Naltrexone and combined behavioral intervention effects on trajectories of drinking in the COMBINE study
Introduction
Some of the heterogeneity of clinical findings in studies evaluating the efficacy of pharmacotherapies and behavioral therapies in the treatment of alcohol dependence can be attributed to the wide use of standard statistical analytical tools of summary drinking measures that poorly reflect the distributions, variability, and complexity of drinking data. Novel statistical analysis tools based on trajectories over time provide a more realistic and complete picture of treatment effects on drinking behavior. In secondary analyses of data from the VA naltrexone study (Krystal et al., 2001) and the Women's naltrexone study (O’Malley et al., 2007), we successfully used trajectory analyses to examine whether there were distinct trajectories of drinking during treatment and whether naltrexone modified the chance of following a specific trajectory (Gueorguieva et al., 2007). The results of these analyses were remarkably similar across the two studies and revealed three trajectories (“abstainers”, “sporadic drinkers”, and “consistent drinkers”). Despite negative findings on the primary summary measures based on more traditional analytic methods, in the trajectory-based reanalysis we demonstrated that naltrexone significantly decreased the chance of being in the “consistent drinker” trajectory. In a similar reanalysis using latent growth mixture models of the Project MATCH data, Witkiewitz et al. (2007) also identified three trajectories of drinking and were able to detect interactions between baseline self-efficacy and treatment (Cognitive Behavioral Therapy vs. Motivational Enhancement) for frequent drinkers that had not been detected in the original analyses. These three studies demonstrate the ability of trajectory-based approaches based on mixture models to capture heterogeneity in drinking data and to identify trajectories where treatment effects are more pronounced.
Trajectory analyses have been also applied to large-scale observational studies of developmental patterns of alcohol use (Muthén and Muthén, 2000a, Muthén and Muthén, 2000b, Hill et al., 2000, Chassin et al., 2002, Del Boca et al., 2003, Greenbaum et al., 2004). For example, Muthén and Muthén, 2000a, Muthén and Muthén, 2000b have explored the development of heavy drinking and alcohol-related problems from ages 18 to 37 in a nationally representative sample from the National Longitudinal Study of Youth. Hill et al. (2000) and Chassin et al. (2002) have assessed developmental trajectories of adolescent binge drinking. Chung et al., 2004, Chung et al., 2005 have analyzed drinking patterns and the relationship of drinking patterns and symptom occurrence in treated adolescents.
In the current manuscript we perform exploratory trajectory analyses to investigate the effects of naltrexone, acamprosate and the Combined Behavioral Intervention (CBI) in the COMBINE study. The COMBINE study (Anton et al., 2006) represents the largest study of pharmacotherapy for alcoholism in the United States. It was designed to answer questions about the benefits of combining behavioral and pharmacological interventions. Naltrexone, an opiate antagonist, was studied based on evidence that it reduced the risk of heavy drinking and increased the percentage of days abstinent in most studies (Kranzler and Van Kirk, 2001, Pettinati et al., 2006, Srisurapanont and Jarusuraisin, 2005). Acamprosate, thought to reduce glutamatergic hyperactivity associated with protracted abstinence, was chosen because it had been demonstrated to maintain abstinence within varied behavioral treatment frameworks (Mann et al., 2004, Mason, 2001, Mason, 2005, Soyka et al., 2002). The behavioral interventions examined included Medical Management (MM) (Pettinati et al., 2004, Pettinati et al., 2005) and the Combined Behavioral Intervention (CBI) (Longabaugh et al., 2005, Miller, 2004). MM was designed as a means of enhancing medication compliance and reinforcing of sobriety that could be used in a primary care or managed care settings by nonspecialists (Fleming et al., 1997). CBI integrated components from cognitive behavioral, motivational enhancement, and 12-step facilitation therapies originally developed for and evaluated positively in Project MATCH Research Group, 1997a, Project MATCH Research Group, 1997b.
