Network support for drinking: an application of multiple groups growth mixture modeling to examine client-treatment matching

饮酒网络支持:多组增长混合模型在检验客户-治疗匹配中的应用

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Abstract

OBJECTIVE: The current study re-examined the Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity) hypothesis that individuals with high network support for drinking would have the best treatment outcomes if they were assigned to twelve-step facilitation (TSF). METHOD: Drinking consequences, as measured by the Drinking Inventory of Consequences, was the primary outcome measure. Growth mixture models with multiple groups were used to estimate the drinking consequence trajectories of 952 outpatients during the 12 months following treatment for each of the three Project MATCH treatment conditions. Growth factors within latent trajectory classes were regressed on network support for drinking to assess whether treatment condition moderated the relationship between network support for drinking and drinking consequences over time. RESULTS: Three latent classes were identified, representing low (n = 154, 16.2%), medium (n = 400, 42%), and high (n = 398, 41.8%) levels of drinking consequences. Classes did not differ across treatment groups. Greater network support for drinking predicted more drinking consequences over time but only for clients assigned to cognitive-behavioral therapy and motivational enhancement therapy, not TSF. CONCLUSIONS: This study provides further support for one of the original Project MATCH matching hypotheses: Clients with social networks supportive of drinking had better outcomes immediately after treatment if they were assigned to TSF. Because the original Project MATCH studies found this matching effect only at the 3-year follow-up, these results add validity to the network support for drinking matching effect. The study also provides additional evidence that accounting for heterogeneity in alcohol treatment outcomes is important for accurately estimating treatment effectiveness.

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