418
Commentary
and the investigation of relational factors. In meta-analyses some variation will always exist, whether due to chance or to differences in trial context or content [5,6]. On its own, variance in a meta-analysis does not de-legitimize that meta-analysis, nor does it necessarily mask key causes of variation. Indeed, once these relational factors are specified, measured and tested in the same way in which specific factors are currently dealt with, meta-analyses of the trials of these new tests would still be subject to variation in outcome due to remaining unspecified factors. Miller & Moyers are right to point out that, in focusing exclusively on specific factors related to treatment content, research into addiction treatments may be overlooking important relational factors and their associated effects. However, in acknowledging genuine and measurable causes of variation—more trees, arguably—it is important that a true forest view is not obscured. Using existing methodology, findings can be aggregated without masking the impact of underlying mechanisms, as long as these potential mechanisms are identified in advance. Relational factors that can be measured empirically and in which therapists can be trained can be tested in randomized controlled trials, as has recently been done with empathy in the context of physician training [7,8]. Such trials could then be aggregated in meta-analyses. Even where not tested directly, the contribution of these relational factors could be examined in systematic reviews through meta-regression, as has already been performed with specific factors [9,10]. The trees and the forest can both be taken into account, but it is important not to lose sight of which is which.
2. Gearing R. E., El-Bassel N., Ghesquiere A., Baldwin S., Gillies J., Ngeow E. Major ingredients of fidelity: a review and scientific guide to improving quality of intervention research implementation. Clin Psychol Rev 2011; 31: 79–88. 3. Robb S. L., Burns D. S., Docherty S. L., Haase J. E. Ensuring treatment fidelity in a multi-site behavioral intervention study: implementing NIH behavior change consortium recommendations in the SMART trial. Psycho-Oncology 2011; 20: 1193–201. 4. Norcross J. C., Wampold B. E. Evidence-based therapy relationships: research conclusions and clinical practices. Psychotherapy 2011; 48: 98 –102. 5. Higgins J. P., Thompson S. G., Deeks J. J., Altman D. G. Measuring inconsistency in meta-analyses. BMJ 2003; 327: 557– 60. 6. Higgins J., Thompson S., Deeks J., Altman D. Statistical heterogeneity in systematic reviews of clinical trials: a critical appraisal of guidelines and practice. J Health Serv Res Policy 2002; 7: 51– 61. 7. Riess H., Kraft-Todd G. E.M.P.A.T.H.Y.: a tool to enhance nonverbal communication between clinicians and their patients. Acad Med 2014; 89: 1108 –12. 8. Riess H., Kelley J. M., Bailey R. W., Dunn E. J., Phillips M. Empathy training for resident physicians: a randomized controlled trial of a neuroscience-informed curriculum. J Gen Intern Med 2012; 27: 1280 –6. 9. Magill M., Ray L. A. Cognitive–behavioral treatment with adult alcohol and illicit drug users: a meta-analysis of randomized controlled trials. J Stud Alcohol Drugs 2009; 70: 516. 10. Michie S., Whittington C., Hamoudi Z., Zarnani F., Tober G., West R. Identification of behaviour change techniques to reduce excessive alcohol consumption. Addiction 2012; 107: 1431– 40.