At the time COMBINE was designed, summary measures derived from timeline reports of daily drinking were the standard approach to assessing outcomes (Babor et al., 1994, Finney et al., 2003). In COMBINE, the two primary outcomes were time to the first day of heavy drinking and percent days abstinent in the 16-week treatment period. The primary findings were that either naltrexone (+ MM) or CBI (+ placebo naltrexone + MM) improved outcomes compared to MM + placebo and that there was no additional advantage of combining CBI with naltrexone over each monotherapy. The fact that there was no advantage of combined treatment with CBI and naltrexone was unanticipated. In addition, the failure to find an effect of acamprosate either alone or in combination with CBI or naltrexone was particularly unexpected given the positive studies of acamprosate (Mann et al., 2004, Mason, 2005) and of the combination of acamprosate and naltrexone (Kiefer et al., 2003, Feeney et al., 2006) conducted in Europe.
Like other studies, the primary outcome measures used by COMBINE have a number of potential limitations. For example, time to the first heavy day of drinking does not take advantage of the daily reports of drinking that occur after the first event. The distribution of percentage of days abstinent is skewed and subject to ceiling effects. None of these summary measures allow description of temporal trends of the data and the standard statistical analyses that are typically applied to these measures poorly reflect the multimodal distribution of drinking data.
Advances in longitudinal statistical modeling enable the use of daily drinking data. Growth modeling (Lindsey, 1993, Longford, 1993, Diggle, 1994, Raudenbush and Bryk, 2002) assumes that every individual follows the same type of trajectory over time, while mixture approaches (Muthén and Muthén, 2000a, Muthén and Muthén, 2000b, Nagin, 1999, Dolan et al., 2005) allow data-driven identification of distinct classes of developmental trajectories. Thus, it is possible to identify subgroups of subjects who show distinct patterns of clinical response within a clinical trial based on the structure of the data generated by that trial, i.e., subgroups that might not have been hypothesized a priori by the investigative team.
For heterogeneous populations, the trajectory-based approach appears to be more powerful than the analysis of traditional summary measures of drinking and time to event models. For example, trajectories capture information on frequency of drinking, duration of abstinence and rate of change over time. If we were to fit a model using summary measures of drinking we would have to analyze three highly correlated such measures to capture all these aspects of drinking. Although no direct comparison of trajectory models to multiple time-to-event methods (Wang et al., 2002) has been performed to date, the purposes of these analyses are different and these procedures should be regarded as complementary rather than competing. In trajectory models the goal is to identify subgroups of subjects who might be more or less responsive to interventions while in multiple time-to-event models the goal is to take into account drinking behavior for the population as a whole beyond the first episode of drinking.
The main objective of the trajectory re-analyses of the drinking data in COMBINE was to estimate distinct trajectories of any drinking and heavy drinking and to assess the effects of naltrexone, acamprosate and CBI on these trajectories. We hypothesized that different treatments may affect different trajectories of drinking. In particular, we predicted that there would be at least three trajectories of drinking over time, similar to those obtained in the VA naltrexone study and the women's naltrexone study (“abstainers”, “sporadic drinkers” and “consistent drinkers”) on any and heavy drinking. We anticipated that naltrexone would significantly decrease the chance to belong to a “consistent drinker” trajectory as compared to “sporadic drinker” and “abstainer” trajectories and we hypothesized that CBI would have a similar effect. Based on the original COMBINE analyses, we did not anticipate that the combination of CBI and naltrexone would be more beneficial than either CBI or naltrexone alone. We also planned to explore acamprosate effects on the probability to follow particular trajectories although we did not anticipate observing significant results given that the tests of acamprosate effects were not significant in the original COMBINE analyses.
