Data from: Predicting optimal outcomes in cognitive therapy or interpersonal psychotherapy for depressed individuals using the Personalized Advantage Index approach

Introduction: Although psychotherapies for depression produce equivalent
outcomes, individual patients respond differently to different therapies.
Predictors of outcome have been identified in the context of randomized
trials, but this information has not been used to predict which treatment
works best for the depressed individual. In this paper, we aim to
replicate a recently developed treatment selection method, using data from
an RCT comparing the effects of cognitive therapy (CT) and interpersonal
psychotherapy (IPT). Methods: 134 depressed patients completed the pre-
and post-treatment BDI-II assessment. First, we identified baseline
predictors and moderators. Secondly, individual treatment recommendations
were generated by combining the identified predictors and moderators in an
algorithm that produces the Personalized Advantage Index (PAI), a measure
of the predicted advantage in one therapy compared to the other, using
standard regression analyses and the leave-one-out cross-validation
approach. Results: We found five predictors (gender, employment status,
anxiety, personality disorder and quality of life) and six moderators
(somatic complaints, cognitive problems, paranoid symptoms, interpersonal
self-sacrificing, attributional style and number of life events) of
treatment outcome. The mean average PAI value was 8.9 BDI points, and 63%
of the sample was predicted to have a clinically meaningful advantage in
one of the therapies. Those who were randomized to their predicted optimal
treatment (either CT or IPT) had an observed mean end-BDI of 11.8, while
those who received their predicted non-optimal treatment had an end-BDI of
17.8 (effect size for the difference = 0.51). Discussion: Depressed
patients who were randomized to their predicted optimal treatment fared
much better than those randomized to their predicted non-optimal
treatment. The PAI provides a great opportunity for formal decision-making
to improve individual patient outcomes in depression. Although the utility
of the PAI approach will need to be evaluated in prospective research,
this study promotes the development of a treatment selection approach that
can be used in regular mental health care, advancing the goals of
personalized medicine.