Shapes of Early Change in Psychotherapy Under Routine Outpatient Conditions
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1 Journal of Consulting and Clinical Psychology Copyright 2007 by the American Psychological Association 2007, Vol. 75, No. 6, X/07/$12.00 DOI: / X Shapes of Early Change in Psychotherapy Under Routine Outpatient Conditions Niklaus Stulz University of Berne and University of Trier Chris Leach and Mike Lucock South West Yorkshire Mental Health NHS Trust and University of Huddersfield Wolfgang Lutz University of Trier Michael Barkham Centre for Psychological Services Research, University of Sheffield Although improvement of clients state is a central concern for psychotherapy, relatively little is known about how change in outcome variables unfolds during psychotherapy. Client progress may follow highly variable temporal courses, and this variation in treatment courses may have important clinical implications. By analyzing treatment progress using growth mixture modeling up to the 6th session in a sample of 192 outpatients treated under routine clinic conditions, the authors identified 5 client groups based on similar progress on the short form versions of the Clinical Outcomes in Routine Evaluation Outcome Measure. The shapes of early change typical for these client groups were characterized by (a) high initial impairment, (b) low initial impairment, (c) early improvement, (d) medium impairment with continuous treatment progress, or (e) medium impairment with discontinuous treatment progress. Moreover, the shapes of early change were associated with different treatment outcomes and durations, and several intake variables (depression, anxiety, and age) enabled prediction of the shape of early change and/or prediction of individual treatment progress within client groups with similar shapes of change. Keywords: client-focused research, repeated measurement, shapes of early change, discontinuity in treatment courses, growth mixture modeling The exploration and modeling of change are a central concern of psychotherapy research (Barkham, Stiles, & Shapiro, 1993; Kopta, Howard, Lowry, & Beutler, 1994; Stiles et al., 2004). While studies to evaluate psychotherapy outcome often rely on pretreatment to posttreatment comparisons and implicitly assume linear and steady change, there are two main research traditions that are both concerned with the question of how change unfolds during treatment. The first, client-focused research, is based on research Niklaus Stulz, Department of Psychology, University of Berne, Berne, Switzerland, and Department of Psychology, University of Trier, Trier, Germany; Wolfgang Lutz, Department of Psychology, University of Trier; Chris Leach and Mike Lucock, Psychological Services, South West Yorkshire Mental Health NHS Trust, England, and School of Human and Health Sciences, University of Huddersfield, Huddersfield, England; Michael Barkham, Centre for Psychological Services Research, University of Sheffield, Sheffield, England. This work was partially supported by Swiss National Science Foundation Grant PP to Wolfgang Lutz. Collection of the original data was partially supported by Northern & Yorkshire Grant NYRO ACJ April 97 from the regional office of the United Kingdom s National Health Service (NHS). Chris Leach and Mike Lucock were supported by the NHS Priorities and Needs Research & Development Levy awarded to the South West Yorkshire Mental Health NHS Trust. Michael Barkham was supported by the NHS Priorities and Needs Research & Development Levy awarded to Leeds Mental Health Teaching NHS Trust. Correspondence concerning this article should be addressed to Niklaus Stulz, Department of Psychology, University of Berne, Gesellschaftsstrasse 49, CH-3012, Berne, Switzerland. niklaus.stulz@psy.unibe.ch on continuous dose effect relations in psychotherapy (Barkham et al., 2006; Barkham, Rees, et al., 1996; Howard, Moras, Brill, Martinovich, & Lutz, 1996) and deals with the early identification of clients at risk for treatment failure, feedback to therapists, and outcome management (Lambert, 2007; Leach & Lutz, in press). The second, process and outcome research, focuses on relations between psychotherapeutic processes and outcomes over the course of treatment (e.g., Elliott, 1989; Greenberg, 1986; Orlinsky, Ronnestad, & Willutzki, 2004; Rice & Greenberg, 1984). This approach comprises a rich tradition of intensive process research investigating generic change processes and specific therapeutic tasks (for a summary, see Elliott, Greenberg, & Lietaer, 2004). One branch of this latter research tradition is concerned with major between-sessions changes (i.e., with discontinuity in treatment progress), their causes, and their relevance for treatment outcomes (e.g., Stiles et al., 2003). Even though we outline client-focused research and process and outcome research on discontinuous treatment progress as distinct traditions, they are not mutually exclusive and, in fact, represent complementary contributions to a better understanding of change processes during therapy, their causes, and their consequences. Client-focused research is concerned with monitoring individual treatment progress not only by considering pre- and posttreatment assessments but also by taking into account intermediate ideally, session by session assessments of clients states during treatment (Howard et al., 1996; Lambert, 2007; Lutz, 2002). For instance, Lutz, Martinovich, and Howard (1999) applied random coefficient regression models (e.g., Bryk & Raudenbush, 1992) to a sample of 864
2 SHAPES OF EARLY CHANGE 865 outpatients with repeated assessments during treatment and were able to identify a set of client intake characteristics (e.g., treatment expectations) that allowed them to predict individual treatment progress. These prediction models were then successfully used to provide feedback to therapists based on comparisons between the actual and the expected treatment response and then to develop decision rules to support clinical practice (Lambert et al., 2001, 2003; Leach & Lutz, in press; Lutz, Lambert, et al., 2006). However, while such random coefficient regression models respect individual differences in change over time, they are still built on the assumption that one specific shape of change can be employed for all clients in the data set. On the one hand, this assumption is useful in estimating a general trend over time and has utility in terms of generating feedback to support clinical decisions. On the other hand, it has also been noted that individual client progress may follow highly variable