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doi:10.1111/add.12693

The forest and the trees: relational and specific factors in addiction treatment William R. Miller & Theresa B. Moyers Department of Psychology and Center on Alcoholism, Substance Abuse and Addictions (CASAA), The University of New Mexico, Albuquerque, New Mexico, USA

ABSTRACT Aims Increased expectations for the use of evidence-based methods in addiction treatment have fueled a debate regarding the relative importance of ‘specific’ versus ‘common’ factors in treatment outcome. This review explores the influence of these factors on addiction treatment outcome. Methods The authors review and link findings from four decades of research on specific and general factors in addiction treatment outcome research. Findings Although few would argue that what one does in addiction treatment is immaterial, outcome studies tend to find small to no difference when specific treatment methods are compared with each other or with treatment as usual. In contrast, there are usually substantial differences among therapists in client outcomes, and relational factors such as therapist empathy and therapeutic alliance can be significant determinants of addiction treatment outcome. Conclusions In addiction treatment, relational factors such as empathy, which are often described as common, non-specific factors, should not be dismissed as ‘common’ because they vary substantially across providers and it is unclear how common they actually are. Similarly they should not be relegated to ‘non-specific’ status, because such important relational influences can be specified and incorporated into clinical research and training. Keywords treatment.

Common factors, empathy, outcome, specific factors, therapeutic relationship, therapist factors,

Correspondence to: William R. Miller, Department of Psychology and Center on Alcoholism, Substance Abuse and Addictions (CASAA), The University of New Mexico, Albuquerque, New Mexico 87106-1010, USA. E-mail: [email protected] Submitted 1 May 2014; initial review completed 9 June 2014; final version accepted 16 July 2014

INTRODUCTION Imagine having a life-threatening illness and consulting a practitioner who told you: ‘I don’t pay much attention to research. I’ve been treating people for thirty years and I know what works. Participants in those clinical trials aren’t like the patients I see. Besides, I don’t have time to read scientific journals. They don’t have anything to do with practice in the real world.’1 Would you entrust your health to this person? When seeking medical care, people often hope and expect that their providers will keep up with emerging clinical research and offer them the treatment that is known to be most effective in addressing their health problems. Failing to do so can be grounds for malpractice. Several trends in health care indicate that similar expectations for a scientific evidence base are emerging for treatments that are provided for substance use disorders (SUDs) [1,2]. One is an increasing demand for at least the

nominal use of evidence-based treatment in order to be reimbursed for treatment. There is also a strong trend towards integrated care, bundling behavioral health services with health care more generally [3,4]. This is driven not only by economic considerations but also by the fact that most people with SUDs have concomitant medical and/or psychological problems, and integrated care is appropriate. The closer integration of addiction treatment with broader health-care systems will favor continued professionalization (e.g. higher educational standards) of providers, and increased expectation to adhere to scientific standards for evidence-based treatment. The era of ‘anything goes’ in addiction treatment seems to be coming to an end. Is clinical science sufficiently advanced in the addiction field to provide a basis for specific treatment decisions? The usual gold standard for demonstrating treatment efficacy in health care is the randomized clinical trial (RCT). A handful of positive RCTs may suffice for

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This is a verbatim statement to the senior author from the prominent director of an addiction treatment service at a scientific conference in 1999. © 2014 Society for the Study of Addiction

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approval of a new medication as efficacious. For some common forms of cancer, the entire clinical literature consists of but a few RCTs (e.g. [5]). In contrast, for treatments of alcohol use disorders alone more than 400 RCTs have been published [6]. The treatment outcome literature for behavioral and pharmacotherapies in detoxification and treatment of all substance use disorders, including tobacco, would probably encompass more than 1000 controlled trials. Furthermore, it seems abundantly clear that not all treatments are equally effective (e.g. [1,7,8]). Using a methodologically weighted cumulative evidence score, Miller and colleagues found that 90 different treatment methods for alcohol use disorders varied widely in their evidence base, from some with strong support across dozens of RCTs to others with overwhelmingly negative evidence in dozens of trials [6,9]. Nearly half (47%) of all published trials had reported negative findings [6,9], which particularly occurs when bona fide treatments are compared with each other [10,11]. Despite equivocal findings for brand-name treatments, specific behavioral treatment components and practitioner competencies have been linked to more favorable outcomes in both alcohol [12,13] and smoking cessation research [14,15].

