The personalized advantage index: Translating research on prediction into individualized treatment recommendations. A demonstration

Background: Advances in personalized medicine require the identification of variables that predict differential response to treatments as well as the development and refinement of methods to transform predictive information into actionable recommendations. Objective: To illustrate and test a new method for integrating predictive information to aid in treatment selection, using data from a randomized treatment comparison. Method: Data from a trial of antidepressant medications (N = 104) versus cognitive behavioral therapy (N = 50) for Major Depressive Disorder were used to produce predictions of post-treatment scores on the Hamilton Rating Scale for Depression (HRSD) in each of the two treatments for each of the 154 patients. The patient's own data were not used in the models that yielded these predictions. Five pre-randomization variables that predicted differential response (marital status, employment status, life events, comorbid personality disorder, and prior medication trials) were included in regression models, permitting the calculation of each patient's Personalized Advantage Index (PAI), in HRSD units. Results: For 60% of the sample a clinically meaningful advantage (PAI≥3) was predicted for one of the treatments, relative to the other. When these patients were divided into those randomly assigned to their "Optimal" treatment versus those assigned to their "Non-optimal" treatment, outcomes in the former group were superior (d = 0.58, 95% CI .17-1.01). Conclusions: This approach to treatment selection, implemented in the context of two equally effective treatments, yielded effects that, if obtained prospectively, would rival those routinely observed in comparisons of active versus control treatments. © 2014 DeRubeis et al

Prognosis moderates the engagement-outcome relationship in unguided cCBT for depression:a proof of concept for the prognosis moderation hypothesis

OBJECTIVE: Understanding how treatments work is a goal of psychotherapy research, however the strength of relationships between therapy processes and outcomes is inconsistent. DeRubeis, Cohen, et al. (2014) proposed that process-outcome relationships are moderated by patient characteristics. These "patient response patterns" (PRPs) indicate individuals' responsiveness to the active ingredients of treatment. Given the same quality of therapy, one individual may receive more benefit than another depending on their PRP. The "prognosis moderation hypothesis" states that PRPs can be defined by pretreatment prognostic indicators. Medium prognosis groups ("pliant-like") will have stronger process-outcome relationships than good ("easy-like") or poor ("challenging-like") groups.METHOD: N = 190 individuals received unguided computerized CBT. They were 58% women, aged 44.7 years. Engagement with the cCBT program was the process variable. PRPs were defined by predicted scores from a prognostic regression model. Outcomes were BDI scores at 3, 6, and 12 months. "Easy-like," "pliant-like" and "challenging-like" groups were created and the engagement-outcome relationship was assessed as a function of group.RESULTS: Engagement-outcome correlations by PRP were: easy-like, r = -.27 (p &lt; .05); pliant-like, r = -.36 (p &lt; .01); and challenging-like, r = .05 (p = .70). The pliant-like group was found to be the only moderator of the engagement-outcome relationship. Results were similar at 6 months but faded at 12.CONCLUSIONS: The engagement-outcome relationship varied as a function of prognosis, providing support for the prognosis moderation hypothesis. The "pliant-like" group appeared most sensitive to treatment procedures. Future research is needed to refine the methods for identifying PRPs. (PsycINFO Database Record</p

Dysfunctional attitudes or extreme response style as predictors of depressive relapse and recurrence after mobile cognitive therapy for recurrent depression

Background: According to previous research, dysfunctional attitudes and/or scoring extreme on the end-point anchors of questionnaires of dysfunctional thinking predict depressive relapse/recurrence. Evidence that these two methods represent a risk for depressive relapse/recurrence is however mixed, due to differential or poorly defined concepts. The current study aimed to test the two methods. Methods: Remitted recurrently depressed patients with low residual depressive symptoms (N = 264) were recruited as part of a randomized controlled trial of the effectiveness of mobile Cognitive Therapy for recurrent depression versus treatment as usual. In the current secondary analysis, Cox regression models were conducted to test dysfunctional attitudes and extreme responding variables (assessed on the Dysfunctional Attitudes Scale [DAS]) as predictors of depressive relapse/recurrence within two years after randomization. Results: Data from 255 participants were analyzed. Results showed that DAS total scores at baseline significantly predicted depressive relapse/recurrence (Hazard Ratio [HR] = 1.01, p =.042). An index that reflects endorsement of habitual relative to functional responses was a significant predictor of depressive relapse/recurrence (HR = 2.11, p =.029). Limitations: The current study employed a single measure to identify extreme responses and dysfunctional attitudes. Secondly, various statistical analyses were performed without correcting for multiple testing, which in turn increased the likelihood to finding significant results. Conclusions: Current study confirmed both methods: People who scored higher on the DAS or had relatively more habitual than functional responses on the extreme positive ends of the DAS had a decreased time to depressive relapse/recurrence

The treatment (Tx) main effect and interactions of Tx with the prescriptive variables.

<p>LOO  =  Leave One Out. CBT  =  Cognitive Behavioral Therapy. ADM  =  Antidepressant Medication. Tx  =  Treatment. HRSD  =  Hamilton Rating Scale for Depression.</p>a<p> =  See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083875#pone-0083875-t004" target="_blank">Table 4</a>.</p

Descriptive statistics for baseline variables.

<p>ADM  =  Antidepressant Medication. CBT  =  Cognitive Behavioral Therapy. HRSD  =  Hamilton Rating Scale for Depression.</p><p>^a =  Capped at 2; sample breakdown for number prior medications: 0 = 52% (55% in ADM, 46% in CBT), 1 = 24% (21% in ADM, 30% in CBT), 2 or more  = 24% (24% in ADM, 24% in CBT).</p

How the weights associated with prognostic and prescriptive variables combine with a patient's values to contribute to the calculation of the patient's Personalized Advantage Index.

<p>LOO  =  Leave One Out. ADM  =  Antidepressant Medication. HRSD  =  Hamilton Rating Scale for Depression.</p>a<p> =  Prognostic variable.</p>b<p> =  Prescriptive variable.</p>c<p> =  See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083875#pone-0083875-t004" target="_blank">Table 4</a>.</p

Frequency histogram showing Personalized Advantage Index (PAI) scores for all patients in the sample.

<p>Frequency histogram showing Personalized Advantage Index (PAI) scores for all patients in the sample.</p