temporal courses and that this variation may be clinically important (e.g., Barkham et al., 1993, 2006; Krause, Howard, & Lutz, 1998). Furthermore, there may also be client intake characteristics that are not global that is, predictors that are predictive for the individual treatment progress only for specific subsets of clients (Krause et al., 1998). Therefore, in random coefficient regression models, the validity of the use of growth curve prediction weights for any particular client may depend on the extent to which the study sample is representative of the population of which that client is a member. One way to account for client subpopulations is to use an extended growth curve methodology that employs nearest neighbor (NN) techniques (Lutz et al., 2005; Lutz, Saunders, et al., 2006). This NN approach is derived from alpine avalanche occurrence research (e.g., Brabec & Meister, 2001), and it identifies those previously treated clients who most closely match the target client (hence, nearest neighbors) on intake variables. By using this homogeneous subgroup to generate predictions of treatment progress for the target client, the NN approach mirrors the way clinicians often talk about how their work with a current client is informed by their clinical experience of similar past clients. The NN approach was found to be superior to the conventional expected treatment response models described above in predicting the rate of change (Lutz, Lambert, et al., 2006; Lutz et al., 2005). However, while in this NN approach client subgroups are identified on the basis of similar intake characteristics, yet another approach is to identify client subpopulations on the basis of similar shapes of change over time and to look for specific predictors of individual treatment progress within these client subgroups. Process and outcome research on discontinuous treatment progress the other research tradition tries to use information about large between-sessions changes to predict final treatment outcomes of ongoing therapies. Thompson, Thompson, and Gallagher-Thompson (1995), for example, found the overall level of discontinuity, as well as positive and negative major betweensessions changes in self-reported depressive mood (so-called improvement shifts and worsening shifts), to be associated with higher recovery rates by the end of time-limited therapy among older clients with major depressive disorder. Similarly, Tang and DeRubeis (1999) reported better treatment outcomes for depressed clients who experienced large and enduring symptom reductions in a single between-sessions interval (so-called sudden gains) a finding replicated by Stiles et al. (2003) in a sample of clients with mixed diagnoses and treated under routine clinic conditions. The common denominator of these two research traditions is that they share a more detailed exploration of change processes during treatment. This kind of research also raises the question of whether there are different subgroups of clients that follow characteristic shapes of change and that have specific predictors for individual treatment progress. To address this general question, we identified a new method that enables identification of distinct groups of individuals through the empirical investigation of developmental trajectories (B. O. Muthén, 2004; B. O. Muthén & Muthén, 2000). This growth mixture modeling (GMMing) approach also allows examination of predictors of development over time in the same modeling framework. Accordingly, it is an appropriate method to use in addressing specific questions focusing on the shape of change. Informed by the above literature, we formulated four questions relating to the shape of change in routine service settings as the focus of this study. First, are there typical shapes of change in a global outcome variable over the first six treatment sessions in a sample of clients treated under routine outpatient conditions? Second, do these client groups with characteristic shapes of change differ from one another with respect to discontinuity in treatment courses? Third, are these shapes of early change associated with different final treatment outcomes and/or different treatment durations? Fourth, are there client intake characteristics that enable discrimination between the shapes of early change and/or prediction of individual differences in development over time within client groups with similar shapes of early change? Settings Method The clients in our study were drawn from three main bases of a large United Kingdom National Health Service Trust, serving a local population of 320,000 people across the Wakefield Metropolitan District in West Yorkshire. Psychotherapeutic services are normally free to clients in these settings but require referral. About 1,100 referrals a year are received, mainly from local primary care providers. Of these referrals, approximately 75% attend at least one assessment appointment. Only clients offered individual therapy in one of the three main clinic bases were considered in this study (approximately 50% of referrals). This data set was also explored in Stiles et al. (2003) and Lutz et al. (2005). Participants Our sample consisted of 192 clients treated in these settings for at least 7 sessions and who provided symptom intensity ratings at the beginning of at least 3 of the first 6 treatment sessions (a minimum of 3 assessments was needed to allow for first-order growth modeling, as described later). All of these clients gave informed consent. The average client age at the beginning of treatment was 36.7 years (SD 10.8, range 16 63), and 137 (71%) of them were women. Client ethnicity was not routinely recorded, but the population served by the clinics is predominantly White and includes many people from former coal mining communities. The treatment duration ranged from 7 to 203 sessions (M 29.3, SD 26.0). There were no external criteria for