LIMITATIONS OF SPECIFIC TREATMENT EFFECTS Part of the debate surrounding evidence-based treatment (EBT) is whether there are specific treatment methods that clinical science has shown to exert unique or differential benefit. ‘Specific’ refers here to particular prescribed treatment techniques that are believed to be responsible for benefit, and are not characteristic of most other treatment methods. Specific or technical treatment components are generally differentiated from elements alleged to be ‘general’ or ‘common’ factors (found across many forms and providers of treatment), and thus are ‘non-specific’ (not limited) to a particular treatment method. Such factors are often described as inhering in the therapeutic relationship [16,17]. Rather than perpetuating the polarizing debate as to whether or not clinical science is relevant in treatment [18], it is time to recognize and address the legitimate limitations and concerns raised by critics of EBTs. We hope that this paper offers a step in that direction. Small effect sizes A minimum consideration in judging a specific treatment to be evidence-based is whether it yields outcomes better than no treatment. In RCTs of medications, the typical comparison is with double-blind placebo, for which there is no logical equivalent in psychotherapy research. Fur© 2014 Society for the Study of Addiction

thermore, truly untreated control groups are rare, in part because of ethical and pragmatic concerns regarding denial of treatment. More common are RCTs to determine whether a specific treatment is more effective than brief or minimal intervention. Treatments for SUDs clearly vary in the amount and strength of evidence for their efficacy relative to more minimal intervention [1,6–9,19]. A different question is whether a specific treatment is more effective than other bona fide treatments; for example, is a new medication more effective than other medications already on the market for the same purpose? A meta-analysis of studies directly comparing treatments for alcohol problems with each other [11] found that such comparisons centered around an effect size of zero. In other words, different bona fide treatments yielded similar outcomes on average. There is variability in findings, of course, which Imel et al. [11] argued can be accounted for in part by study authors’ allegiance to their particular approaches. The absence of between-treatment differences appears to be particularly characteristic of findings from multisite trials, which are powered to detect even relatively small effects. In Project MATCH (n = 1726), for example, nearly all the principal investigators had a clear public allegiance to cognitive–behavior therapy (CBT), which was compared with two other bona fide treatments: 12-Step and motivational enhancement therapies [20]. Although no formal prediction was made regarding main effects, most of the hypothesized matching effects predicted that CBT would be superior to either or both of the comparison treatments [21]. To the research team’s surprise, all three treatments yielded virtually identical outcomes across 3 years of follow-up on the primary dependent measures [22,23], and few of the matching hypotheses were confirmed [21]. A subsequent multi-site trial, the UK Alcohol Treatment Trial [24], contrasted a shortened motivational therapy with an enhanced cognitive–behavioral treatment involving the client’s social network, again finding equivalent outcomes [25,26]. Multi-site trials are often used to test treatments that have already shown positive effects in single-site RCTs. Such trials can be hybrids of efficacy and effectiveness research. In the National Institute on Drug Abuse Clinical Trials Network, for example, multi-site RCTs are conducted in ongoing community programs, with treatments provided by regular clinical staff [27,28]. In this way, treatments with ‘proven efficacy’ in separate trials are submitted to simultaneous testing across multiple clinical sites. An impressive array of multi-site trials have been conducted within this Clinical Trials Network, typically comparing various treatment methods with treatmentAddiction

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as-usual (TAU). All treatments are delivered by regular front-line clinical staff members who are usually assigned at random to deliver TAU or the investigational treatment. The first 10 years of CTN trials focused on treatment methods that had already shown efficacy in prior clinical trials [29]. Nevertheless, the modal finding of these trials was small or no difference between the EBT and TAU. For example, four multi-site trials of treatments based on motivational interviewing, which has been supported in multiple trials and meta-analyses [10,30–32], failed to show any statistically significant difference from TAU [33–36]. This finding of modest specific effects in multi-site trials is not limited to behavioral therapies. A multi-site trial of disulfiram versus placebo yielded modest effects at best [37], contributing perhaps to a marked decline in its use. The multi-site Combined Medications and Behavioral Interventions (COMBINE) trial [38] found a relatively small effect size for naltrexone in the absence of behavioral psychotherapy (and for behavior therapy in the absence of naltrexone) and no effect at all for acamprosate, all of which had received reasonable support in prior trials [6,9].

