Qualitative treatment-subgroup interactions in the antidepressant treatment of major depression: Application of QUINT to individual participant data from seven placebo-controlled randomized controlled trials

Maruo, Kazushi; Furukawa, Toshi A.; Noma, Hisashi; Ikeda, Kazutaka; Yamawaki, Shigeto

doi:10.1016/j.pmip.2019.100054

Cited by 1 publication

(2 citation statements)

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“…QUINT is appropriate for (multi)categorical and continuous predictors and continuous outcomes regardless of distribution. Previous research using QUINTs have examined which subgroups of people with HIV and depressive symptoms benefited from a cognitive-behavioral intervention (van Luenen et al, 2020), subgroups for whom different health interventions work (Formanoy et al, 2016), and differences in treatment effect between antidepressants and placebo in the treatment of major depression (Maruo et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Glinternet achieves this by also including the main effects as possible predictors, and because of the regularization, it is possible for both the main effects and the sets of coefficients (lower order and interactions) to be included in the model simultaneously. This method, unlike QUINTs, is still prioritizing accurate prediction of the outcome but has shown promise as a method for identifying groups of treatment responders (Formanoy et al, 2016; Lipkovich et al, 2017; Maruo et al, 2020; van Luenen et al, 2020) and performed better than stepwise selection methods for this purpose (Wester et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

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Identifying Treatment Responders to Varenicline for Alcohol Use Disorder Using Two Machine-Learning Approaches

Grodin

Montoya

Cruz

et al. 2023

Clinical Psychological Science

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Varenicline has shown promise for treating alcohol use disorder (AUD); however, not everyone will respond to varenicline. Machine-learning methods are well suited to identify treatment responders. In the present study, we examined data from the National Institute on Alcohol Abuse and Alcoholism Clinical Intervention Group multisite clinical trial of varenicline using two machine-learning methods. Baseline characteristics taken from a randomized clinical trial of varenicline were examined as potential moderators of treatment response using qualitative interaction trees ( N = 199) and group least absolute shrinkage and selection operator interaction nets ( N = 200). Results align with prior research, highlighting smoking status, AUD severity, medication adherence, and drinking goal as predictors of treatment response. Novel findings included the interaction between age and cardiovascular health in predicting clinical response and stronger medication effects among individuals with lower craving. With increased integration of machine-learning methods, studies that effectively integrate methods and medication development have high potential to inform clinical practice.

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Section: Discussionmentioning

confidence: 99%