3 866 STULZ, LUTZ, LEACH, LUCOCK, AND BARKHAM treatment termination, and discharge from treatment was at the therapist s own discretion. The 33 therapists who participated in the study included 11 clinical psychologists, 4 consultant clinical psychologists, 1 consultant psychotherapist, 11 counselors, 2 counseling psychologists, and 4 nurse therapists (note that British categories of professional psychotherapists differ somewhat from American ones). All therapists had training in psychological therapy and at least 1 year postqualification experience. Twenty-three (70%) of the therapists were women, and their average number of years postqualification was 7.4 (SD 5.3, range 1 22). A variety of treatment approaches was offered, including cognitive therapy, psychodynamic therapy, gestalt therapy, cognitive analytic therapy, transactional analysis, and other integrative therapies. Many of the therapists were familiar with published treatment manuals, and 2 had previous specialized training in cognitive therapy, but none consistently followed a formal manualized protocol in the treatments we studied. Treatment duration was not subject to strict time limits, although a few therapists set time limits as part of their treatment strategy (Stiles et al., 2003). Instruments and Data Collection The Clinical Outcomes in Routine Evaluation (CORE) measures. To track treatment progress, immediately prior to (almost) every treatment session the clients in our sample completed the Clinical Outcomes in Routine Evaluation Short Form A & Form B (CORE SF; see Cahill et al., 2006), which are short forms of the Clinical Outcome in Routine Evaluation Outcome Measure (CORE OM; Barkham, Gilbert, Connell, Marshall, & Twigg, 2005; Barkham et al., 2001; Cahill et al., 2006; Evans et al., 2000, 2002). The last available CORE SF assessment was used to evaluate final treatment outcomes (see also Footnote 6). The two parallel short forms (CORE SF Form A and Form B) of the 34-item CORE OM comprise 18 items each: Four items cover subjective well-being, 6 ask for problems and symptoms, 6 assess functioning (including close relationships, general functioning, and social aspects), and 2 detect risk to self and risk to others. In the current study, we used only the overall scores of these items, answered on a 5-point scale (0 not at all to 4 most or all of the time). CORE SF clinical score is computed as the mean of all completed items, which is then multiplied by 10, so CORE SF clinical scores can range from 0 to 40. The internal consistencies for the CORE SF are.94 (Form A) and.94 (Form B), with means of 13.3 (SD 9.1) and 13.3 (SD 9.0), respectively. Further psychometric properties of the CORE measures have been reported in detail elsewhere (Barkham et al., 2001, 2005; Cahill et al., 2006; Evans et al., 2002; Stiles et al., 2003). For consistency, we considered the two CORE SF forms interchangeable in the current study. To determine measurable change on the CORE SF, we used the reliable change (RC) criterion (e.g., Jacobson & Truax, 1991). The RC criterion of an instrument depends on its reliability, and it equals the minimal amount of change in the score between two repeated assessments that is unlikely ( p.05) to occur without actual change (for details, see Jacobson & Truax, 1991). In the present data, the RC criterion for the CORE SF was 6.3 for Form A and 6.1 for Form B (M 6.2). In addition to the CORE SF, the following instruments were administered prior to each therapy. In the current study, the sum of their items was used as an index of severity of the respective problem area. Beck Anxiety Inventory (BAI). The BAI (Beck, Epstein, Brown, & Steer, 1988) is a client self-report scale with 21 items to measure severity of anxiety. Each item presents four responses and is scored from 0 to 3 to reflect the intensity of the respective symptom. The internal consistency (alpha) is.92, and the 1-week test retest correlation is.75 (Beck, Epstein, et al., 1988). Beck Depression Inventory (BDI). The BDI (Beck, Ward, Mendelson, Mock, & Erbaugh, 1961) is a client self-report measure with 21 items, each presenting several statements concerning a depressive symptom (e.g., sadness, crying, indecisiveness), scored from 0 to 3 to reflect the intensity of that symptom. The average internal consistency (alpha) is.86 among psychiatric clients. Further psychometric properties of the BDI have been well documented (cf. Beck, Steer, & Garbin, 1988). Inventory of Interpersonal Problems 32 (IIP-32). The IIP-32 (Barkham, Hardy, & Startup, 1996) is a shortened version of the 127-item IIP (Horowitz, Rosenberg, Baer, Ureno, & Villasenor, 1988). This client self-report instrument comprises 32 items, answered a 5-point scale ranging from 0 (not at all) to 4 (extremely), that cover a wide range of interpersonal difficulties. Correlations between the mean item score on the IIP and the IIP-32 were.94 and.96, respectively, at pre- and posttreatment administrations in an efficacy study of time-limited treatment for depression (Barkham, Hardy, & Startup, 1996; Hughes & Barkham, 2005). Data Analysis Strategy In this study, four steps of analysis were conducted: (a) determination of the number of distinct subgroups of clients with similar shapes of early change via unconditional GMMing (see below), (b) comparison of these shapes of early change with respect to (dis)continuity of treatment courses, (c) examination of associations between the shapes of early change and final treatment outcome and duration, and (d) identification of predictors of group membership probabilities (i.e., predictors of the shape of early change) together with the identification of predictors of growth within these groups (i.e., predictors of individual differences in development over time within the groups) via the incorporation of client intake characteristics as predictors into the growth mixture model (GMM). GMMing. The GMMing approach is particularly appropriate for these analyses, since it allows identification of unobserved groups of individuals with shared shapes of change over time in one or multiple outcome variables and examination of predictors of change over time (B. O. Muthén, 2004; B. O. Muthén & Muthén, 2000). The GMMing approach is based on conventional latent growth modeling (LGM), which analyzes longitudinal data by relating an observed outcome variable (e.g., treatment progress) to time or time-related variables (e.g., number of treatment sessions). In LGM, individual variation in growth is captured by the fact that coefficients (continuous latent variables or growth factors in a structural equation modeling framework) are random and can, therefore, vary across individuals. According to this approach, LGM (like, e.g., random coefficient regression modeling) implicitly assumes that all individuals in the sample are drawn from one single population with common population parameters. LGM then estimates one common mean growth curve, while the individual