Clinically meaningful differences A difference that is statistically significant may nevertheless seem of doubtful clinical importance. In the COMBINE trial, for example, the interaction of naltrexone and behavioral treatment was significant (P < 0.009), but the actual mean difference was 25% (with neither treatment) versus 21% drinking days (with either or both treatments). Similarly, a significant (P < 0.02) main effect of naltrexone reduced the percentage of patients with at least one heavy drinking day during treatment from 71 to 68%. Perceived relative advantage is one of the key conditions that favor the adoption of any new technology [39]. How large must a between-group difference be for clinicians to regard it as clinically meaningful? Miller & Manuel [40] surveyed addiction treatment professionals on this topic. Practitioners’ answers were the same when asked how large a difference was clinically meaningful, and how big the difference should be for them to be interested in learning a new treatment. In other words, if the improvement in outcome from a new treatment was large enough to be judged clinically meaningful, then they were interested in learning it. On outcomes expressed as a percentage of clients doing well (e.g. percentage abstinent, employed, arrested), clinicians judged a separation of 10 percentage points to be a meaningful difference. On continuous measures such as the percentage of days with substance use, their responses clustered around doubling or halving. The authors suggested that clinical trials could © 2014 Society for the Study of Addiction

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be powered to detect a between-treatment difference that would be regarded by clinicians to be clinically meaningful, rather than merely P < 0.05 statistical significance. The impact of ‘treatment as usual’ Why are between-group outcome differences so small when a specific EBT is compared with another bona fide treatment or even with uncontrolled treatment-as-usual (TAU)? One reason is that TAU appears to be tough competition. The average outcomes of treatment for alcohol use disorders are quite good, and do not seem to have changed much in 40 years [41]. The Rand Corporation’s evaluation of uncontrolled treatment at public treatment facilities in the 1970s [42,43] yielded aggregate 1-year outcomes not substantially different from those of outpatients participating in Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity) [20] or the COMBINE study [38]. Across seven large trials of alcohol treatment enrolling 8389 clients, abstinent days averaged 81% and one-third of clients remained asymptomatic through a year of follow-up. Often missed is the substantial improvement of the remaining two-thirds, whose drinking on average was reduced by 87%, with 75% days abstinent [41]. In a direct comparison [44], the outcomes for Project MATCH participants did not differ significantly from those for clients receiving uncontrolled TAU in the same period at the same treatment program. TAU may be a difficult standard to beat, and given the average outcomes described above, detection of treatment effects in a clinical trial may be limited by a ceiling effect, at least in addressing alcohol use disorders. If TAU if often as good as specific treatments, does this mean that anything goes and it makes no difference what and how treatment is provided? We think not. There is substantial variability among providers’ outcomes both in uncontrolled TAU [45–47] and in well-controlled manual-guided therapies [48,49]. Such variance can impair signal detection of specific treatment effects, particularly if they are relatively small. Furthermore, such variability in outcomes may result from unmeasured but important treatment factors. This is an implicit argument in the defense of ‘common’ factors—that there are actually large effects of unspecified components such as therapeutic relationship. If inadequately measured aspects of treatment do in fact have a large impact on outcome, clinical science ought to specify and measure them, gathering evidence on how they effect change. In sum, there are legitimate reasons to question the superiority of specific EBTs. Even brief treatments are found consistently to be better than no treatment, but when bona fide treatments are compared with each other the difference in outcomes is typically small and variable. The relative advantage of one treatment over another is Addiction

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often so small as to be of little clinical interest [11]. Furthermore, any attempt to dichotomize treatments as ‘evidence-based’ versus not depends on placing an arbitrary cut-point on what is a continuum of scientific evidence [50]. The bar for an EBT in the US National Registry of Evidence-Based Programs and Practices (http://www.nrepp.samhsa.gov/) has been set so low (one positive trial) that more than 330 different interventions for SUDs are listed as evidence-based. WHY ARE SPECIFIC TREATMENT EFFECTS SO SMALL? Sometimes the argument stops here—that there are no specific effective treatments and no science-based standards for preferring one approach over another [11]— although few would advocate truly abandoning scientific evidence on behalf of ‘anything goes’ in health care. Nevertheless, something important does seem to be missing in understanding what matters in treatment. Critiques of evidence-based approaches have highlighted excessive focus on specific techniques and brand-name therapies to the neglect of the substantial impact of therapeutic relationship, client and contextual factors [11,51,52]. We concur that there are important factors influencing treatment outcome beyond the specific contents of therapy manuals or medication capsules. Clinical research can be designed to understand the importance of such factors rather than ignoring them as background noise. Site × treatment interactions An oft-ignored aspect of multi-site trials is the presence of site × treatment interactions, which appear to be the norm rather than the exception, at least in addiction treatment studies. In other words, the specific therapies ‘work’ at some sites but not others in exactly the same study using standardized and supervised treatment procedures. In clinical trial reports, these differences tend to receive a passing mention at most, and emphasis is given to the average outcomes of treatments across all sites.

