Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-23T20:42:00.906Z Has data issue: false hasContentIssue false

7 - Time-Varying Effect Modeling to Examine Recovery Outcomes across Four Years

from Part I - Micro Level

Published online by Cambridge University Press:  23 December 2021

By
Ashley N. Linden-Carmichael ,
Samuel W. Stull ,
Christy K. Scott  and
Michael L. Dennis
Edited by
Jalie A. Tucker  and
Katie Witkiewitz
Jalie A. Tucker
Affiliation:
University of Florida
Katie Witkiewitz
Affiliation:
University of New Mexico
Get access

Summary

This chapter introduces readers to the use of time-varying effect modeling (TVEM), a statistical tool for capturing dynamic changes over time, as applied to the study of substance use disorder recovery processes. The chapter presents an empirical demonstration of using TVEM to examine the effect of an intervention, Recovery Management Checkups (RMCs), on substance use and key features of the ongoing process of recovery (life satisfaction, cognitive avoidance, self-efficacy) as a continuous function of time. The example application data come from the Early Re-Intervention experiment of 446 adults from a large addiction treatment agency who were randomly assigned to receive RMCs or an assessment control. Given the time-varying nature of the effect of the RMC on recovery outcomes and the differential patterns observed by type of outcome, TVEM may be a viable option in lieu of or in addition to using common metrics of “treatment success.” SAS syntax is provided.

Keywords

time-varying effect modelingrecovery management checkupsEarly Re-Interventionlife satisfactioncognitive avoidanceself-efficacy
Type
Chapter
Information
Dynamic Pathways to Recovery from Alcohol Use Disorder
Meaning and Methods
 , pp. 114 - 136
Publisher: Cambridge University Press
Print publication year: 2022