4 SHAPES OF EARLY CHANGE 867 variation around this mean growth curve is captured by individual variation in the continuous latent growth factors. By implementing a categorical latent class variable into the modeling framework, GMMing relaxes this single population assumption of LGM and allows identification of latent classes (i.e., subpopulations of individuals) that correspond to different growth curve shapes. That is, GMMing allows identification of unobserved subpopulations of individuals that vary around qualitatively different mean growth curves, which are shared within homogenous latent classes. GMMing then both estimates the mean growth curve for each latent class and captures individual variation around these growth curves by estimating the growth factor variances for each class (B. O. Muthén, 2004; B. O. Muthén & Muthén, 2000). Moreover, in contrast to conventional cluster analytic approaches, GMMing also takes into account the often not perfectly reliable assignment of individuals to latent classes by estimating each individual s probability of membership in each of the latent classes (so-called posterior probabilities; Colder, Campbell, Ruel, Richardson, & Flay, 2002). Finally, GMMing also allows for the identification of predictors of these latent classes through the simultaneous use of multinomial logistic regressions in the modeling framework. In the present study, the GMMs were estimated with the Mplus software package (Version 3.11, L. K. Muthén & Muthén, 2004). Mplus uses maximum likelihood estimation and an accelerated expectation maximization procedure and allows for estimation of models with missing values in continuous outcome variables. Shapes of Early Change Results As predicted by the dose effect model of Howard, Kopta, Krause, and Orlinsky (1986), in our data set models with negatively accelerated (log-linear) mean growth curves consistently outperformed models that assumed linear change. 1 Therefore, the growth models we report below rely on the assumption of a log-linear relationship between the amount of treatment (sessions) and treatment progress. As a first step of the analysis, the number of distinct patterns of early change was determined by means of an unconditional GMM (B. O. Muthén, 2004; B. O. Muthén & Muthén, 2000). Starting with one latent class (i.e., with a conventional LGM), we incrementally entered additional latent classes into the GMM until the optimal number of latent classes was found. 2 Overall, the fit criteria to determine the optimal number of latent classes (shapes of change) in a GMM suggested five latent classes, with the growth parameter (co)variances held equal across latent classes, to be the most accurate solution. 3 This solution with five latent classes also makes sense in terms of the practical utility of the latent classes, which should also be a criterion when one is deciding on the number of latent classes (B. O. Muthén & Muthén, 2000). As can be seen in the upper part of Figure 1, which shows the GMM solutions with three, four, and five latent classes, adding a fourth latent class resulted in the extraction of an additional client group with early improvement during the first six treatment sessions (labeled Pattern 1 in Figure 1b and early improvement in Figure 1c). When we added a fifth latent class, the client group with medium initial impairment and, on average, no significant improvement over the first six treatment sessions (Pattern 4 in Figure 1b; slope: estimate 0.215, SE 0.373, ns) split into two groups (continuous and discontinuous in Figure 1c). Whereas these two groups were characterized by similar mean growth curves, they differed in two important characteristics of early treatment progress. On the one hand, the slope differed significantly from zero in the continuous group (estimate 0.297, SE 0.106, p.01) but did not do so in the discontinuous group (estimate 0.042, SE 0.327, ns). On the other hand, the clients in the discontinuous group experienced much more session-to-session variation on the CORE SF in an early treatment phase (see Figure 1e) than the clients in the continuous group (see Figure 1d; for a detailed evaluation of the session-tosession variation, see below). Besides these two groups with medium initial impairment, the five-class solution contained three further client groups two with high initial impairment, and one with low initial impairment. As shown in Figure 1c, among the high-impaired clients, there was a group with very high initial impairment and no change over the first six treatment sessions (slope: estimate 0.096, SE 0.123, ns) and another one with somewhat less initial impairment and very rapid early improvement (slope: estimate 1.563, SE 0.162, p.001). Finally, the clients in the low-impairment group did not, on average, improve over the first six treatment sessions (slope: estimate 0.107, SE 0.093, ns). To evaluate discontinuity in treatment progress, we applied the criteria proposed by Thompson et al. (1995) to the individual treatment courses within the five latent classes. 4 As shown 1 No results of the evaluation of these growth models are shown here because of space limitations, but detailed information is available from Niklaus Stulz on request. 2 Following B. O. Muthén (2001), we tested three types of models: In a first series of models, the variances and covariances of the growth parameters were fixed at zero (so-called latent class growth analysis; B. O. Muthén & Muthén, 2000; Nagin, 1999), while these growth parameter (co)variances were freely estimated in a second model type. Finally, in a third series of models, the growth parameter (co)variances as well as the residual variances of the outcomes were estimated but restricted to be equal across latent classes. 3 The following criteria to evaluate GMMs were considered: The Bayesian information criterion (Schwartz, 1978) is a model fit index that balances goodness of fit and parsimony of mixture models (Nagin, 1999), the Vuong Lo Mendell Rubin likelihood ratio test (Lo, Mendell, & Rubin, 2001) checks whether the implementation of an additional latent class results in a significant improvement of the Bayesian information criterion, and the entropy is a summary measure of classification quality of individuals into the latent classes (Ramaswamy, DeSabro, Reibstein, & Robinson, 1993). In addition, we also used a procedure developed by Kaas and Wasserman (1995) that calculates the probability that each model is the correct one with respect to the others under consideration. No detailed information on model specifications and on results are shown here because of space limitations. Detailed information is available from Niklaus Stulz on request. 4 The sudden gain concept, as used by Tang and DeRubeis (1999) and by Stiles et al. (2003), could not be evaluated in our sample because, as a result of missing values, there were not enough CORE-SF assessments within the first six treatment sessions.