Farmington, CT 90 80 70 60 50 40 30 CBT MET TSF 20 Intake 3 mo 6 mo 9 mo 12 mo 15 mo

In Project MATCH, for example, the three therapies were compared at nine different locations. Averaging across all sites there were no significant differences among the three treatments on the a priori outcome measures [22,23], but at individual sites the picture was different. At two sites, 12-Step facilitation was significantly more effective than the other two treatments. There were no significant differences at the other seven sites, with variation in the relative outcomes of the three treatments. Figure 1 illustrates the relative outcomes of treatments at three different out-patient sites. At the West Haven site investigator allegiance would have favored CBT, which turned out to be least effective, whereas at nearby Farmington CBT showed an advantage. Investigator allegiance at Milwaukee would have favored MET, which again was found least effective. The 12-Step facilitation therapy occupied different positions at each of these sites. If these were regarded as nine independent replications, they would reflect marked variability in outcomes. The research group was unable to account for these differences by client or investigator factors. Similarly, in a multi-site trial within the NIDA Clinical Trials Network, the overall main effect of motivational enhancement therapy was not significant, masking significant differences in efficacy across sites [53]. Aggregation of findings—whether across participants in a study, sites within a multi-site trial or trials within a metaanalysis—masks variability in outcomes that may hold important clues to underlying mechanisms. What differences in practice or context may exert important effects on client outcomes? If exactly the same treatments exert an effect at some sites and not others, even when providers are all trained together, use a standard manual and are supervised and monitored by audio recording, then something else may be affecting outcome besides the specific treatment procedures being used. Sites may, for example, exert varying research participant effects [54,55] through differences in procedures for recruiting, enrolling and instructing participants. Could such ‘non-specific’ factors be specified, measured and evaluated in clinical research?

West Haven, CT 100 90 80 70 60 50 40 30 CBT MET TSF 20 Intake 3 mo 6 mo 9 mo 12 mo 15 mo

Milwaukee, WI 90 80 70 60 50 40 CBT MET TSF 30 20 Intake 3 mo 6 mo 9 mo 12 mo 15 mo

Figure 1 Relative treatment outcomes (percentage of days abstinent) at three Project MATCH sites. CBT = Cognitive Behavioral Coping Skills Therapy; MET = Motivational Enhancement Therapy; TSF = Twelve Step Facilitation Therapy © 2014 Society for the Study of Addiction

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Therapist differences in addiction treatment Although it has received scant attention in the addictions treatment literature, there is a broader body of research indicating that one of the best indicators of clients’ retention and outcome is the particular counselor to whom they happen to be assigned [56,57]. In one early study, problem drinkers were assigned randomly to one of nine counselors who were trained and supervised together, delivering a manual-guided behavior therapy. In a clinical significance analysis using outcome classifications, counselors’ rates of positive behavioral outcomes in their caseload varied from 25 to 100% [58]. Valle [45] similarly found significant differences in the relapse rates of clients assigned randomly to counselors in an alcohol treatment program. In another study, when two counselors resigned from an inner-city drug abuse treatment program, their case-loads were allocated randomly to four remaining counselors [47]. Clients of three counselors showed substantial improvement relative to prior functioning on a variety of outcome measures, but one counselor’s case-load showed no improvement or marked deterioration on the same measures. Psychotherapists delivering standardized treatment in clinical trials regularly show substantial differences in the outcomes of clients they treat [46,59,60]. Large therapist variation was observed in Project MATCH, accounted for in some cases by a few therapists with outstandingly poor outcomes [48]. In the COMBINE study, the particular therapist the client was assigned accounted for nearly 10% of the variance in drinking outcomes [61].

Therapist characteristics related to better client outcomes If therapists are so important in accounting for treatment effects, what is it exactly about them or what they do that makes such a difference? Decades of research have found that moderator variables such as therapist age, ethnicity, gender, level of experience and type of education show little consistent relationship to client outcomes [62]. Other variables such as therapist beliefs and interpersonal skills have been investigated only sporadically, despite their promising relationship to outcomes— particularly in the treatment of addictions (e.g. [13]). Nevertheless, there are some particular therapist characteristics and behaviors that appear to be associated with better substance use outcomes.

