A., Mooney, J., Angus, C., & Hickman, M. (2016). Propensity score matching for selection of local areas as controls for evaluation of effects of alcohol policies in case series and quasi case-control designs. Public Health, 132 , 40–49. https://doi.org/10.1016/j.puhe.2015.10.033Google Scholar
Xu, Z., & Kalbfleisch, J. D. (2012). Propensity score matching in randomized clinical trials. Biometrics, 66(3), 813–823. https://doi.org/10.1111/j.1541-0420.2009.01364.xGoogle Scholar