5 868 STULZ, LUTZ, LEACH, LUCOCK, AND BARKHAM Figure 1. Mean latent growth curves for growth mixture model solutions with (a) three, (b) four, and (c) five latent classes, and estimated mean growth curves and observed individual values in the (d) continuous and (e) discontinuous groups of the five-class solution. CORESF Clinical Outcomes in Routine Evaluation Short Form. in Table 1, discontinuity was clearly highest in the discontinuous group. For instance, this was indicated by the average root-mean-square error of the individual growth models. Because the root-mean-square error represents the residual variance not explained by the negatively accelerated growth curve in effect, it equals the standard deviations of the residuals around the regression line it can be viewed as an indicator of the extent of the overall nonloglinearity or discontinuity of individual treatment courses (Thompson et al., 1995). As another aspect of discontinuity, we also considered the number
6 SHAPES OF EARLY CHANGE 869 Table 1 Indicators of Discontinuity in Treatment Progress for the Five Client Groups Client group Indicator of discontinuity 1. Early improvement M (SD) 2. Low impairment M (SD) 3. High impairment M (SD) 4. Continuous M (SD) 5. Discontinuous M (SD) ANOVA F (total) (significant comparisons) Session-to-session CORE-SF differences a 3.12 (1.46) 2.59 (1.35) 3.17 (1.65) 2.86 (1.42) 6.93 (2.96) *** (1 5, 2 5, 3 5, 4 5) RMSE of the log-linear regression model b 1.30 (0.79) 1.28 (0.71) 1.50 (0.83) 1.35 (0.56) 3.53 (1.65) *** (1 5, 2 3, 2 5, 3 5, 4 5) No. improvement shifts between consecutive assessments 0.58 (0.50) 0.08 (0.27) 0.06 (0.25) 0.13 (0.33) 0.76 (0.54) *** (1 2, 1 3, 1 4, 2 5, 3 5, 4 5) No. worsening shifts between consecutive assessments (0.27) 0.19 (0.39) 0.07 (0.26) 0.84 (0.50) *** (1 3, 1 5, 2 5, 3 5, 4 5) Total shifts between consecutive assessments 0.58 (0.50) 0.15 (0.46) 0.25 (0.48) 0.20 (0.48) 1.61 (0.76) *** (1 2, 1 4, 1 5, 2 5, 3 5, 4 5) No. difference calculations between consecutive CORE-SF assessments 3.29 (0.81) 3.89 (0.91) 3.58 (1.01) 3.36 (0.98) 3.42 (1.00) Note. ANOVA analysis of variance. CORE-SF Clinical Outcomes in Routine Evaluation-Short Form; RMSE root-mean-square error. a Because of missing values, not all session-to-session differences were available. Therefore, the mean session-to-session variations presented here are only approximative. However, the number of calculated session-to-session differences did not differ between the latent classes, F(4, 187) 1.484, p.209. b Indicates the overall discontinuity. of reliable CORE SF changes between consecutive assessments in individual treatment courses. 5 The mean number of such discontinuity shifts per client during the first six treatment sessions was also clearly highest in the discontinuous class, and the clients in this group experienced both improvement and worsening shifts (see Table 1). A high rate of improvement shifts was furthermore also observed among the earlyimproving clients, whereas none of them ever experienced a worsening shift. Compared to the highly impaired clients without early change, the early-improving clients experienced significantly more major between-assessments improvements. The number of available between-assessments differences did not differ significantly between the latent classes (see Table 1); thus, different baseline probabilities can be excluded as an alternative explanation for different numbers of shifts between latent classes. Table 2 Relative Frequencies of Treatment Durations and Change During Treatment on the CORE-SF (Effect Sizes) in the Five Client Groups Client group n Treatment duration (%) 7 13 sessions sessions 30 session Change during treatment (d) Early improvement Low impairment High impairment Continuous Discontinuous Final Treatment Outcomes and Treatment Durations by Shapes of Early Change As shown in Table 2, the changes on the CORE SF during treatment (effect sizes) and the relative frequencies of the different treatment durations (less than 14, 14 30, and more than 30 sessions) differed depending on the shape of early change. 6 For instance, the clients in the early-improvement group improved most of all during treatment (d 2.23, or 96% reliably improved), although treatments were comparatively short in this group of initially quite highly impaired clients (almost half the clients had fewer than 14 sessions). By contrast, the very highly impaired clients with no early change improved much less during treatment 5 In keeping with Stiles et al. (2003), these analyses relied on CORE-SF differences between assessments and not between treatment sessions because there were missing assessments in some sessions. 6 Note that these effect sizes, while helpful for relative comparisons of the improvement between the client groups, do not show the overall effect of the therapies. The overall effect of the therapies is underestimated because these effect sizes were calculated on the basis of the differences between the first and the last available CORE-SF assessment. However, not all clients in our sample had a CORE-SF assessment in Session 1 (some of them had the first assessment previous to Session 2 or even Session 3), and, moreover, posttreatment assessments were available only for a minority of the clients (Stiles et al., 2003). Additional analyses in a subsample of clients with a pretreatment and a posttreatment CORE OM full version assessment (n 80) yielded an effect size of 0.93, indicating that therapies worked in our client sample. The individual treatment durations were classified on the basis of their overall distribution in our sample: About one third of the clients were treated for fewer than 14 sessions, one third were treated for 14 to 30 sessions, and the remaining third were treated for 31 or more sessions.