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would be helpful information with the treatment staff, identifying clients who, based on their testing, had particularly high alcoholism recovery potential (HARP). Their predictions were borne out over time. Although HARPs had not differed from other clients on prior treatment history or severity of alcohol problems at intake, at discharge their counselors rated them as having been more motivated, cooperative, punctual in attending appointments, neater in appearance, trying harder and having a better prognosis. Indeed, over the course of a year of follow-up, HARPs were more likely to be abstinent and employed and to have longer spans of abstinence and fewer slips. The researchers’ secret, however, was that HARPs had been selected at random, not on the basis of any assessment results. The only difference between HARPs and other clients was the counselors’ induced expectations of their potential. Allegiance Therapist allegiance to a particular treatment approach also matters. In a series of clinical trials, Azrin and colleagues contrasted his community reinforcement approach (CRA) with disease-model TAU (both delivered by the same behaviorally trained counselors) and found substantial superiority of CRA in client outcomes [64– 66]. In a subsequent evaluation of CRA [67], the TAU was delivered by addiction counselors who were committed to a disease model of alcoholism and highly experienced in providing such treatment. In this study the observed advantage for CRA was small, albeit statistically significant at 6 months, and had reversed in direction by 16 months (See Fig. 2; [68]). Allegiance may affect outcomes in trials where therapists are assigned randomly to the treatments they provide, a condition that would not occur in normal treatment delivery systems [24,28]. Allegiance is itself an abstraction, and it could be useful to examine what therapists who are allied to a particular treatment approach do differently from therapists without such allegiance to the approach.

Expectancy

100 90 80 70 60 50 40 30 20 10 0

In an early study [63], three alcohol treatment programs allowed researchers to study personality traits of clients in relation to outcome. The investigators completed their assessments, and in appreciation shared what they hoped

Figure 2 Percentage of days abstinent at follow-up in four community reinforcement approach (CRA) studies

© 2014 Society for the Study of Addiction

Traditional CRA

Azrin 1973

Azrin 1976

Azrin Miller Miller 1982 2001 2001 (6 mo) (16 mo)

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Interpersonal skills Therapist interpersonal skills are also associated with better treatment outcomes [69]. For addiction treatment providers in particular, therapist empathy predicts increased retention and less drinking across a wide spectrum of clients and settings [13]. In the COMBINE study, therapist empathy expressed within sessions predicted less drinking during treatment [12]. This correlational finding is paralleled in randomized assignment studies, where therapist empathy [58,70] and client-centered interpersonal skills [45] predicted substantial variance in client drinking outcomes during 1–3 years of follow-up. Fidelity Therapist fidelity in delivering complex behavioral interventions is another potentially important factor influencing treatment outcome [71–73]. If measured at all, fidelity is often assessed via relatively crude Likert scales with a small subsample of treatment sessions. As specific mechanisms of treatment efficacy are clarified, the extent to which they have been delivered should predict client outcomes. Process research on motivational interviewing supports the linkage of prescribed therapist behaviors with theoretically relevant aspects of in-session client speech which, in turn, predict client outcomes [74–77]. Although therapist characteristics tend to account for a relatively small percentage of variance in client outcomes (usually between 3–7%), they often exceed the variance accounted for by specific treatment factors such as the type of treatment employed (e.g. [12,48]) Furthermore, well-designed and powered studies show that it is typically between-therapist variability that accounts for more variance in outcomes than within-therapist differences [78–80]. If for no other reason, therapist relational factors are a sensible focus because they are potentially malleable, unlike static variables such as ethnicity, sex or professional degree.

Client factors in treatment outcome Another reason why treatment effects may be relatively small in clinical trials is the comparatively larger amount of variance accounted for by characteristics and behaviors of the client, which are often measured inadequately if at all. As in the wider psychotherapy literature [81], client characteristics often reflect large effect sizes in addiction treatments, especially for static baseline characteristics such as problem severity, level of impairment, socio-economic status and treatment history. There is a particularly robust relationship between higher severity and less favorable outcomes [82–84]. Less attention has been paid to more dynamic client variables in addictions treatment, despite the fact that © 2014 Society for the Study of Addiction