Declaration of interests
We thank our colleagues for their thoughtful commentaries. Such discussion is what we hoped for in publishing this monograph. Bergmark [1] defends the continued use of the term ‘common factors’. It remains unclear just how common such factors like empathy actually are across treatments and providers. We hardily disagree that relational factors are ‘incidental’ in treatment theory, which instead should be expanded to include such factors. It is not either/or, but both/and. Hartmann-Boyce [2] defends meta-analytical aggregation of findings and worries that specifying ‘non-specific’ factors simply plants more trees. Happily, multivariate analyses have evolved far beyond those required for a horse race or an effect size. The challenge is to examine the simultaneous impact of specified factors within the context of other important factors. Meta-analyses should take into account intervention fidelity [3] and conditions with which treatments are compared [4]. Magill [5] expands on the paucity of differences when specific therapies are compared with treatment as usual, the closest thing to a placebo control in psychotherapy research. She calls for better study of standard care, a
None. Acknowledgements J.H.-B. receives funding from the National Institute for Health Research School for Primary Care Research (NIHR SPCR). The views expressed in this research are those of the author and not necessarily those of the NHS, the NIHR or the Department of Health. Keywords Common factors, evidence, outcome, specific factors, treatment, research methods. JAMIE HARTMANN-BOYCE Nuffield Department of Primary Care Health Sciences, University of Oxford, UK. E-mail: [email protected] References 1. Miller W. R., Moyers T. B. The forest and the trees: relational and specific factors in addiction treatment. Addiction 2015; 110: 401–413. © 2015 Society for the Study of Addiction
ON HAVING AND EATING CAKE
Addiction, 110, 414–419
Commentary
different genre of research than testing specific therapy brands [6], challenging the field to develop methods that are sufficiently creative to model complex interactive processes. Bricker [7] wants to climb the trees for a better view. Linking therapy processes with outcome lies at the heart of ‘mechanisms of action’ studies already well advanced in addiction treatment [8,9]. Removing therapists from the equation through standardized delivery has been around for some time in ‘bibliotherapy’ research [10,11], and more recently in computer- and web-based interventions that can reach larger populations [12,13]. This does not obviate contextual characteristics; specific aspects of delivery mode may still influence outcome [14]. Similarly, cost-effectiveness research has a long history in addiction treatment [15,16]. Our field has over-relied on searching for superior specific treatment agents [5]. A psychotherapy cannot be separated from the therapist (or even technology) delivering it. Testing specific hypotheses remains important, but a broader view is needed. Models including a wider range of relational and contextual factors could increase the amount of outcome variance accounted for, and may even be useful in pharmacotherapy research. Both medication and placebo are delivered by staff who interact with patients, and the effectiveness of placebo conditions can vary widely within a multi-site trial [17]. Some ‘placebos’ are more effective than others, perhaps owing to relational and contextual factors that can affect the likelihood of finding a medication effect. Much is going on in treatment beyond the posited effect of any specific factor. Psychotherapies are complex, inseparable from provider and context. Manual-guided treatments may contain ‘active’ ingredients that promote change, irrelevant superstitious elements and even iatrogenic components that impede change. Providers vary in fidelity of the intended treatment and a host of relational factors [18]. Treatment effectiveness is unlikely to advance much further by simply examining one tree at a time or comparing any two or three. Understanding the forest has, we think, been obscured by standing too close to the trees. Declaration of interests None. Keywords Common factors, empathy, therapeutic relationship, meta-analysis, treatment effectiveness, therapist effects. WILLIAM R. MILLER & THERESA B. MOYERS
Center on Alcoholism, Substance Abuse and Addictions (CASAA), The University of New Mexico, Albuquerque, NM, USA E-mail: [email protected] © 2015 Society for the Study of Addiction
419