7 870 STULZ, LUTZ, LEACH, LUCOCK, AND BARKHAM Table 3 Prediction of Group Membership and Prediction of Growth Within Groups by Client Intake Characteristics Early improvement (n 24) Low impairment (n 26) High impairment (n 48) Continuous (n 56) Discontinuous (n 38) Intake characteristic Effect t Effect t Effect t Effect t Effect t Prediction of client group a BAI BDI Prediction of intercept (initial status) Age BAI BDI IIP Prediction of slope (change) Age BAI BDI IIP Note. Values in boldface are significant coefficients (t 1.98). BAI Beck Anxiety Inventory; BDI Beck Depression Inventory; IIP Inventory of Interpersonal Problems. a The discontinuous group was used as the reference class in multinomial logistic regressions. (d 0.30, or 22% reliably improved), despite comparatively long treatments (only 2% of the clients had fewer than 14 sessions). The two groups with medium impairment at intake, the continuous group and the discontinuous group, did not differ in terms of the effect sizes (d 0.55 vs. d 0.54). However, using the RC criterion to evaluate change in these two groups revealed a higher rate of clearly improved clients in the group of clients with high session-to-session variation during the first six treatment sessions (discontinuous group) than in the group of clients with continuous early development (44% vs. 19%). This higher proportion of clearly positive treatment outcomes in the discontinuous group seems to be especially noteworthy since (a) the mean growth curve was less favorable in the discontinuous group than in the continuous group (see Figure 1c) and (b) the treatments tended to be shorter in the discontinuous group (see Table 2). However, conversely, the rate of reliably deteriorated clients was also higher in the discontinuous client group than among clients with continuous early treatment courses (13% vs. 0%). The lack of treatment effects in the low-impairment group (d 0.24, or 0% reliably improved) is addressed in the Discussion section. Overall, there was an almost significant correlation between the treatment duration and change on the CORE SF (r.14, p.052), whereas within the client subgroups there were no significant correlations between these two variables. Prediction of Early Change Besides the identification of typical shapes of early change by means of unconditional GMMs, we also examined whether available client intake characteristics (age, sex, depression, anxiety, and interpersonal problems at the beginning of treatment) enabled prediction of early change. Preliminary analyses with univariate prediction models for each predictor were run to determine whether a given client intake characteristic predicted (a) latent class membership (i.e., the shape of early change), (b) individual growth differences within latent classes, (c) both, or (d) neither (Stoolmiller, Kim, & Capaldi, 2005). In summary, these preliminary analyses yielded the following results. Age and the IIP-32 score at intake predicted the initial status (intercept) and/or change (slope) within at least one of the latent classes. The pretreatment score on the BAI as well as the intake score on the BDI both had genuine effects on both latent class membership probabilities and within-class growth. Sex predicted neither class membership nor growth within latent classes. 7 To identify a compact set of predictors for client allocation to latent classes and for within-class growth, respectively, the significant predictors from the univariate analyses were entered into a multivariate prediction model. In this final model, the predictors of within-class growth were tested by means of conventional regressions of the growth parameters on the predictors within the five latent classes. The predictors of latent class membership probabilities were examined via multinomial logistic regressions. For this, the discontinuous change pattern was designated as the reference group, and each client intake characteristic was tested to see whether it discriminated between a given latent class and the discontinuous class. Table 3 shows the results of these regression analyses. As can be seen, the BDI pretreatment score discriminated between the low-impairment class and the reference (discontinuous) class; the significant logistic regression coefficient of 0.14 represents the decrease in the log odds of being in the lowimpairment class versus being in the reference class for a unit increase on the BDI. That is, with each additional point on the BDI at the beginning of therapy, the probability of a client belonging to the low-impairment class, as compared to the probability of being a member of the reference class, decreased by the factor 0.87 (e 0.14 ). Moreover, higher BDI pretreatment scores tended to increase the probability of belonging to the high-impairment 7 No detailed results of these preliminary analyses are shown here because of space limitations, but detailed information is available from Niklaus Stulz on request.