such characteristics are potentially responsive to intervention. A wide variety of client characteristics have been associated with improved outcomes, including initial optimism about treatment effectiveness [85,86], motivation [87,88], self-efficacy [89–91] and hope [92]. A strong example of a relevant client variable is motivation, which can be defined as the client’s personal considerations, commitments, reasons and intentions to perform certain behaviors [93]. Two decades of research indicate that readiness for change is related consistently to increased help-seeking, treatment adherence and treatment completion. Clients with higher levels of motivation also show better treatment outcomes [94]. Average effect sizes for client motivation or readiness to change are d = 0.46 [95] much larger than typical treatment effects in addictions. In Project MATCH, client motivation at baseline was a robust predictor of drinking outcomes at both 1- and 3-month follow-up points. Further, across all patient characteristics measured in MATCH, client motivation was one of the strongest predictors of both frequency and intensity of drinking outcomes [93]. Although often measured as a static baseline variable, motivation is clearly fluid and amenable to therapeutic interventions [96]. When measured as a dynamic variable, client motivation may be an even stronger predictor of outcome than when measured once at baseline. In one randomized trial, client commitment at the beginning of treatment did not predict drug use outcomes, whereas commitment language strength at the end of a motivational interviewing session did [97]. When motivation is measured from client speech during treatment sessions, substance use outcomes are associated commonly with language indicating a desire, ability or intent to change [76,97–100]. Clients who say that they are ready, willing and able to change in meaningful interactions with their therapists are more likely to do so. In addition to the importance that clients attach to change, their confidence in doing so is a good predictor of outcome. In health behavior models, importance and confidence flow together to predict change [101,102]. Clients who believe that they can do what is needed to make and sustain a change [82,103] have a distinct advantage as they begin treatment. A common belief in addiction treatment has been that client resistance is associated with poorer therapeutic alliance and treatment outcomes [104,105], and that such resistance reflects a static characteristic of the client that is an inherent aspect of addictions [106]; yet resistance is behavior that occurs in an interpersonal context and is highly responsive to therapist style [107,108]. Client characteristics may moderate the relationship between therapist style and resistance. Karno & Longabaugh [109] found that therapist directiveness Addiction

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predicted poorer outcomes (more drinking) for high- but not low-reactance clients. Here, then, is another important client factor that can be influenced by specifiable aspects of treatment. Beyond personal characteristics of clients, contextual variables can also influence response to particular treatments. For example, the extent of social support for drinking affects one’s models for and pressures towards sobriety versus continued alcohol use. In the MATCH trial, clients with low support for sobriety (whose social networks favored continued drinking) benefitted differentially from the 12-Step facilitation approach that linked them with Alcoholics Anonymous (AA) as a social support network favoring abstinence [110]. Interactions between clients and therapists Therapists and clients both bring proclivities for expressing particular characteristics, which are then influenced by the unfolding process of the treatment. Even when large differences are observed between therapists, variability among clients for the same therapist is not uncommon [79,111]. This is true for both the overall impact of the counselor and for particular characteristics such as empathy and directiveness [12]. Similarly, client characteristics such as self-efficacy and motivation may have complex interactions across the course of a treatment that are associated with outcomes only when they are measured in relationship to each other [112]. A common method for measuring the quality of therapist–client interaction is the Working Alliance Inventory (WAI [113]), assessing the strength of the bond between the client and therapist, their consensus on the goals for treatment and their agreement about the tasks to be carried out. A meta-analysis of 201 studies [114] found an overall relationship between alliance and outcome of r = 0.275, a modest but highly reliable effect that accounts for about 7.5% of the variance in treatment outcomes. For addiction treatments specifically, helping alliance is associated with better treatment engagement, retention and outcomes [105,115], particularly when it is measured closer to the end of treatment [80]. However, the relationship between alliance and outcome is complex: an alliance that is too strong can diminish client progress. In a well-designed study, CritsCristoph et al. [80] found that both high and low levels of alliance were associated with more modest gains in alcohol treatment than scores in the middle range. THE FOREST AND THE TREES: LEARNING MORE FROM CLINICAL RESEARCH It seems fairly clear at this point that treatment outcomes are not likely to be improved greatly by searching for © 2014 Society for the Study of Addiction