References 1. Bergmark A. Where is the forest? Addiction 2015; 110: 421. 2. Hartmann-Boyce J. Specific versus relational factors in addiction treatment: the forest and the trees, or just more trees? Addiction 2015; 110: 422–423. 3. Miller W. R., Rollnick S. The effectiveness and ineffectiveness of complex behavioral interventions: impact of treatment fidelity. Contemp Clin Trials 2014; 37: 234–41. 4. Karlsson P., Bergmark A. Compared with what? An analysis of control group types in Cochrane and Campbell reviews of psychosocial treatment efficacy with substance use disorders. Addiction 2015; 110: 420–428. 5. Magill M. Branding addiction therapies and reified specific factors. Addiction 2015; 110: 419–420. 6. Martino D., Ball S. A., Nich C., Frankforter T. C., Carroll K. M. Informal discussions in substance abuse treatment sessions. J Subst Abuse Treat 2009; 36: 366–75. 7. Bricker J. Climbing above the forest and the trees: three future directions in addiction treatment research. Addiction, 2015; in press. 8. Longabaugh R., Magill M., Morgenstern J., Huebner R. Mechanisms of behavior change in treatment for alcohol and other drug use disorders. In: McCrady B. M., Epstein E. E., eds. Addictions: a Comprehensive Guidebook, 2nd edn. New York: Oxford Press; 2013, pp. 572–96. 9. Morgenstern J., Blanchard K. A., Kahler C., Barbosa K. M., McCrady B. S., McVeigh K. H. Testing mechanisms of action for intensive case management. Addiction 2008; 103: 469–77. 10. Christensen A., Miller W. R., Muñoz R. F. Paraprofessionals, partners, peers, paraphernalia, and print: expanding mental health service delivery. Prof Psychol 1978; 9: 249–70. 11. Apodaca T. R., Miller W. R. A meta-analysis of the effectiveness of bibliotherapy for alcohol problems. J Clin Psychol 2003; 59: 289–304. doi: 10.1002/jclp.10130. 12. Muñoz R. F., Lenert L. L., Delucchi K., Stoddard J., Perez J. E., Penilla C. et al. Toward evidence-based internet interventions: a Spanish/English web site for international smoking cessation trials. Nicotine Tob Res 2006; 8: 77–87. 13. Hester R. K., Squires D. D., Delaney H. D. The drinker’s checkup: 12-month outcomes of a controlled clinical trial of a stand-alone software program for problem drinkers. J Subst Abuse 2005; 28: 159–69. 14. Miller W. R. Interactive journaling as a clinical tool. J Mental Health Counsel 2014; 36: 31–42. 15. Longabaugh R., McCrady B., Fink E., Stout R., McAuley T., Doyle C. et al. Cost-effectiveness of alcoholism treatment in partial vs. inpatient settings: six-month outcomes. J Stud Alcohol 1983; 44: 1049–71. 16. Holder H. D., Longabaugh R., Miller W. R., Rubonis A. V. The cost effectiveness of treatment for alcoholism: a first approximation. J Stud Alcohol 1991; 52: 517–40. 17. Anton R. F., O’Malley S. S., Ciraulo D. A., Couper D., Donovan D. M., Gastfriend D. R. et al. Combined pharmacotherapies and behavioral interventions for alcohol dependence. The COMBINE study: a randomized controlled trial. JAMA 2006; 295: 2003–17. 18. Norcross J. C. editor. Psychotherapy Relationships That Work: Therapist Contributions and Responsiveness to Patients. New York: Oxford University Press; 2002.
Addiction, 110, 414–419
Commentary
and the investigation of relational factors. In meta-analyses some variation will always exist, whether due to chance or to differences in trial context or content [5,6]. On its own, variance in a meta-analysis does not de-legitimize that meta-analysis, nor does it necessarily mask key causes of variation. Indeed, once these relational factors are specified, measured and tested in the same way in which specific factors are currently dealt with, meta-analyses of the trials of these new tests would still be subject to variation in outcome due to remaining unspecified factors. Miller & Moyers are right to point out that, in focusing exclusively on specific factors related to treatment content, research into addiction treatments may be overlooking important relational factors and their associated effects. However, in acknowledging genuine and measurable causes of variation—more trees, arguably—it is important that a true forest view is not obscured. Using existing methodology, findings can be aggregated without masking the impact of underlying mechanisms, as long as these potential mechanisms are identified in advance. Relational factors that can be measured empirically and in which therapists can be trained can be tested in randomized controlled trials, as has recently been done with empathy in the context of physician training [7,8]. Such trials could then be aggregated in meta-analyses. Even where not tested directly, the contribution of these relational factors could be examined in systematic reviews through meta-regression, as has already been performed with specific factors [9,10]. The trees and the forest can both be taken into account, but it is important not to lose sight of which is which.