8 SHAPES OF EARLY CHANGE 871 group, as compared to the discontinuous group ( p.10). The severity of initial anxiety allowed for a discrimination between the continuous and discontinuous client groups each additional point on the BAI decreased the probability of a client belonging to the continuous, versus the discontinuous, class by the factor 0.88 (e 0.13 ). The early-improvement class and the reference class could not be discriminated by either initial anxiety or initial depression. While every predictor was associated with initial impairment on the CORE SF within at least one latent class, in terms of the prediction of individual change over the first six treatment sessions within the latent classes, only the regressions of the slope on age and on the BAI pretreatment score within the continuous class yielded statistically significant results (see Table 3). The negative regression coefficients indicate that older age and higher initial anxiety were associated with lower (or more negative) slopes that is, with more rapid recovery in this client group. However, in the low-impairment group, older clients tended ( p.10) to have worse treatment courses. Finally, in the high-impairment class, more depressed clients tended to show more rapid improvement on the CORE SF in an early stage of treatment ( p.10). Discussion Using GMMing, on the basis of similar shapes of early change measured by a global outcome variable, we identified five groups of clients treated under routine clinic conditions. These client groups differed with respect to multiple characteristics of early treatment progress. For example, there were two groups with almost identical average impairment at intake that, however, differed in the average rate of early change and in the session-tosession variation of early treatment progress. In addition to these continuous and discontinuous groups, there were two further groups that differed predominately in initial impairment one characterized by very high impairment and the other by low impairment. Both these groups failed to improve during the first six sessions, and there was comparatively little discontinuity in their scores. A fifth group of initially highly impaired clients was characterized by rapid early improvement. Compared to the highimpairment group without early change, in this early-improvement group major between-sessions improvements occurred frequently. This finding of different courses of early change during psychotherapy is clinically appealing and consistent with previous findings (Barkham et al., 1993). The shapes of early change described so far were, furthermore, associated with different treatment outcomes and durations. In addition, both the severity of anxiety and the severity of depression at intake allowed differentiation between some of the five groups. Given this, the identification and prediction of shapes of early change can provide important information to support outcome management in routine clinical care, facilitate early identification of clients at risk of treatment failure, and provision of feedback to therapists. For example, in line with previous findings (e.g., Haas, Hill, Lambert, & Morell, 2002), the early-improving clients were clearly improved at discharge (d 2.23), despite the comparatively short treatments in this initially quite highly impaired group (almost half of the clients were treated for fewer than 14 sessions). It is therefore reasonable to recommend that clients in this group do not need longer term therapy. At present, the active mechanisms linking early dramatic improvement to positive treatment outcomes are unknown. They might include flight into health, medications beginning to work, reductions of psychosocial stressors, and simply very rapid response to psychotherapy deriving from insight or important realizations (Lambert, 2007). Also, if the clients of the early-improvement group respond to treatment before theoretically important techniques are introduced, then this might be theoretically and clinically important, since for such clients it would then be difficult to attribute central importance to these techniques. Likewise, in line with previous findings (Lutz et al., 1999), there was a second group of highly impaired clients who did not improve in an early stage of treatment. Consistent with the lack of early change, treatment effects were much lower (d 0.30) in this client group than in the early-improvement group, despite the comparatively long treatments (70% of the clients were seen for more than 30 sessions). However, the characteristics of the instrument may also play a role in the lack of improvement, since with clients with very severe problems the goals of therapy may be broader than those measured by the CORE SF. As in previous studies that found associations between discontinuity in treatment courses and higher recovery rates at discharge (Stiles et al., 2003; Tang & DeRubeis, 1999; Thompson et al., 1995), clearly positive treatment outcomes were more frequent among our discontinuous group than among the clients with quite identical initial impairment but continuous early treatment progress (44% vs. 19% reliable improvements). This was the case even though the continuous group, on average, improved more over the first six treatment sessions and did not receive fewer sessions. On the basis of these findings, therapists may be reassured to have empirical support that discontinuity and instances of significant worsening can be expected over the early course of therapy and do not necessarily forecast treatment failure. However, further research with follow-up assessments should examine whether the higher proportion of improved clients at discharge in the discontinuous group remains stable over time. For example, Thompson et al. (1995) found high numbers of major betweensessions shifts to be related not only to better treatment outcomes at discharge but also to a higher relapse risk at follow-up. It is possible, for example, that there is a bias for clients in the discontinuous group to be discharged at a point where their status is positive but their variability means their status could more readily become negative after discharge. Furthermore, it should also be noted that in our discontinuous client group not only positive but also clearly negative final treatment outcomes were more frequent than in the continuous group (14% vs. 0% reliable deteriorations). This suggests that, at some yet unknown threshold, high discontinuity of early client progress might indicate vulnerability to negative treatment outcome and/or early relapse. Analyses of predictors of latent classes suggest that more severe anxiety at intake decreases the probability of belonging to the continuous, as compared to the discontinuous, group. Following this, anxious clients might tend to show more instability during early treatment phases, resulting in higher chances for positive treatment outcomes but also higher risk for negative treatment outcomes. The low-impairment client group improved least of all groups (d 0.24). However, this seems to have been due to characteristics of the instrument. The low-impaired clients started treatment with an average CORE SF intake score (7.7) that nearly equaled