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better specific content to prescribe in therapist manuals. At the same time, it would be quite cynical (and inconsistent with research reviewed above) to assert that it makes no difference what a therapist does. Some treatment approaches are found somewhat consistently to be ineffective or even harmful (e.g. [6,51,116]). As a contrast example, the community reinforcement approach has shown differential benefit in all 12 clinical trials to date. What has so often been overlooked, however, is the large impact in addiction treatment of what are sometimes referred to as ‘common’ or ‘non-specific’ factors, both of which are misnomers. To refer to an important component of treatment as ‘non-specific’ simply means that we have not done our homework. If indeed such relational factors exert a large impact on treatment outcome, then we owe it to our clients to specify them: to understand how they improve outcomes, develop reliable measures, test and incorporate them into therapist training and quality assurance. Relational factors that have a large effect on outcome should not be difficult to detect. Similarly, it is unclear just how ‘common’ such factors are in practice. Accurate empathy is a prime example of a specifiable evidence-based relational factor that is both learnable and measurable, the absence of which is associated with poor outcomes, at least in addiction treatment [13]. Levels of counselor empathic skill vary widely and predict 1- and 2-year client drinking outcomes [13,45,58,70]. The separation of specific from relational effects is a false dichotomy. When a specific treatment is delivered in the context of an interpersonal relationship, the two are inseparable. To ask which is more important is like asking whether the quality of a meal is due to the chef or the raw ingredients, or whether winning a race is due to the horse or the jockey. It is possible to learn much more from clinical trials than who won the horse race. This requires an additional investment of time and resources beyond end-point analyses, but the increased cost is relatively minor compared to the expense involved in hiring and training, recruiting and retaining the clinical sample, delivering and documenting treatment and collecting outcome data over the span of follow-up. We offer here some recommendations for studying the forest as well as the trees in clinical research, and in Table 1 suggest promising candidate variables for studying therapist, client and relational effects.

Study therapist effects Overall outcome differences among providers are normative in addiction treatment research, often with larger impact than variations in treatment methods [12,48]. Addiction

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Table 1 Some promising candidate variables to measure in clinical research. Therapist effects

Client effects

Relational effects

Basic processes

Empathy, interpersonal functioning Warmth, unconditional positive regard Reflection : question ratio Motivational interviewing (MI)-inconsistent responses (e.g. confront, warn, advise without permission) Change talk : sustain talk ratio (frequency) Change talk strength Self-efficacy, confidence Readiness, importance Experiencing Working alliance Client feedback Discourse analysis Talk time Sequential analyses of therapist and client responses Presence of theory-predicted key mechanisms of efficacy

Whenever possible, include a large enough range of providers to permit meaningful analysis of their contribution to outcomes. When feasible, assign cases randomly to counselors in order to avoid bias in case assignment. Heterogeneity of providers can be an asset from a forest perspective. In actual practice, treatments are delivered by a wide range of practitioners. Within a large organization, studies of differences in case-load outcomes could yield useful clues as to the characteristics and practices of more versus less effective therapists. Study client effects Clients also bring substantial variance to treatment. Studying static client characteristics as predictor or matching variables has been minimally fruitful in improving addiction treatment outcome [20]. Dynamic client factors such as motivation and self-efficacy predict outcome, with the added complexity that they can be influenced by treatment. Self-efficacy, for example, often increases during treatment and such gains are associated with better outcomes. This increase in confidence does not appear to be related to specific treatments. Despite theoretical predictions that increased confidence should result from interventions focused on acquiring coping skills and completing specific homework assignments, it does not [117,118]. Instead, it appears that increasing client confidence may be a result of other treatment influences, and that once galvanized it offers a benefit in making and sustaining change. The empirical literature on self-efficacy in substance use outcomes is most consistent with the hypothesis that clients use a wide variety of © 2014 Society for the Study of Addiction

different mechanisms to make changes they deem important, some of which they access through formal treatments, and some of which they either find on their own or opportunistically ‘scavenge’ from complex, theoretically driven treatments containing many possible active elements in addition to those specified [119]. Study relational factors and treatment processes Outcome versus process research is also a false dichotomy. Documenting the processes of treatment delivery allows examination of treatment factors other than prescribed content [120]. Audio recording of interventions is fairly inexpensive and unobtrusive, and allows for subsequent coding and analyses of what actually transpired in treatment sessions. Observed practice is normative in health care, but has been less common in addiction treatment, particularly outside clinical trials [121]. Beyond recording, the resources required for process coding are still modest relative to the typical costs of conducting a clinical trial, and could be supported from a separate funding source. What is it about practice, the delivery of a treatment within an interpersonal context, that most influences outcome? Such research can link specific (including relational) treatment factors to in-session client responses that in turn presage beneficial change (e.g. [76]). With careful planning and investment of financial resources, process investigations of this kind could be integrated but separate projects adjacent to large clinical trials of substance use treatments, allowing a larger return on investment. Research on treatment processes or ‘mechanisms of action’ can focus not only on specific treatment content but also on broader relational factors. Studies are needed to specify ‘non-specifics’ as measurable outcome-relevant processes that occur in treatment. Such research can be performed within structured clinical trials, but is also possible within unstructured treatment-as-usual where normal variability occurs. Theory-based hypotheses about process–outcome links can be tested first in correlational post-hoc fashion in search of promising candidate factors. Reliable measurement of process constructs is itself a contribution pioneered by Carl Rogers and his students [122–125]. To what extent do these processes vary between and within therapists, and how are they related to client outcome? Expect the unexpected Spend time getting to know your data. A posteriori findings are acceptable when identified as such, and can be reported not only as probability levels but also as effect sizes. Such findings can alert other clinician investigators to possible effects, and replication across studies is more informative than the findings of any single study. Were Addiction

