Translational Behavioral Medicine

, Volume 6, Issue 1, pp 125–134 | Cite as

Illustrating idiographic methods for translation research: moderation effects, natural clinical experiments, and complex treatment-by-subgroup interactions

  • Ty A Ridenour
  • Andrea K Wittenborn
  • Bethany R Raiff
  • Neal Benedict
  • Sandra Kane-Gill
Original Research

Abstract

A critical juncture in translation research involves the preliminary studies of intervention tools, provider training programs, policies, and other mechanisms used to leverage knowledge garnered at one translation stage into another stage. Potentially useful for such studies are rigorous techniques for conducting within-subject clinical trials, which have advanced incrementally over the last decade. However, these methods have largely not been utilized within prevention or translation contexts. The purpose of this manuscript is to demonstrate the flexibility, wide applicability, and rigor of idiographic clinical trials for preliminary testing of intervention mechanisms. Specifically demonstrated are novel uses of state-space modeling for testing intervention mechanisms of short-term outcomes, identifying heterogeneity in and moderation of within-person treatment mechanisms, a horizontal line plot to refine sampling design during the course of a clinic-based experimental study, and the need to test a treatment’s efficacy as treatment is administered along with (e.g., traditional 12-month outcomes).

Keywords

Trajectory analysis State-space modeling Translation Prevention 

Notes

Acknowledgments

This was an investigator-initiated study funded by grants from the National Institute on Drug Abuse (P50 05605), National Institute of Child Health and Human Development (R21 061683), an investigator-initiated grant from Hospira, Inc., and Fahs-Beck Fund for Research and Experimentation and the Virginia Tech College of Liberal Arts and Human Sciences Dean’s Faculty Fellowship. The funders played no role in the design, conduct, or analysis of the study nor in the interpretation and reporting of the study findings.

Compliance with ethical standards

Conflict of interest

Ty Ridenour, Andrea Wittenborn, Bethany Raiff, Neal Benedict, and Sandra Kane-Gill have no conflict of interests to declare regarding the research reported herein.

Adherence to ethical principles

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee, overseen by the institutional review boards of the institutions where the study was conducted, and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

