Examining differential effects of psychosocial treatments for cocaine dependence: An application of latent trajectory analyses
Introduction
Psychosocial interventions form the basis of the treatment of cocaine dependence (Carroll, 2005, Vocci and Montoya, 2009). However, there is an ongoing debate about the optimal type of psychosocial treatment for cocaine dependence and whether different treatment approaches might be best suited for different subgroups of cocaine dependent patients (Carroll et al., 1994a, Maude-Griffin et al., 1998).
So far two main lines of research have been followed in cocaine treatment research: on the one hand, there has been research that primarily focused on outcomes of treatments to establish their efficacy. To compare the efficacy of different treatment approaches, this kind of research usually compared samples of patients treated with different interventions in terms of their mean outcomes. Findings from these studies have been variable, with some suggesting that specialized professional psychosocial treatments are superior to drug counseling approaches (Higgins et al., 1993, Maude-Griffin et al., 1998), other studies indicating no differences between different treatment approaches (Carroll et al., 1998), whereas in the largest study done to date, in the National Institute on Drug Abuse (NIDA) Collaborative Cocaine Treatment Study (CCTS), a combination of individual drug counseling and group drug counseling produced statistically and clinically superior outcomes compared with two types of professional psychotherapy in terms of reducing cocaine use as well as overall drug use (Crits-Christoph et al., 1999).
However, the mean effects of various treatments for a specific disorder in samples of patients usually used to evaluate treatments in randomized controlled trials may mask differential treatment effects on individuals or subgroups within a sample (Cuijpers et al., 2005). Psychotherapy research, e.g., has shown that comparisons of aggregated pre-to-post treatment change between patient samples, relying on the assumption of linear and steady change over the course of treatment, may mask potentially meaningful differences in individual treatment courses (Krause et al., 1998) or different patterns of change that are shared by many individual patients (Lutz et al., 2009, Stulz and Lutz, 2007, Stulz et al., 2007), and that this interindividual variation in treatment outcomes may be clinically important (Barkham et al., 1993).
By shifting the focus from mean outcome differences between patient samples to predictors of treatment outcomes, a second line of research was giving more attention to individual differences in treatment outcomes of cocaine dependent patients (e.g., Crits-Christoph et al., 2003, Crits-Christoph et al., 2007, Siqueland et al., 2004, Siqueland et al., 1998). For example, by reanalyzing the data of the NIDA CCTS, Crits-Christoph et al. (2007) identified 4 baseline characteristics (craving, acuity of biomedical problems, belief in the 12-step philosophy, and expectations for improvement) that predicted interindividual differences in sustained abstinence from drug use irrespective of the type of treatment. Also, research on mediators and moderators of treatment outcome increasingly shows that some subpopulations of psychotherapy patients do benefit less than others do from psychosocial treatments (Shadish and Sweeney, 1991).
Overall, these research findings underscore the significance of heterogeneity among psychotherapy patients in general and among cocaine users in particular, and they point to the possible need to develop and use specialized treatments for clinically distinct subgroups of cocaine abusers (Carroll et al., 1994a). Although the two lines of research discussed just before (the one focusing on comparisons of mean outcomes of different treatments and the other focusing on predictors of individual differences in outcomes) usually also looked for predictor × treatment × outcome-interactions, modern techniques for longitudinal data analysis provides an alternative that brings together these two research traditions via the identification of patient subgroups with typical patterns of change during treatment. These growth mixture models (GMMs) permit the identification of distinct groups of individuals who differ in the initial level and the course of a specific behavior (e.g., drug use) through the empirical identification of developmental trajectories (Muthén, 2001). Furthermore, these GMMs also allow examination of whether the effects of different interventions differ for these subpopulations, ascertain which characteristics predict membership to these subpopulations, and establish whether outcomes are different for each of these subpopulations (Muthén, 2001, Muthén et al., 2002). In contrast to cluster analytic approaches, which have also been used to identify typical growth trajectories in outpatient psychotherapy (Barkham et al., 1993) and in substance abuse treatment (Morral et al., 1997, Waldron et al., 2005), GMMs allow simultaneous estimation of subgroup-specific treatment effects, which makes them a promising approach to examine differential treatment effects in patient subgroups. Concerning the examination of moderators of treatment outcomes, there is yet another advantage of GMMs: a fundamental problem inherent in traditional research studies on moderators of treatment outcomes is that these studies are looking for subpopulations who benefit more or benefit less from an intervention, but actually examine only characteristics which may be indicative of these subpopulations (Cuijpers et al., 2005). By contrast, if using the GMM approach, the identification of patient subpopulations within a sample is based on the target behavior itself (e.g., on changes in drug use over time).