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ashford, R. D., Brown, A., Brown, T., Callis, J., Cleveland, H. H., Eisenhart, E., Groover, H., Hayes, N., Johnston, T., Kimball, T., Manteuffel, B., McDaniel, J., Montgomery, L., Phillips, S., Polacek, M., Statman, M. Whitney, J. (2019). Defining and operationalizing the phenomena of recovery: A working definition from the recovery science research collaborative. Addiction Research & Theory, 27, 179–188. https://doi.org/10.1080/16066359.2018.1515352Google Scholar
Asparouhov, T., Hamaker, E. L., & Muthén, B. (2018). Dynamic structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 25, 359–388. https://doi.org/10.1080/10705511.2017.1406803CrossRefGoogle Scholar
Bray, J. W., Aden, B., Eggman, A. A., Hellerstein, L., Wittenberg, E., Nosyk, B., Stribling, J. C., & Schackman, B. R. (2017). Quality of life as an outcome of opioid use disorder treatment: A systematic review. Journal of Substance Abuse Treatment, 76, 88–93. https://doi.org/10.1016/j.jsat.2017.01.019CrossRefGoogle ScholarPubMed
Conrad, K. M., Conrad, K. J., Passetti, L. L., Funk, R. R., & Dennis, M. L. (2015). Validation of the full and short-form self-help involvement scale against the Rasch measurement model. Evaluation Review, 39, 395–427. https://doi.org/10.1177/0193841x15599645CrossRefGoogle ScholarPubMed
Dennis, M., Scott, C. K., & Funk, R. (2003a). An experimental evaluation of recovery management check-ups (RMC) for people with chronic substance use disorders. Evaluation and Program Planning, 26, 339–352. https://doi.org/10.1016/S0149-7189(03)00037-5Google Scholar
Dennis, M. L., Foss, M. A., & Scott, C. K. (2007). An eight-year perspective on the relationship between the duration of abstinence and other aspects of recovery. Evaluation Review, 31, 585–612. https://doi.org/10.1177/0193841X07307771Google Scholar
Dennis, M. L., & Scott, C. K. (2012). Four-year outcomes from the early re-intervention (ERI) experiment using recovery management check-ups (RMCs). Drug and Alcohol Dependence, 121, 10–17. https://doi.org/10.1016/j.drugalcdep.2011.07.026Google Scholar
Dennis, M. L., Scott, C. K., Funk, R., & Foss, M. A. (2005). The duration and correlates of addiction and treatment careers. Journal of Substance Abuse Treatment, 28 Suppl 1, S51–S62. https://doi.org/10.1016/j.jsat.2004.10.013Google Scholar
Dennis, M. L., Titus, J. C., White, M. K., & Unsicker, J. I. (2003b). Global appraisal of individual needs: Administration guide for the GAIN and related measures. Chestnut Health Systems. www.gaincc.org/instrumentsGoogle Scholar
Diederichs, C., Berger, K., & Bartels, D. B. (2011). The measurement of multiple chronic diseases—A systematic review on existing multimorbidity indices. The Journals of Gerontology: Series A, 66A, 301–311. https://academic.oup.com/biomedgerontology/article-abstract/66A/3/301/600233Google Scholar
Donovan, D. M., Bigelow, G. E., Brigham, G. S., Carroll, K. M., Cohen, A. J., Gardin, J. G., Hamilton, J. A., Huestis, M. A., Hughes, J. R., Lindblad, R., Marlatt, G. A., Preston, K. L., Selzer, J. A., Somoza, E. C., Wakim, P. G., Wells, E. A. (2012). Primary outcome indices in illicit drug dependence treatment research: Systematic approach to selection and measurement of drug use end-points in clinical trials. Addiction, 107, 694–708. https://doi.org/10.1111/j.1360-0443.2011.03473.xGoogle Scholar
Dziak, J. J., Li, R., & Wagner, A. T. (2017). WeightedTVEM SAS macro users’ guide (Version 2.6). [Computer software] https://aimlab.psu.edu/tvem/weighted-tvem-sas-macro/Google Scholar
Evans-Polce, R., & Schuler, M. S. (2016). Rates of past-year alcohol treatment across two time metrics and differences by alcohol use disorder severity and mental health comorbidities. Drug and Alcohol Dependence, 166, 194–201. https://doi.org/10.1016/j.drugalcdep.2016.07.010Google Scholar
Finney, J. W., Moyer, A., & Swearingen, C. E. (2003). Outcome variables and their assessment in alcohol treatment studies: 1968–1998. Alcoholism: Clinical and Experimental Research, 27, 1671–1679. https://doi.org/10.1097/01.Alc.0000091236.14003.E1CrossRefGoogle ScholarPubMed
