Although a few neurocognitive and psychiatric variables were associated with treatment outcome, the frequency of MA abuse at study outset was a much stronger predictor of outcome. Specifically, participants with two or fewer urine tests positive for MA metabolite during the first two weeks of the study (i.e., baseline period) were much more likely to complete treatment and achieve abstinence from MA in the majority of the treatment weeks. In contrast, participants with three or more MA-metabolite positive urines during the baseline period were more likely to drop out and continue to abuse MA regularly. These findings are consistent with several other studies finding an association between baseline frequency of MA abuse and treatment success (Peterson et al., 2006
; Maglione et al., 2000b
; Brecht et al., 2005
; Brecht et al., 2006
; Hillhouse et al., 2007
; Maglione et al., 2000a
; Shoptaw et al., 2008
). Generally speaking, the best predictor of future MA abuse is the frequency of current MA abuse.
Because contingency management treatment was provided during the two week baseline period (in contrast with CBT and study medication which began after the two baseline weeks), MA use during the baseline period may reflect response to the initial contingency management treatment, as well as the baseline frequency of MA use prior to treatment (indeed, MA use in the 30 days prior to enrollment and during the two week baseline period were strongly related in our study). Still, the relationship we found between MA abuse at baseline and treatment outcome may be partly accounted for by the relationship between early and later responses to contingency management. While this complicates the interpretation of initial MA use, our results are nonetheless apt to be generalizable to the situation in the majority of substance abuse treatment programs in which baseline urinalysis is not conducted prior to treatment initiation. Further, although the relationship between baseline MA abuse and the mostly abstinent outcome may have been spuriously inflated due to the similarity in metric (both are measured by urinalysis), such measurement issues would not explain the strong predictive power of baseline MA abuse for the study completion outcome variable, which relied on session attendance rather than urinalysis. As anticipated, those participants who were mostly abstinent also tended to be completers (79%), but differences in predictors between these two outcomes do indicate somewhat different correlates. In sum, use patterns during the initial stages of treatment are likely to be quite predictive of subsequent outcome, both in terms of continued MA abuse and treatment attendance.
While self-reported MA use was not as predictive of treatment outcome as was urine-verified use, several self-report measures were identified in the CART importance ratings as associated with outcome. The importance ratings indicate the degree to which the variables could be used as proxies for the (best) predictors identified in the CART trees. Self-report measures with high importance ratings included days of MA used in the month preceding the study, longest full-time job (years), recent money spent on MA, and frequency of recent polysubstance abuse. However, when baseline urinalysis in the models was restricted to only the first urine obtained or was removed altogether, the subsequent prediction trees did not exceed chance guessing. Thus when considered individually, no variable other than the one derived from multiple initial urine tests was exceptionally predictive of treatment outcome. This observation suggests that treatment programs wishing to identify participants at risk for treatment failure should use multiple urine tests if possible.
Variables other than urinalysis may become most useful in predicting subsets of MA users who are successful or unsuccessful after initial MA usage is known. The elegance of the CART analysis lies in the ability to easily visualize interactions between multiple variables. For example, in the analysis of treatment completion (see ), the Global Assessment of Functioning (GAF) score from the DSM-IV diagnosis (i.e., Axis V) specifically aided prediction for frequent users of MA at baseline. For frequent users, a low GAF score (<70) was associated with an exceptionally high probability of treatment drop-out (100%). In contrast, for those who showed infrequent initial MA abuse, the most discriminating variable after urinalysis was the Fagerström Test of Nicotine Dependence. Infrequent MA abusers with low Fagerström scores were quite likely to drop out of treatment (88% drop out), while infrequent users with higher Nicotine Dependence scores more likely to remain in treatment (30% drop out). In this way, CART prediction trees provide straightforward expectations regarding the treatment success of samples and subsamples of MA participants, and may be particularly useful in clinical settings in which an individual’s risk for treatment noncompliance can be quickly estimated from a few baseline measures. However, because the sample sizes of some of the CART nodes were small (i.e., GAF and Fagerstrom nodes), additional research in different treatment settings is needed to determine the generalizability of the interactions.