These characteristics make GMMs specifically appropriate to examine the following research questions in the NIDA CCTS dataset that go beyond previous reports of average treatment outcomes and predictors of average outcomes: (1) Are there different trajectory classes (i.e., patient subgroups following different patterns of change) of drug and cocaine use during psychosocial treatments for cocaine dependence?; (2) Are there patient baseline characteristics that allow allocation of patients to these trajectory classes?; and (3) Do different psychosocial treatments have differential effects on drug and cocaine use in different trajectory classes (i.e., in different patient subgroups)?
Section snippets
Design and procedures
The design and procedures of the NIDA CCTS have already been detailed elsewhere (Crits-Christoph et al., 1997, Crits-Christoph et al., 1999). In brief, the NIDA CCTS was a multi-site randomized clinical trial that compared the efficacy of four psychosocial treatments for cocaine dependence: in two of these treatments, professional psychotherapy, either cognitive therapy (CT; Beck et al., 1993) or supportive-expressive psychodynamic therapy (SE; Luborsky, 1984, Mark and Luborsky, 1992), was
Results
As indicated by the lowest value in the Bayesian Information Criterion (BIC; Schwartz, 1978)—a model fit index that balances goodness of fit and parsimony of (mixture) models (Nagin, 1999)—a cubic latent growth model best fitted change data in the ASI–Drug Use Composite Score (intercept-only: 18121.73, linear: 17902.57, loglinear: 17749.67, quadratic: 16927.79, cubic: 15940.14) as well as in the outcome variable counting the number of days with cocaine use during the past 30 days
Discussion
Whereas the efficacy of psychosocial interventions for the treatment of cocaine dependence has been demonstrated (Carroll, 2005, Dutra et al., 2008, Woody, 2003), there is an ongoing debate whether different psychosocial interventions are equally effective or some are superior to others (Carroll et al., 1998, Crits-Christoph et al., 1999, Higgins et al., 1993, Maude-Griffin et al., 1998). In this study, we examined differential effects of the four psychosocial interventions for cocaine
Role of funding sources
Preparation of this article was supported in part by grant PBBEP11-123652 (Niklaus Stulz) from the Swiss National Science Foundation (SNF). Collection of the original data was supported in part by grant U01-DA07090 and career development awards K05-DA00168, K02-DA00326, U01-DA07663, U01-DA07673, U01-DA07693, and U01-DA07085 from the National Institute on Drug Abuse, Rockville, MD, and Clinical Research Center grant P30-MH-45178 and Career Development Award K02-MH00756 from the National
Contributors
Niklaus Stulz was responsible for the concept, the data analyses and the final draft of the manuscript. Robert Gallop consulted in data management and analyses. Wolfgang Lutz and Glenda L. Wrenn contributed to the manuscript from the first draft to the final version. Paul Crits-Christoph consulted in data analyses and contributed to the manuscript from the first draft to the final version. All authors contributed significantly to and have approved the final manuscript.
Conflict of interest
All authors declare that they have no conflicting interests.
Acknowledgements
The NIDA Collaborative Cocaine Treatment Study was a National Institute on Drug Abuse (NIDA) funded Cooperative Agreement involving four clinical sites, a Coordinating Center, and NIDA staff. The Coordinating Center at the University of Pennsylvania included: Paul Crits-Christoph, Ph.D. (PI), Lynne Siqueland, Ph.D. (Project Coordinator), Karla Moras, Ph.D. (Assessment Unit Director), Jesse Chittams, M.A. and Robert Gallop, M.S. (Director of Data Management), and Larry Muenz, Ph.D.
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