Commentary to be congruent with the ‘common factor ‘ approach , such an interpretation is hard to maintain due to the fact that Miller & Moyers also claim that terms such as ‘common’ and ‘non-speciﬁc’ factors are misnomers. They claim that it is a mistake to refer to an important treatment factor as ‘non-speciﬁc’, and that to the extent that such factors are present we should specify them and develop an understanding of how they the work as well as of how they can be measured and incorporated into therapist training. Here it appears that Miller & Moyers assert that any factor that is important is also a speciﬁc factor; in my view, that is at least one step too far. Speciﬁc factors are factors that are characteristic and theoretically central to a given treatment intervention. In contrast, common factors are factors that seem to be present among a majority of treatment interventions, and for this reason they are designated as common or contextual factors. By deﬁnition, common factors are incidental to treatment intervention theory, a fact that does not hinder that a given common factor may be highly important for treatment outcome at the same time as it is not unique. The most well-studied common factor, which for many decades has been a part of the ‘gold standard’ design in the medical ﬁeld, is the placebo, present in the randomized double-blind placebo-controlled effect study. A treatment intervention is considered to be a placebo if its content is incidental, as opposed to characteristic in relation to the therapeutic theory studied (i.e. the sugar pill is incidental in a study of the effects of a given antibiotic). The rationale that underlies this design is that comparison conditions should be impossible to identify as different compared to the experimental condition, i.e. the contextual framing should be common to both. For these reasons I ﬁnd it hard to agree with the suggestion of Miller & Moyers  that it is mistake to talk about common and non-speciﬁc factors.
Declaration of interests None. Keywords Common factors, Dodio bird, future research, speciﬁc factors, treatment outcome, treatment theory. ANDERS BERGMARK Addiction Research Group, University of Stockholm, Sweden. E-mail: [email protected]
2. Babor T., Del Boca F. Treatment Matching in Alcoholism. Cambridge: Cambridge University Press; 2003. 3. Babor T. When prophecy fails: ﬂying saucers, the second coming, and the treatment matching hypothesis. Nord Stud Alcohol Drugs 2008; 25: 294–6. 4. Magill M., Longabaugh R. Efﬁcacy combined with speciﬁed ingredients: a new direction for empirically supported treatment. Addiction 2013; 108: 874–81. 5. Wampold B. The Great Psychotherapy Debate: Model, Methods and Findings. Mahwah, NJ: Lawrence Erlbaum Associates.
SPECIFIC VERSUS RELATIONAL FACTORS IN ADDICTION TREATMENT: THE FOREST AND THE TREES, OR JUST MORE TREES? Miller & Moyers’ paper, ‘The Forest and the Trees: Relational and Speciﬁc Factors in Addiction Treatment’, argues that, currently, a focus on the ‘trees’ (speciﬁc factors, i.e. speciﬁc treatment content) acts to the detriment of examining the ‘forest’ (relational factors, i.e. the larger interpersonal context in which treatment is delivered) . The title chosen by the authors evokes the proverbial challenge of not being able to see the forest for the trees, but the authors suggest some additions to existing methodology that would enable researchers to look at both types of variable. In particular, they make the case for specifying and studying these ‘non-speciﬁc’ or relational factors. However, from an alternative perspective, specifying ‘non-speciﬁc’ factors in the way that Miller & Moyers suggest may actually be creating more trees, instead of enabling a ‘forest’ view. Factors such as therapist interpersonal skills and treatment ﬁdelity, which the authors highlight as modiﬁable relational factors, have, to date, been targeted through a mechanism very similar to that used for ‘speciﬁc content’, e.g. through therapist training [2–4]. In other words, as soon the non-speciﬁc factors are speciﬁed and practitioners trained accordingly, they can be treated in the same way as speciﬁc factors, hence becoming more ‘trees’. Miller & Moyers conclude: ‘as relational inﬂuences on outcome come to be better understood, they can be speciﬁed, measured, implemented in treatment, tested, and incorporated into the training of the next generation of addiction professionals’. Would this require a fundamental shift in research methods? Arguably not. Observational analyses in existing clinical trials could be used to generate hypotheses regarding these relational factors. Once identiﬁed, presumably the next step would be their implementation in treatment, testing via trials, and then incorporation in meta-analyses. However, Miller & Moyers argue that: ‘aggregation of ﬁndings—whether across participants in a study, sites within a multisite trial, or trials within a meta-analysis— masks variability in outcomes that may hold important clues to underlying mechanisms’. Such a statement risks painting a false dichotomy between aggregated ﬁndings Addiction, 110, 414–419
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  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  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  and conditions with which treatments are compared . Magill  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
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