9 872 STULZ, LUTZ, LEACH, LUCOCK, AND BARKHAM the CORE SF score in a nonclinical sample (7.6). Thus, for initially low-impaired clients, there seems to be not much room to improve on the CORE SF (i.e., floor effect). Unfortunately, our data do not allow us to answer the question of why therapists provided therapy to these clients. One possible explanation is that these clients suffered from problems that are not assessed by the CORE SF. Concerning the prediction of membership in this lowimpaired group, lower depression at intake significantly increased the probability of belonging to this low-impaired group (relative to the probability of belonging to the discontinuous group), whereas initial anxiety did not discriminate between these two client groups. As for the prediction of the shape of early change by intake characteristics, there were few significant predictors of individual treatment progress (i.e., of the slopes) within the client groups with typical shapes of early change. Being older and, somewhat surprising, reporting higher anxiety at intake were both associated with more rapid early improvement in the continuous client group. The second finding may be a result of clients experiencing a high level of anxiety over some recent life events that resolved themselves relatively quickly. Conversely, among low-impaired clients, older clients tended to have a poorer early treatment course ( p.051). These findings support the idea that there might be client intake characteristics that predict individual treatment progress in early treatment phases only for specific subsets of clients, and the application of GMMing procedures revealed its potential to identify such predictors of individual treatment progress in early treatment phases. To find more subpopulation-specific predictors, further studies with other data sets that contain more client intake characteristics are necessary. For example, the present study did not contain measures of Axis II disorders, such as personality disorders, which tend to be associated with more modest improvements from longer term psychotherapy (Bateman & Fonagy, 2000). In the current study, the absence of numerous predictors of within-class growth in early treatment phases might also be caused by the fact that the latent classes absorbed much of the variation in the growth factors. While this implies that groups were homogenous with respect to development over the first few treatment sessions, it also impedes the search for intake characteristics that predict a significant proportion of the little amount of remaining variance in growth factors. Besides the strengths of the current study, there are also some limitations. Because the study was based on a naturalistic sample, there is the traditional trade-off of high external validity at the expense of internal validity. Moreover, our data have the familiar limitations of studies conducted under routine care conditions, namely lack of complete data on diagnosis and other characteristics of participants as well as lack of detailed data on therapists (for a thorough account of these issues, see Stiles, Barkham, Mellor- Clark, & Connell, in press; Stiles, Barkham, Twigg, Mellor-Clark, & Cooper, 2006). Hence, for example, an examination of therapist effects (e.g., Lutz, Leon, Martinovich, Lyons, & Stiles, 2007) was not feasible, since we did not know for every particular client by which particular therapists he or she was treated. As a further consequence of the naturalistic design, the possibility that there were unobserved variables associated with the predictors, on the one hand, and with initial impairment and/or early treatment progress, on the other hand, cannot be excluded. Therefore, it might still be the case that these unobserved variables caused the associations between the predictors and the patterns of early change. As a final limitation concerning predictors, as already mentioned, we were forced to focus on the few client intake characteristics available in our data set. Further research should examine additional predictors as well as time-varying covariates (e.g., life events, social support, or the therapeutic alliance) that can also contribute to the understanding and explanation of early treatment courses. Provided that there is reliable diagnostic information, further studies might also look for specific patterns of early change for certain diagnostic groups or subgroups. Another important question for further research is to test whether the different shapes of early change allow for better predictions of final treatment outcomes than other, less complex prediction models, which, for example, use the initial impairment and/or the amount of change over the first few treatment sessions to predict individual treatment outcomes. Because of missing data (not all clients in our sample had CORE SF assessments in Sessions 1 and 6), we could not, unfortunately, examine this question. Finally, further studies with other data sets and instruments are necessary to examine whether the shapes of early change identified in the current study are representative of psychotherapy outpatients. Such replication is of particular importance in exploratory research on subtypes, because there are no strong rules to determine the subtypes, and these can vary considerably across samples. In addition, in our study the client groups were quite small, which may also affect the generalizability of the findings. Given the replication of these subgroups, further studies should also strive for an in-depth exploration of the change processes underlying these different patterns of early change (Stulz & Lutz, 2007). Despite these limitations, the current study demonstrates a viable approach to exploring early change during psychotherapy. While the exploration of change should be a central concern for psychotherapy research, a widely held model of change often used to analyze psychotherapy outcomes is based on pretreatment to posttreatment statistical comparisons and assumes that change is linear and steady across time (Barkham et al., 1993). By identifying different shapes of early change, the current study overcomes this misleading assumption and can, therefore, help in developing feedback systems to support decision making in clinical interventions. Furthermore, the in-depth exploration of early change as carried out in this study can also contribute to a better understanding of early change processes during treatment and of factors affecting these processes. This might then be helpful in developing theoretical models of how change unfolds during early treatment phases, and it can provide important information about how to configure and conduct clinical interventions. References Barkham, M., Connell, J., Miles, J. N. V., Evans, C., Stiles, W. B., Marginson, F., & Mellor-Clark, J. (2006). Dose effect relations and responsive regulation of treatment duration: The good enough level. Journal of Consulting and Clinical Psychology, 74, Barkham, M., Gilbert, N., Connell, J., Marshall, C., & Twigg, E. (2005). Suitability and utility of the CORE-OM and CORE-A for assessing severity of presenting problems in psychological therapy services based in primary and secondary care settings. British Journal of Psychiatry, 186, Barkham, M., Hardy, G. E., & Startup, M. (1996). The development of the
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