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there sizeable differences on measures other than the specified a priori outcome measures, or at other times than the primary end-point? Might there be a main effect of consecutive subject number (i.e. do participants treated earlier have different outcomes from those treated later, perhaps because of therapist experience effects or fluctuations in recruitment)? Replication of such findings in other research is important to determine whether they are ‘false positive’ results. Report site × treatment interactions In multi-site treatment trials, always clearly report site × treatment interactions that reflect differences in treatment effects across sites. Although often regarded as nuisance variance to be reduced or ignored, such interactions can be informative [53]. As illustrated earlier, multi-site trials can also be regarded as multiple replications. Variability of effect across performance sites can be of clinical interest: how many sites that implemented the treatment (usually under ideal conditions) actually witnessed a benefit to clients? Why does the treatment seem to ‘work’ at some sites and not others? Can it be explained, for example, by differences in treatment fidelity across sites?

DISCUSSION Clinical research on addiction treatment has been far too focused on the trees (specific treatment content) while often ignoring the larger interpersonal and programmatic context within which treatment is delivered (the forest). The two are not readily separable, and there is solid science to warrant attention to both. Few would advocate divorcing practice from clinical science. All treatments are not created equal. Some approaches have been strongly supported in multiple trials; others are generally found to yield no change or worse (e.g. [6,51,116]). The content of treatment is not irrelevant, and as addiction treatment becomes integrated more closely with mainstream health care, scientific standards for practice are likely to increase. There are also legitimate critiques of the limitations of studying specific treatment content. Specific treatments are often found to yield outcomes no different on average to those from uncontrolled treatment as usual, and comparisons of bona fide treatment approaches typically show small (if any) differences that fade over time. The emperor of evidence-based treatment is at best scantily clad. We believe it is misguided, however, to therefore reject the findings of clinical research as irrelevant to practice—a position that most would find abhorrent in their own health care. Behavioral treatments (and even pharmacotherapies) are always delivered in a relational context, and there © 2014 Society for the Study of Addiction

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appear to be general factors (including therapist, client and process variables) that contribute more to outcome variance than is typically accounted for by specific content. If such factors are ‘non-specific’ it is only because they have been insufficiently understood, operationalized, measured, implemented, tested and taught. Relational factors are not beyond the reach of clinical science. It is not even clear whether standardizing of treatment is a good idea. Adding process research to studies of treatment-as-usual could help to fill a current knowledge gap regarding what constitutes front-line addiction treatment. Highly specific and standardized treatments do not necessarily yield better outcomes than uncontrolled treatment-as-usual [44] and providing counselors with feedback about clients’ progress can improve treatment outcomes, even if counselors are not instructed about how and whether to change what they are doing. The use of a therapist manual may impair the effectiveness of some treatments [10]. To be sure, it is difficult to advance clinical science without knowing what treatment is being delivered, but attempts to standardize providers may impede rather than advance understanding of the efficacy of treatment. In any event, it makes little sense to continue studying specific treatment content as if it could be separated from the relational context within which it is delivered. Both can and should be studied, and the field has hit the limitations of what can be discovered by tweaking the content of therapist manuals. Relational factors are also evidence-based, although given too little attention in clinical research. Knowledge on what matters in therapeutic relationships is still at a relatively early stage, particularly in addiction treatment. With some forethought, clinical trials can yield far more new knowledge than the relative outcomes of different treatment content, and with a relatively modest increase in effort and cost. 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. Declaration of interests None. References 1. Berglund M., Thelander S., Jonsson E., editors. Treating Alcohol and Drug Abuse: An Evidence-Based Review. Weinheim, Germany: Wiley-VCH Verlag; 2003. 2. Mee-Lee D., McLellan A. T., Miller S. D. What works in substance abuse and dependence treatment. In: Duncan B. L., Miller S. D., Wampold B. E., Hubble M. A., editors. The Heart and Soul of Change, 2nd edn. Washington, DC: American Psychological Association; 2011, pp. 393–417. Addiction

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