English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

What if we wait? Using synthetic waiting lists to estimate treatment effects in routine outcome data