References

  1. 1.
    Fishbein DH, Sussman S, Ridenour TA, Herman-Stahl M. Expanding the translational spectrum of prevention science: emerging research to support scaling up proven practices that prevent behavioral health problems. Transl Behav Med. This issue.Google Scholar
  2. 2.
    Dishion TJ, Spracklen KM, Andrews DW, Patterson GR. Deviancy training in male adolescent friendships. Behav Ther. 1996; 27: 373-390.CrossRefGoogle Scholar
  3. 3.
    MacKinnon DP. Introduction to statistical mediation analysis. NY: Lawrence Erlbaum; 2008.Google Scholar
  4. 4.
    Edwards JR, Lambert LS. Methods for integrating moderation and mediation: a general analytical framework using moderated path analysis. Psychol Meth. 2007; 12: 1.CrossRefGoogle Scholar
  5. 5.
    National Institutes of Health. NIH funds research consortia to study more than 200 rare diseases: $29 million awarded to expand NCATS’ collaborative Rare Diseases Clinical Research Network. Accessed on Jan 6, 2015 at: www.nih.gov/news/health/oct2014/ncats-08.htm. 2014.
  6. 6.
    Ridenour TA, Pineo TZ, Maldonado-Molina MM, Hassmiller-Lich K. Toward idiographic research in prevention science: demonstration of three techniques for rigorous small sample research. Prev Sci. 2013; 14: 267-278.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Franklin RD, Gorman BS, Beasley TM, Allison DB. Graphical display and visual analysis. In: Franklin RD, Allison DB, Gorman RS, eds. Design and analysis of single-case research. Mahwah, NJ: Lawrence Erlbaum; 1997: 119-158.Google Scholar
  8. 8.
    Smith JD. Single-case experimental designs: a systematic review of published research and current standards. Psychol Method. 2012; 17: 510-550.CrossRefGoogle Scholar
  9. 9.
    Hedeker D, Gibbons RD. Longitudinal data analysis. Hoboken, NJ: Wiley; 2006.Google Scholar
  10. 10.
    Singer JD, Willett JB. Applied Longitudinal Data Analysis. New York: Oxford: 2003.Google Scholar
  11. 11.
    Chow SM, Ho MHR, Hamaker EL, Dolan CV. Equivalence and differences between structural equation modeling and state-space modeling techniques. Struct Equat Model. 2010; 17: 303-332.CrossRefGoogle Scholar
  12. 12.
    Molenaar PC, Huizenga HM, Nesselroade JR. The relationship between the structure of interindividual and intraindividual variability: a theoretical and empirical vindication of developmental systems theory. In: Understanding human development. US: Springer; 2003: 339-360.CrossRefGoogle Scholar
  13. 13.
    Zheng Y, Wiebe RP, Cleveland HH, Molenaar PC, Harris KS. An idiographic examination of day-to-day patterns of substance use craving, negative affect, and tobacco use among young adults in recovery. Multivariate Behav Res. 2013; 48: 241-266.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Gu F, Preacher KJ, Ferrer E. A state space modeling approach to mediation analysis. J Educ Behav Stat. 2014; 39: 117-143.CrossRefGoogle Scholar
  15. 15.
    Liao P, Klasnja P, Tewari A, Murphy SA. Micro-Randomized Trials in mHealth. arXiv preprint arXiv:1504.00238.Google Scholar
  16. 16.
    Bobashev GV, Liao D, Hampton J, Helzer JE. Individual patterns of alcohol use. Addict Behav. 2014; 39: 934-940.CrossRefPubMedGoogle Scholar
  17. 17.
    Kessler RC, Berglund P, Demler O, et al. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA. 2003; 289: 3095-3105.CrossRefPubMedGoogle Scholar
  18. 18.
    Centers for Disease Control and Prevention. Web-based injury statistics query and reporting system (WISQARS). Retrieved from: www.cdc.gov/injury/wisqars/index.html 2011.
  19. 19.
    Simpson HB, Nee JC, Endicott J. First-episode major depression: few sex differences in course. Arch General Psychiatr. 1997; 54: 633-639.CrossRefGoogle Scholar
  20. 20.
    Wittenborn AK, Culpepper B, Liu T. Treating depression in men: the role of emotionally focused couple therapy. Contemp Fam Ther. 2012; 34: 89-103.CrossRefGoogle Scholar
  21. 21.
    Barbato A, D’Avanzo B. Efficacy of couple therapy as a treatment for depression: a meta-analysis. Psychiatr Quart. 2008; 79: 121-132.CrossRefGoogle Scholar
  22. 22.
    Granger CWJ. Investigating causal relations by econometric models and cross-spectral methods. Econometrica. 1969; 37: 424-428.CrossRefGoogle Scholar
  23. 23.
    Shiffman S. Ecological momentary assessment (EMA) in studies of substance use. Psychol Assess. 2009; 21: 486-497.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Wittenborn AK, Liu T, Ridenour TA, Seedall RB. Emotionally focused therapy for depression: a rigorous pilot randomized controlled trial. Under review.Google Scholar
  25. 25.
    Beck AT, Steer RA, Brown GK. Manual for the Beck Depression Inventory-II. San Antonio, TX: Psychological Corporation; 1996.Google Scholar
  26. 26.
    Spanier GB. Measuring dyadic adjustment: new scales for assessing the quality of marriage and similar dyads. J Marriage Fam Ther. 1976; 38: 15-28.CrossRefGoogle Scholar
  27. 27.
    Jacobi J, Fraser GL, Coursin DB, et al. Clinical practice guidelines for the sustained use of sedatives and analgesics in the critically ill adult. Crit Care Med. 2002; 30: 119-141.CrossRefPubMedGoogle Scholar
  28. 28.
    Jackson DL, Proudfood CW, Cann KF, et al. The incidence of sub-optimal sedation in the ICU: a systematic review. Crit Care. 2009; 13: R204.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Benedict N, Felbinger M, Ridenour TA, Anthes A, Altawalbeh S, Kane-Gill S. Correlation of patient reported outcomes of sedation and sedation assessment scores in critically ill patients. J Crit Care. 2014; 29: 1132.e5-1132.e9.CrossRefGoogle Scholar
  30. 30.
    Corbett SM, Rebuck JA, Greene CM, et al. Dexmedetomidine does not improve patient satisfaction when compared with propofol during mechanical ventilation. Crit Care Med. 2005; 33: 940-945.CrossRefPubMedGoogle Scholar
  31. 31.
    Riker RR, Picard JT, Fraser GL. Prospective evaluation of the sedation-agitation scale for adult critically ill patients. Crit Care Med. 1999; 27: 1325-1329.CrossRefPubMedGoogle Scholar
  32. 32.
    Tueller S. longCatEDA: Package for Plotting Categorical Longitudinal and Time-Series Data. R package version 0.17. 2014.Google Scholar
  33. 33.
    Centers for Disease Control and Prevention. FastStats—leading causes of death. (n.d.). Accessed September 16, 2014, from www.cdc.gov/nchs/fastats/leading-causes-of-death.htm.
  34. 34.
    Centers for Disease Control and Prevention. National Diabetes Fact Sheet—Publications—Diabetes DDT. (n.d.). Accessed Feb 6, 2012, from www.cdc.gov/diabetes/pubs/factsheet11.htm.
  35. 35.
    Crowley R, Wolfe I, Lock K, McKee M. Improving the transition between paediatric and adult healthcare: a systematic review. Arch Dis Child. 2011 archdischild202473.Google Scholar
  36. 36.
    Berg CA, Wiebe DJ, Suchy Y, et al. Individual differences and day-to-day fluctuations in perceived self-regulation associated with daily adherence in late adolescents with type 1 diabetes. J Pediatr Psychol. 2014; 39: 1038-1048.CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Haller MJ, Stalvey MS, Silverstein JH. Predictors of control of diabetes: monitoring may be the key. J Pediatr. 2004; 144: 660-661.CrossRefPubMedGoogle Scholar
  38. 38.
    Stratton IM, Adler AI, Neil HAW, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ. 2000; 321: 405-412.CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Raiff BR, Barry VB, Jitnarin N, Ridenour TA. Internet-based incentives increase blood glucose testing with a non-adherent, diverse sample of teens with type 1diabetes: a randomized, controlled trial. Under Rev.Google Scholar
  40. 40.
    Raiff B, Dallery J. Internet-based contingency management to improve adherence with blood glucose testing recommendations for teens with type 1 diabetes. J Appl Behav Anal. 2010; 43: 487-491.CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Institute of Medicine. Committee on Quality of Health Care in America. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: National Academy Press; 2001.Google Scholar

Copyright information

© Society of Behavioral Medicine 2015

Authors and Affiliations

  • Ty A Ridenour
    • 1
    • 2
  • Andrea K Wittenborn
    • 3
  • Bethany R Raiff
    • 4
  • Neal Benedict
    • 2
  • Sandra Kane-Gill
    • 2
  1. 1.RTI, InternationalResearch Triangle ParkUSA
  2. 2.University of PittsburghPittsburghUSA
  3. 3.Michigan State UniversityEast LansingUSA
  4. 4.Rowan UniversityGlassboroUSA

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