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

Assessing effectiveness and mechanisms in treatment research: A critical look at assay sensitivity and the use of mediation analysis

The author examines two questions: (1) Should “assay sensitivity” be used to determine the appropriateness of a randomized control trial (RCT) for meta-analysis, and (2) How well are published mediation analyses conducted? The first question is relevant to assessing the effectiveness of psychiatric treatments. The second question, though not focusing explicitly on the psychiatric treatment literature, nonetheless has implications for researchers who wish to explore potential mechanisms of treatment. To answer the first question, expected drug vs. placebo effect sizes from (hypothetical) trials with assay sensitivity were calculated over a range of distributional assumptions for combinations of high quality and flawed studies. The use of assay sensitivity to judge RCT quality can lead to substantial bias; this method should not be used. To answer the second question, the author evaluated 50 published mediation analyses with regard to their presentation of three issues (a) testing of the indirect effect, (b) reporting coefficients, and (c) addressing temporal issues. About half of the articles failed to meet minimum criteria for each of these three issues, respectively. Researchers heeding the call of Kraemer et al. (2002, p. 877) to “routinely include and report [mediation] analyses” in RCTs of psychiatric treatment should attend to the recommendations made in this paper. If researchers fail to avoid the problems discussed in this paper, they may end up inadvertently exposing patients to less than ideal treatments, either by not knowing which treatment works best, or by redesigning treatments in a less than ideal manner. If the conclusions in this paper are taken seriously, treatment research will have a better chance of correctly determining the relative effectiveness of treatments, and correctly identifying treatment mechanisms

Examining the Protective Effects of Mindfulness Training on Working Memory Capacity and Affective Experience

We investigated the impact of mindfulness training (MT) on working memory capacity (WMC) and affective experience. WMC is used in managing cognitive demands and regulating emotions. Yet, persistent and intensive demands, such as those experienced during high-stress intervals, may deplete WMC and lead to cognitive failures and emotional disturbances. We hypothesized that MT may mitigate these deleterious effects by bolstering WMC. We recruited 2 military cohorts during the high-stress predeployment interval and provided MT to 1 (MT, n � 31) but not the other group (military control group, MC, n � 17). The MT group attended an 8-week MT course and logged the amount of out-of-class time spent practicing formal MT exercises. The operation span task was used to index WMC at 2 testing sessions before and after the MT course. Although WMC remained stable over time in civilians (n � 12), it degraded in the MC group. In the MT group, WMC decreased over time in those with low MT practice time, but increased in those with high practice time. Higher MT practice time also corresponded to lower levels of negative affect and higher levels of positive affect (indexed by the Positive and Negative Affect Schedule). The relationship between practice time and negative, but not positive, affect was mediated by WMC, indicating that MT-related improvements in WMC may support some but not all of MT’s salutary effects. Nonetheless, these findings suggest that sufficient MT practice may protect against functional impairments associated with high-stress contexts

Sequence of improvement in depressive symptoms across cognitive therapy and pharmacotherapy

Background The authors examined the patterns of improvement in cognitive and vegetative symptoms of major depression in individuals treated with cognitive therapy (CT) or pharmacotherapy (PT).Method Outpatients diagnosed with major depressive disorder (n = 180) were randomized to receive either CT or PT. Cognitive and vegetative symptoms of major depression were measured by the Beck Depression Inventory-II at baseline and regularly throughout 16 weeks of treatment.Results Multivariate hierarchical linear modeling demonstrated the same patterns of change over time for cognitive and vegetative symptoms within CT and within PT.Limitations Self-report measures may not be sufficiently specific to capture subtle differences in improvements between vegetative and cognitive symptoms.Conclusions These results are consistent with Beck's [Beck, A.T., 1984, November. Cognition and theory [Letter to the editor]. Arch. Gen. Psychiatry 41, 1112-1114.] hypothesis that CT and PT have a similar site of action, which when targeted, results in changes in both cognitive and vegetative features

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

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