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• W4322754676 abstract "Objective: Due to the lack of randomization, pre-post routine outcome data precludes causal conclusions. We propose the synthetic waiting list (SWL) control group to overcome this limitation.Method: First, a step-by-step introduction illustrates this novel approach. Then, this approach is demonstrated using an empirical example with data from an outpatient cognitive-behavioral therapy (CBT) clinic (N = 139). We trained an ensemble machine learning model (Super Learner) on a data set of patients waiting for treatment (N = 311) to make counterfactual predictions of symptom change during this hypothetical period.Results: The between-group treatment effect was estimated to be d = 0.42. Of the patients who received CBT, 43.88% achieved reliable and clinically significant change, while this probability was estimated to be 14.54% in the SWL group. Counterfactual estimates suggest a clear net benefit of psychotherapy for 41% of patients. In 32%, the benefit was unclear, and 27% would have improved similarly without receiving CBT.Conclusions: The SWL is a viable new approach that provides between-group outcome estimates similar to those reported in the literature comparing psychotherapy with high-intensity control interventions. It holds the potential to mitigate common limitations of routine outcome data analysis." @default.
• W4322754676 created "2023-03-03" @default.
• W4322754676 creator A5012160982 @default.
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• W4322754676 date "2023-03-01" @default.
• W4322754676 modified "2023-10-18" @default.
• W4322754676 title "What if we wait? Using synthetic waiting lists to estimate treatment effects in routine outcome data" @default.
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• W4322754676 doi "https://doi.org/10.1080/10503307.2023.2182241" @default.
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• W4322754676 hasPublicationYear "2023" @default.
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