Which intervention works for whom: Identifying pre-treatment characteristics that predict who will benefit from a specific alcohol text message intervention from a randomized trial

Tammy Chung , Brian Suffoletto , Trishnee Bhurosy
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Abstract

Introduction

Effective interventions show heterogeneity in treatment response. Addressing this heterogeneity involves identifying which intervention works best for whom. One method to address this heterogeneity identifies treatment-subgroup interactions to determine which of two interventions has greater effect for certain individuals based on their profile of pre-treatment characteristics. This secondary analysis of a randomized clinical trial (RCT) to address binge drinking examined whether two of the RCT's interventions, GOAL and COMBO, which produced similar reductions in drinking outcomes, might have involved treatment-subgroup interactions. Identifying treatment-subgroup interactions can inform efficient patient-treatment matching that optimizes individual outcomes.

Methods

These secondary analyses included young adults (n = 344; 68.6 % female, ages 18–25) randomized to GOAL or COMBO 12-week alcohol text message interventions and who completed 3-month follow-up (end of intervention). GOAL provided weekly support for drinking limit goals. COMBO included all GOAL features, in addition to pre-event feedback on drinking plans and post-event feedback on alcohol consumption. QUINT, a tree-based algorithm, aimed to identify treatment-subgroup interactions using 21 pre-treatment (baseline) characteristics (e.g., demographics, perceived risk of binge-drinking related harm, perceived number of peers drinking to intoxication) that predicted the primary outcome of binge drinking at follow-up.

Results

The algorithm used five pre-treatment characteristics (sex, race, perceived risk of binge drinking-related harm, perceived number of peers drinking to intoxication, and any cannabis use in the past 3 months) to identify 7 treatment-subgroup interactions. COMBO had greater effectiveness than GOAL, for example, for females who reported lower risk of binge-drinking related harm and no cannabis use in the past 3 months, whereas GOAL had greater effectiveness for females who reported higher risk of binge-drinking related harm and more peers who drank to intoxication. In comparison, GOAL had greater effectiveness than COMBO among White males, whereas males of other racial backgrounds benefitted more from COMBO than GOAL.

Conclusions

The identified treatment-subgroup interactions involving GOAL and COMBO indicated which intervention had greater effectiveness for which subgroups of individuals based on pre-treatment characteristics. These findings can help efficiently match individuals to effective interventions, bringing the field closer to personalized, precision care.
Clinical trials registration number: NCT02918565.
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哪种干预措施对谁有效?从随机试验中找出可预测谁将从特定酒精短信干预中受益的治疗前特征。
介绍:有效的干预措施在治疗反应方面具有异质性。要解决这种异质性问题,就必须确定哪种干预措施对谁最有效。解决这种异质性的一种方法是确定治疗与亚组之间的相互作用,从而根据某些个体治疗前的特征,确定两种干预措施中哪一种对他们的效果更好。本研究对一项针对酗酒的随机临床试验(RCT)进行了二次分析,研究了RCT中的两项干预措施--GOAL和COMBO--是否会产生治疗-亚组交互作用,这两项干预措施对酗酒结果的减少效果相似。识别治疗与亚组之间的相互作用可以为有效的患者治疗匹配提供信息,从而优化个体结果:这些二次分析包括随机接受 GOAL 或 COMBO 12 周酒精短信干预并完成 3 个月随访(干预结束)的年轻人(n = 344;68.6% 为女性,年龄在 18-25 岁之间)。GOAL 每周为实现饮酒限制目标提供支持。COMBO 包括 GOAL 的所有功能,以及活动前对饮酒计划的反馈和活动后对饮酒量的反馈。QUINT是一种基于树状结构的算法,旨在利用治疗前(基线)的21个特征(如人口统计学特征、对暴饮暴食相关伤害风险的感知、对酗酒至醉酒同伴数量的感知)来识别治疗与亚组之间的相互作用,从而预测随访时暴饮暴食的主要结果:该算法利用治疗前的五个特征(性别、种族、酗酒相关危害的感知风险、酗酒致醉同伴的感知人数以及过去 3 个月中吸食大麻的情况)确定了 7 个治疗与亚组之间的交互作用。例如,对于报告暴饮相关危害风险较低且在过去 3 个月中未吸食大麻的女性而言,COMBO 比 GOAL 更有效;而对于报告暴饮相关危害风险较高且有更多同伴饮酒至醉的女性而言,GOAL 更有效。相比之下,在白人男性中,GOAL 比 COMBO 更有效,而在其他种族背景的男性中,COMBO 比 GOAL 更有效:结论:GOAL 和 COMBO 的治疗与亚组之间的相互作用表明,根据治疗前的特征,哪种干预措施对哪些亚组人群更有效。这些发现有助于有效地将个体与有效的干预措施相匹配,使该领域更接近个性化的精准医疗:临床试验注册号:NCT02918565。
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Journal of substance use and addiction treatment
Journal of substance use and addiction treatment Biological Psychiatry, Neuroscience (General), Psychiatry and Mental Health, Psychology (General)
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