Garner, B. R., Scott, C. K., Dennis, M. L., & Funk, R. R. (2014). The relationship between recovery and health-related quality of life. Journal of Substance Abuse Treatment, 47, 293–298. https://doi.org/10.1016/j.jsat.2014.05.006Google Scholar
Griffith, L. E., Gruneir, A., Fisher, K. A., Nicholson, K., Panjwani, D., Patterson, C., Markle-Reid, M., Ploeg, J., Bierman, A. S., Hogan, D. B., & Upshur, R. (2018). Key factors to consider when measuring multimorbidity: Results from an expert panel and online survey. Journal of Comorbidity, 8, 2235042X18795306. http://dx.doi.org/10.1177/2235042X18795306Google Scholar
Hastie, T., & Tibshirani, R. (1993). Varying-coefficient models. Journal of the Royal Statistical Society Series B (Methodological), 55, 757–796. https://doi.org/10.1111/j.2517-6161.1993.tb01939.xGoogle Scholar
Hoover, D. R., Rice, J. A., Wu, C. O., & Yang, L.-P. (1998). Nonparametric smoothing estimates of time-varying coefficient models with longitudinal data. Biometrika, 85, 809–822. https://doi.org/10.1093/biomet/85.4.809Google Scholar
Kelly, J. F., Greene, M. C., & Bergman, B. G. (2018). Beyond abstinence: Changes in indices of quality of life with time in recovery in a nationally representative sample of US adults. Alcoholism: Clinical and Experimental Research, 42, 770–780. https://doi.org/10.1111/acer.13604CrossRefGoogle Scholar
Kiluk, B. D., Frankforter, T. L., Cusumano, M., Nich, C., & Carroll, K. M. (2018). Change in DSM-5 alcohol use disorder criteria count and severity level as a treatment outcome indicator: Results from a randomized trial. Alcoholism: Clinical and Experimental Research, 42, 1556–1563. https://doi.org/10.1111/acer.13807Google Scholar
Lanza, S. T., & Linden-Carmichael, A. N. (2021). Time-varying effect modeling for the behavioral, social, and health sciences. Springer. doi: 10.1007/978-3-030-70944-0Google Scholar
Lanza, S. T., Vasilenko, S. A., & Russell, M. A. (2016). Time-varying effect modeling to address new questions in behavioral research: Examples in marijuana use. Psychology of Addictive Behaviors, 30, 939–954. https://doi.org/10.1037/adb0000208Google Scholar
Leadbeater, B. J., Ames, M. E., & Linden-Carmichael, A. N. (2019). Age-varying effects of cannabis use frequency and disorder on symptoms of psychosis, depression and anxiety in adolescents and adults. Addiction, 114, 278–293. https://doi.org/10.1111/add.14459Google Scholar
Li, R., Dziak, J. J., Tan, X., Huang, L., Wagner, A. T., & Yang, J. (2017). TVEM SAS macro users’ guide (Version 3.1.1). [Computer software] https://aimlab.psu.edu/tvem/tvem-sas-macro/Google Scholar
Li, R., Tan, X., Huang, L., Wagner, A. T., & Yang, J. (2015). %TVEM_zip (time‐varying effect model) SAS macro users’ guide (Version 2.1.1). [Computer software] The Methodology Center, Penn State. http://methodology.psu.eduGoogle Scholar
Linden-Carmichael, A. N., Dziak, J. J., & Lanza, S. T. (2018). Dynamic features of problematic drinking: Alcohol use disorder latent classes across ages 18–64. Alcohol and Alcoholism, 54, 97–103. https://doi.org/10.1093/alcalc/agy074Google Scholar
Linden-Carmichael, A. N., Vasilenko, S. A., Lanza, S. T., & Maggs, J. L. (2017). High-intensity drinking versus heavy episodic drinking: Prevalence rates and relative odds of alcohol use disorder across adulthood. Alcoholism: Clinical and Experimental Research, 41, 1754–1759. https://doi.org/10.1111/acer.13475CrossRefGoogle ScholarPubMed
McKay, J. R. (2001). Effectiveness of continuing care interventions for substance abusers: Implications for the study of long-term treatment effects. Evaluation Review, 25, 211–232. https://doi.org/10.1177/0193841x0102500205Google Scholar
McLellan, A. T., McKay, J. R., Forman, R., Cacciola, J., & Kemp, J. (2005). Reconsidering the evaluation of addiction treatment: From retrospective follow-up to concurrent recovery monitoring. Addiction, 100, 447–458. https://doi.org/10.1111/j.1360-0443.2005.01012.xGoogle Scholar
Moos, R. H. (1993). Coping Responses Inventory Youth form: Professional manual. Psychological Assessment Resources.Google Scholar
Moos, R. H., & Moos, B. S. (2007). Protective resources and long-term recovery from alcohol use disorders. Drug and Alcohol Dependence, 86, 46–54. https://doi.org/10.1016/j.drugalcdep.2006.04.015Google Scholar