2. Gearing R. E., El-Bassel N., Ghesquiere A., Baldwin S., Gillies J., Ngeow E. Major ingredients of fidelity: a review and scientific guide to improving quality of intervention research implementation. Clin Psychol Rev 2011; 31: 79–88. 3. Robb S. L., Burns D. S., Docherty S. L., Haase J. E. Ensuring treatment fidelity in a multi-site behavioral intervention study: implementing NIH behavior change consortium recommendations in the SMART trial. Psycho-Oncology 2011; 20: 1193–201. 4. Norcross J. C., Wampold B. E. Evidence-based therapy relationships: research conclusions and clinical practices. Psychotherapy 2011; 48: 98 –102. 5. Higgins J. P., Thompson S. G., Deeks J. J., Altman D. G. Measuring inconsistency in meta-analyses. BMJ 2003; 327: 557– 60. 6. Higgins J., Thompson S., Deeks J., Altman D. Statistical heterogeneity in systematic reviews of clinical trials: a critical appraisal of guidelines and practice. J Health Serv Res Policy 2002; 7: 51– 61. 7. Riess H., Kraft-Todd G. E.M.P.A.T.H.Y.: a tool to enhance nonverbal communication between clinicians and their patients. Acad Med 2014; 89: 1108 –12. 8. Riess H., Kelley J. M., Bailey R. W., Dunn E. J., Phillips M. Empathy training for resident physicians: a randomized controlled trial of a neuroscience-informed curriculum. J Gen Intern Med 2012; 27: 1280 –6. 9. Magill M., Ray L. A. Cognitive–behavioral treatment with adult alcohol and illicit drug users: a meta-analysis of randomized controlled trials. J Stud Alcohol Drugs 2009; 70: 516. 10. Michie S., Whittington C., Hamoudi Z., Zarnani F., Tober G., West R. Identification of behaviour change techniques to reduce excessive alcohol consumption. Addiction 2012; 107: 1431– 40.
Declaration of interests
We thank our colleagues for their thoughtful commentaries. Such discussion is what we hoped for in publishing this monograph. Bergmark [1] defends the continued use of the term ‘common factors’. It remains unclear just how common such factors like empathy actually are across treatments and providers. We hardily disagree that relational factors are ‘incidental’ in treatment theory, which instead should be expanded to include such factors. It is not either/or, but both/and. Hartmann-Boyce [2] defends meta-analytical aggregation of findings and worries that specifying ‘non-specific’ factors simply plants more trees. Happily, multivariate analyses have evolved far beyond those required for a horse race or an effect size. The challenge is to examine the simultaneous impact of specified factors within the context of other important factors. Meta-analyses should take into account intervention fidelity [3] and conditions with which treatments are compared [4]. Magill [5] expands on the paucity of differences when specific therapies are compared with treatment as usual, the closest thing to a placebo control in psychotherapy research. She calls for better study of standard care, a
None. Acknowledgements J.H.-B. receives funding from the National Institute for Health Research School for Primary Care Research (NIHR SPCR). The views expressed in this research are those of the author and not necessarily those of the NHS, the NIHR or the Department of Health. Keywords Common factors, evidence, outcome, specific factors, treatment, research methods. JAMIE HARTMANN-BOYCE Nuffield Department of Primary Care Health Sciences, University of Oxford, UK. E-mail: [email protected] References 1. Miller W. R., Moyers T. B. The forest and the trees: relational and specific factors in addiction treatment. Addiction 2015; 110: 401–413. © 2015 Society for the Study of Addiction
ON HAVING AND EATING CAKE
Addiction, 110, 414–419
Commentary
different genre of research than testing specific therapy brands [6], challenging the field to develop methods that are sufficiently creative to model complex interactive processes. Bricker [7] wants to climb the trees for a better view. Linking therapy processes with outcome lies at the heart of ‘mechanisms of action’ studies already well advanced in addiction treatment [8,9]. Removing therapists from the equation through standardized delivery has been around for some time in ‘bibliotherapy’ research [10,11], and more recently in computer- and web-based interventions that can reach larger populations [12,13]. This does not obviate contextual characteristics; specific aspects of delivery mode may still influence outcome [14]. Similarly, cost-effectiveness research has a long history in addiction treatment [15,16]. Our field has over-relied on searching for superior specific treatment agents [5]. A psychotherapy cannot be separated from the therapist (or even technology) delivering it. Testing specific hypotheses remains important, but