Given the larger sample size, we further hypothesized that we would be able to find a larger number of classes of drinking patterns over time in which the “sporadic drinker” and “consistent drinker” classes might split into subclasses that might show stronger treatment effects. In particular, we anticipated that naltrexone might be associated with a trajectory of decreasing drinking over time. Sinclair (1990) hypothesized that extinction of drinking behavior should occur over time with naltrexone due to attenuation of alcohol reinforcement, and preclinical studies have demonstrated that naltrexone progressively reduces the onset and duration of drinking over multiple sessions (Hyytia and Sinclair, 1993). Consistent with this perspective, naltrexone increased the number of days to a second episode of drinking following a lapse in abstinence among alcohol dependent patients (e.g., Anton et al., 1999). At the same time, CBI teaches new skills for coping with situations that otherwise lead to drinking. The benefit of CBI may not emerge until later during treatment, however, because CBI requires several sessions to impart this information (Longabaugh et al., 2005). This benefit could conceivably involve an improvement over time or maintenance of initial improvement. Clinical trials of cocaine abusers provide evidence that Cognitive Behavior Therapy, a component of CBI, has a delayed emergence of efficacy involving maintenance of improvements relative to comparison treatment conditions in which initial gains deteriorated over time (Carroll et al., 1994, Carroll et al., 2004).
Section snippets
Methods
The COMBINE study enrolled 1383 abstinent alcohol dependent patients. Eight groups received medication management (MM) and either placebos, naltrexone, acamprosate, or naltrexone + acamprosate. Half of these groups also received the CBI. Participants on different treatments were found to be comparable on seventy-six pretreatment characteristics (Anton et al., 2006). Analyses of the two primary endpoints, time to the first day of heavy drinking and percent days abstinent, revealed that either
Replication of drinking trajectories
Fig. 1, Fig. 2 plot the estimated trajectories for any and heavy drinking respectively when the number of trajectories was restricted to three. The models with third-degree polynomials fit better than the models with second-degree polynomials and are presented here. The three trajectory patterns over time are similar to the trajectories estimated for the VA naltrexone trial and the Women's study and are interpreted as “abstainers” (48.2% of the sample), “sporadic drinkers” (35.5%) and
Discussion
In these re-analyses of COMBINE data, we identified six clinically useful trajectories of any drinking, replicated a three trajectory solution for heavy drinking, showed that naltrexone and CBI increase the probability of lower risk trajectories and established an association between compliance and drinking trajectories. Although the overall conclusions about the efficacy of naltrexone, CBI and acamprosate are consistent with the conclusions drawn based on traditional summary measures, we
Role of funding source
Funding for this study was provided by NIMH Grants RO1 AA017173-01, P50 AA-012870, P50 DA09241, K05-AA014906, KO514906, K05 DA00089; the National Center for Research Resources, NCRR UL1RR024139; and the following U.S. Veterans Administration Centers: Mental Illness Research, Education and Clinical Center; Alcohol Research Center; Merit Review Program and National Center for PTSD. The sponsors had no further role in the analysis and interpretation of data; in the writing of the report; or in the
Contributors
Authors Gueorguieva and O’Malley designed the study, prepared the literature overview, evaluated results from statistical analyses and wrote the first draft of the manuscript. Authors Wu and Gueorguieva undertook the statistical analyses. Authors Donovan, Rounsaville, Couper and Krystal participated in review of the results from statistical analyses, data interpretation and manuscript revisions. All authors contributed to and have approved the final manuscript.
Conflict of interest
During the period of 2007–2009, Dr. Krystal has served as a scientific consultant and/or on the Scientific Advisory Board to the following companies: Abbott Laboratories, AstraZeneca Pharmaceuticals, Bristol-Myers Squibb, Eli Lilly and Co., Janssen Pharmaceuticals, Merz Pharmaceuticals, Pfizer Pharmaceuticals, SK Holdings Co., Ltd., Takeda Industries, and Transcept Pharmaceuticals. He holds less than $10,000 in exercisable warrant options with Transcept Pharmaceuticals. He is the principal
Acknowledgements
The authors wish to acknowledge Mr. Brian Pittman for helpful comments on interpretation and presentation of statistical results.
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