A GAF score above 70 is associated with transient psychiatric symptoms and no more than slight functional impairment (Association, 2000
). In contrast, scores below 70 suggest at least mild symptoms, with increasingly significant functional impairment as the GAF score lowers. In the GAF results in our study, this indicates that frequent baseline users of MA judged to have almost any functional impairment were exceptionally likely to drop out of treatment (100%). Frequent users with limited functional impairment were more likely to stay in treatment (43%), although even these individuals still dropped out of treatment more than half of the time. This finding reiterates the fact that frequent baseline MA abuse was associated with poor treatment outcome in the majority of cases.
It is not clear why a low level of nicotine dependence was associated with treatment drop out in the infrequent MA abusers. These individuals had lower levels of dependence on MA and nicotine and may not have been sufficiently motivated to complete treatment due to a perception that a brief period of treatment was sufficient for their severity of disease. It is also worth noting that the medication examined in the clinical trial, bupropion, is approved for smoking cessation treatment. While this might raise the possibility that infrequent MA users with higher levels of nicotine dependence might have been retained in the trial due to a benefit of bupropion on their cigarette smoking behavior, we found no evidence of an interaction between medication administration, nicotine dependence, and treatment completion in our analyses.
A few neurocognitive measures were associated with treatment outcome (1-back errors in univariate analyses; SRT and CRT in the importance ratings for study completion), but these variables were not the best predictors of outcome when compared to other indices. Cognitive performance was not related to a gross measure of MA abuse at the time of cognitive testing (testing positive or negative for MA on the testing day), so differences in use at the time of testing did not appear to explain the weakness of predictability demonstrated by cognitive measures. However, the day on which participants received cognitive testing during the baseline period was variable, and the time elapsed since last use of MA at the time of cognitive testing was not known. Thus, we cannot be certain that recent MA usage factors did not add variability into the measurement of cognitive function. Also, because the cognitive measures were only measured once, it is possible that cognitive changes occurred during treatment (possibly associated with frequency of MA use) which were not captured by the single-point assessment.
Our findings on the predictive value of cognitive measures are less robust than those from studies of individuals who abuse cocaine, in which treatment completion has been associated with cognitive performance in a number of domains, including attention, memory, spatial processing, and global cognitive functioning (Aharonovich et al., 2003
; Aharonovich et al., 2006
). However, in contrast with study completion, cognitive variables from these studies have been inconsistently linked with the actual use of cocaine during treatment. Similarly, these studies did not compare cognitive variables to those of initial cocaine use during the beginning of treatment in predicting outcome (only prior self-reported use). It is therefore unclear whether markers of initial drug use would supersede cognitive variables in prediction utility, as demonstrated by our current results. Because our study and those on cocaine treatment have differed in the cognitive tests used and the treatments implemented, it is unclear whether cognition is more or less predictive of outcome for treatment of dependence on the two drugs. Additional studies with comparable methods are needed to further explore these issues, and we recommend that they also include urine-verified measures of initial use to ensure that unique predictive power is attained. Future studies may also benefit from using other neurocognitive measures than those currently implemented. For instance, measures tapping specific domains of function believed to be central to drug use behavior (e.g., inhibitory control, risk taking) may provide better predictive ability than the measures of reaction time and working memory currently implemented.
In the current results, the greatest accuracy in prediction was achieved for participants who were unsuccessful in treatment (dropped out or continued to use), rather than those who were successful. For example, in the CART analysis of those staying mostly abstinent (), frequent initial MA abuse strongly predicted continued use of MA during treatment (28 of 31 frequent users were unable to stay mostly abstinent). However, infrequent initial abuse of MA was not as accurate in predicting the converse—the ability of participants to remain abstinent (only 13 of 29 infrequent users were able to stay mostly abstinent). This difference in predicting treatment failure versus treatment success was replicated in other analyses. This suggests that, in general, it is easier to identify individuals who will do poorly in treatment, rather than those who will do well. Because the CART procedure automatically attempts to maximize the prediction of both treatment success and treatment failure, it can be concluded that none of the 100+ variables we analyzed were extremely accurate in predicting treatment success. Finding variables that accurately predict treatment success for MA-dependent individuals is a challenge that warrants additional investigation.