a broader view is needed. Models including a wider range of relational and contextual factors could increase the amount of outcome variance accounted for, and may even be useful in pharmacotherapy research. Both medication and placebo are delivered by staff who interact with patients, and the effectiveness of placebo conditions can vary widely within a multi-site trial [17]. Some ‘placebos’ are more effective than others, perhaps owing to relational and contextual factors that can affect the likelihood of finding a medication effect. Much is going on in treatment beyond the posited effect of any specific factor. Psychotherapies are complex, inseparable from provider and context. Manual-guided treatments may contain ‘active’ ingredients that promote change, irrelevant superstitious elements and even iatrogenic components that impede change. Providers vary in fidelity of the intended treatment and a host of relational factors [18]. Treatment effectiveness is unlikely to advance much further by simply examining one tree at a time or comparing any two or three. Understanding the forest has, we think, been obscured by standing too close to the trees. Declaration of interests None. Keywords Common factors, empathy, therapeutic relationship, meta-analysis, treatment effectiveness, therapist effects. WILLIAM R. MILLER & THERESA B. MOYERS
Center on Alcoholism, Substance Abuse and Addictions (CASAA), The University of New Mexico, Albuquerque, NM, USA E-mail: [email protected] © 2015 Society for the Study of Addiction
419
References 1. Bergmark A. Where is the forest? Addiction 2015; 110: 421. 2. Hartmann-Boyce J. Specific versus relational factors in addiction treatment: the forest and the trees, or just more trees? Addiction 2015; 110: 422–423. 3. Miller W. R., Rollnick S. The effectiveness and ineffectiveness of complex behavioral interventions: impact of treatment fidelity. Contemp Clin Trials 2014; 37: 234–41. 4. Karlsson P., Bergmark A. Compared with what? An analysis of control group types in Cochrane and Campbell reviews of psychosocial treatment efficacy with substance use disorders. Addiction 2015; 110: 420–428. 5. Magill M. Branding addiction therapies and reified specific factors. Addiction 2015; 110: 419–420. 6. Martino D., Ball S. A., Nich C., Frankforter T. C., Carroll K. M. Informal discussions in substance abuse treatment sessions. J Subst Abuse Treat 2009; 36: 366–75. 7. Bricker J. Climbing above the forest and the trees: three future directions in addiction treatment research. Addiction, 2015; in press. 8. Longabaugh R., Magill M., Morgenstern J., Huebner R. Mechanisms of behavior change in treatment for alcohol and other drug use disorders. In: McCrady B. M., Epstein E. E., eds. Addictions: a Comprehensive Guidebook, 2nd edn. New York: Oxford Press; 2013, pp. 572–96. 9. Morgenstern J., Blanchard K. A., Kahler C., Barbosa K. M., McCrady B. S., McVeigh K. H. Testing mechanisms of action for intensive case management. Addiction 2008; 103: 469–77. 10. Christensen A., Miller W. R., Muñoz R. F. Paraprofessionals, partners, peers, paraphernalia, and print: expanding mental health service delivery. Prof Psychol 1978; 9: 249–70. 11. Apodaca T. R., Miller W. R. A meta-analysis of the effectiveness of bibliotherapy for alcohol problems. J Clin Psychol 2003; 59: 289–304. doi: 10.1002/jclp.10130. 12. Muñoz R. F., Lenert L. L., Delucchi K., Stoddard J., Perez J. E., Penilla C. et al. Toward evidence-based internet interventions: a Spanish/English web site for international smoking cessation trials. Nicotine Tob Res 2006; 8: 77–87. 13. Hester R. K., Squires D. D., Delaney H. D. The drinker’s checkup: 12-month outcomes of a controlled clinical trial of a stand-alone software program for problem drinkers. J Subst Abuse 2005; 28: 159–69. 14. Miller W. R. Interactive journaling as a clinical tool. J Mental Health Counsel 2014; 36: 31–42. 15. Longabaugh R., McCrady B., Fink E., Stout R., McAuley T., Doyle C. et al. Cost-effectiveness of alcoholism treatment in partial vs. inpatient settings: six-month outcomes. J Stud Alcohol 1983; 44: 1049–71. 16. Holder H. D., Longabaugh R., Miller W. R., Rubonis A. V. The cost effectiveness of treatment for alcoholism: a first approximation. J Stud Alcohol 1991; 52: 517–40. 17. Anton R. F., O’Malley S. S., Ciraulo D. A., Couper D., Donovan D. M., Gastfriend D. R. et al. Combined pharmacotherapies and behavioral interventions for alcohol dependence. The COMBINE study: a randomized controlled trial. JAMA 2006; 295: 2003–17. 18. Norcross J. C. editor. Psychotherapy Relationships That Work: Therapist Contributions and Responsiveness to Patients. New York: Oxford University Press; 2002.
Addiction, 110, 414–419