National Academies of Sciences, Engineering, and Medicine (2016). Measuring recovery from substance use or mental disorders: Workshop summary. The National Academies Press. https://doi.org/10.17226/23589Google Scholar
Nicholson, K., Almirall, J., & Fortin, M. (2019). The measurement of multimorbidity. Health Psychology, 38(9), 783–790. https://doi.org/10.1037/hea0000739Google Scholar
Scott, C. K., & Dennis, M. L. (2003). Recovery Management Check-ups: An early re-intervention model. Chestnut Health Systems.Google Scholar
Scott, C. K., & Dennis, M. L. (2009). Results from two randomized clinical trials evaluating the impact of quarterly recovery management check-ups with adult chronic substance users. Addiction, 104, 959–971. https://doi.org/10.1111/j.1360-0443.2009.02525.xGoogle Scholar
Scott, C. K., & Dennis, M. L. (2010). Recovery management check-ups with adult chronic substance users. In Kelly, J. F. & White, W. L. (Eds.), Current clinical psychiatry. Addiction recovery management: Theory, research, and practice (pp. 87–101). Humana Press.Google Scholar
Scott, C. K., Dennis, M. L., & Foss, M. A. (2005). Utilizing Recovery Management Check-ups to shorten the cycle of relapse, treatment re-entry, and recovery. Drug and Alcohol Dependence, 78, 325–338. https://doi.org/10.1016/j.drugalcdep.2004.12.005Google Scholar
Simpson, C. A., & Tucker, J. A. (2002). Temporal sequencing of alcohol-related problems, problem recognition, and help-seeking episodes. Addictive Behaviors, 27, 659–674. https://doi.org/10.1016/s0306-4603(01)00200-3Google Scholar
Simpson, D. D., Joe, G. W., & Broome, K. M. (2002). A national 5-year follow-up of treatment outcomes for cocaine dependence. Archives of General Psychiatry, 59, 538–544. https://doi.org/10.1001/archpsyc.59.6.538Google Scholar
Stull, S. W., Panlilio, L. V., Moran, L. M., Schroeder, J. R., Bertz, J. W., Epstein, D. H., Preston, K. L., Phillips, K. A. (2019). The chippers, the quitters, and the highly symptomatic: A 12-month longitudinal study of DSM-5 opioid- and cocaine-use problems in a community sample. Addictive Behaviors, 96, 183–191. https://doi.org/10.1016/j.addbeh.2019.04.030Google Scholar
Tan, X., Shiyko, M. P., Li, R., Li, Y., & Dierker, L. (2012). A time-varying effect model for intensive longitudinal data. Psychological Methods, 17, 61–77. https://doi.org/10.1037/a0025814Google Scholar
White, W. L. (2007). Addiction recovery: Its definition and conceptual boundaries. Journal of Substance Abuse Treatment, 33, 229–241. https://doi.org/10.1016/j.jsat.2007.04.015Google Scholar
Wilson, A. D., Bravo, A. J., Pearson, M. R., & Witkiewitz, K. (2016). Finding success in failure: Using latent profile analysis to examine heterogeneity in psychosocial functioning among heavy drinkers following treatment. Addiction, 111, 2145–2154. https://doi.org/10.1111/add.13518Google Scholar
Witkiewitz, K., Heather, N., Falk, D. E., Litten, R. Z., Hasin, D. S., Kranzler, H. R., Mann, K. F., O’Malley, S. S., & Anton, R. F. (2020). World Health Organization risk drinking level reductions are associated with improved functioning and are sustained among patients with mild, moderate, and severe alcohol dependence in clinical trials in the United States and United Kingdom. Addiction, 115(9), 1668–1680. https://doi.org/10.1111/add.15011CrossRefGoogle ScholarPubMed
Witkiewitz, K., Pearson, M. R., Hallgren, K. A., Maisto, S. A., Roos, C. R., Kirouac, M., Wilson, A. D., Montes, K. S., & Heather, N. (2017). Who achieves low-risk drinking during alcohol treatment? An analysis of patients in three alcohol clinical trials. Addiction, 112(12), 2112–2121. https://doi.org/10.1111/add.13870.Google Scholar
Witkiewitz, K., & Tucker, J. A. (2020). Abstinence not required: Expanding the definition of recovery from alcohol use disorder. Alcoholism: Clinical and Experimental Research, 44, 36–40. https://doi.org/10.1111/acer.14235Google Scholar
Wu, L. T., Zhu, H., & Ghitza, U. E. (2018). Multicomorbidity of chronic diseases and substance use disorders and their association with hospitalization: Results from electronic health records data. Drug and Alcohol Dependence, 192, 316–323. https://doi.org/10.1016/j.drugalcdep.2018.08.013Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×