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Clinical psychology and psychiatry have many 'holy grails' or research findings that are widely sought after but remain elusive. The use of machine learning (ML) models for treatment selection is one of these holy grails. Ahuvia et al. (Journal of Child Psychology and Psychiatry, 2023) recently analyzed a large trial (n = 996) of two distinct single‐session interventions (SSIs) for internalizing distress and found little evidence that an ML model could predict differential treatment response. I discuss potential avenues for advancing SSI research. One avenue is the dissemination and implementation of SSIs, including how they interact with other treatments in routine care. Quantifying and critically questioning the promises of holy grails like ML models is sorely needed. Using simulation modeling to evaluate the relative merits of using ML models for treatment selection or using SSIs versus other treatment strategies may be another path forward. [ABSTRACT FROM AUTHOR]

Cognitive-behavioral therapies (CBTs) are the most widely studied form of psychotherapy for disorders like depression and anxiety. Nonetheless, there is heterogeneity in response to CBTs vs. other treatments. Researchers have become increasingly interested in using pre-treatment individual differences (i.e., moderators) to match patients to the most effective treatments for them. Several methods to combine multiple variables to create precision treatment rules (PTRs) that identify subgroups have been proposed. We review the rationale behind multivariable PTRs as well as the findings of studies that have used different PTRs. We identify conceptual and methodological issues in the literature. Multivariable treatment assignment is a promising avenue of research. Nonetheless, effect sizes appear to be small and most of the samples that have been used to study these questions have been grossly underpowered to detect small effects. We recommend researchers explore multivariable treatment selection strategies, particularly those resembling risk stratification, in heterogeneous samples of patients undergoing low-intensity CBTs vs. realistic minimal controls. [ABSTRACT FROM AUTHOR]

Introduction: Researchers have proposed that predicting who is a likely placebo responder may help guide treatment allocations to treatment regimens that differ in intensity. Methods: We used data from the Treatment of Adolescent Depression Study (TADS) in which adolescents (n = 439) were randomized 1:1:1:1 to placebo, cognitive-behavioral therapy (CBT), medications (MEDs), or their combination (COMB). We developed a prognostic index (PI) in the placebo group to predict self-reported (RADS) and observer-rated (CDRS) depression outcomes using elastic net regularization. We explored whether the PIs moderated outcomes in the treatment conditions. Results: PI-CDRS was predicted by multiple variables but it did not moderate outcomes. PI-RADS was predicted by baseline severity, age, sleep problems, expectations, maternal depression, and the action stage of change. It moderated outcomes such that there were treatment differences for less placebo-responsive patients. For participants prone to placebo response, type of treatment had no statistically significant impact on outcomes. Baseline depression severity accounted for this effect: treatment differences were small and non-significant for patients with milder depression but larger in more severely depressed patients. Discussion: Future work should investigate whether multiple variable explain outcomes beyond severity as well as complex interactions between severity and other variables. [ABSTRACT FROM AUTHOR]

Objective: Optimizing treatment selection may improve treatment outcomes in depression. A promising approach is the Personalized Advantage Index (PAI), which predicts the optimal treatment for a given individual. To determine the generalizability of the PAI, models needs to be externally validated, which has rarely been done. Method: PAI models were developed within each of two independent trials, with substantial between-study differences, that both compared CBT and IPT for depression (STEPd: n = 151 and FreqMech: n = 200). Subsequently, both PAI models were tested in the other dataset. Results: In the STEPd study, post-treatment depression was significantly different between individuals assigned to their PAI-indicated treatment versus those assigned to their non-indicated treatment (d =.57). In the FreqMech study, post-treatment depression was not significantly different between patients receiving their indicated treatment versus those receiving their non-indicated treatment (d =.20). Cross-trial predictions indicated that post-treatment depression was not significantly different between those receiving their indicated treatment and those receiving their non-indicated treatment (d =.16 and d =.27). Sensitivity analyses indicated that cross-trial prediction based on only overlapping variables didn't improve the results. Conclusion: External validation of the PAI has modest results and emphasizes between-study differences and many other challenges. [ABSTRACT FROM AUTHOR]

Identifying differences in the clinical response to specific interventions is an important challenge in the field of Clinical Psychology. This is especially true in the treatment of depression where many treatments appear to have comparable outcomes. In a controlled trial, we compared a positive psychology group intervention, the Integrative Positive Psychological Intervention for Depression (IPPI-D; n = 62) to a cognitive-behavioral therapy group intervention (CBT; n = 66) for depression. No statistically or clinically-significant differences between the treatments were found, but a slight advantage was observed, on average, for IPPI-D. The aim of the present study was to identify and combine moderators of the differential efficacy of these two psychological interventions for clinical depression. For this purpose, a secondary analysis using the Personalized Advantage Index (PAI) was performed to identify the intervention predicted to produce the better outcome for each patient. Six of the 21 potential moderators were found to predict differential efficacy between the treatments. IPPI-D was predicted to be the optimal treatment for 73% of the sample. Baseline features that characterized these individuals were: mental and physical comorbidity, prior antidepressant medication, higher levels of negative thoughts, and higher personal growth. The 27% who were predicted to achieve better outcomes in CBT than in IPPI-D tended to have these baseline features: no comorbidities, no prior antidepressant medication, lower levels of negative thoughts, and lower personal growth. [ABSTRACT FROM AUTHOR]

Identifying active ingredients of psychological interventions is a major goal of psychotherapy researchers that is often justified by the promise that it will lead to improved patient outcomes. Much of this "active ingredients" research is conducted within randomized controlled trials (RCTs) with patient populations, putting it in Phase T2 of the clinical-translational spectrum. I argue that RCTs in patient populations are very "messy laboratories" in which to conduct active ingredient work and that T0 and T1 research provide more controlled contexts. However, I call attention to the long road from identifying active ingredients of CBTs, whether in T0, T1, or T2 research, to improving outcomes. Dissemination and implementation research (T3 and T4 approaches) may be conceptually closer to improving outcomes. Given how common and disabling mental health symptoms are, I argue that if researchers want to improve patient outcomes, these research programs must receive more attention including work on the uptake of psychological interventions as well as work on optimal ordering of existing interventions. • Research on active ingredients is criticized. • Identifying active ingredients of CBTs may be slow to improve treatment outcomes. • Other research programmes have more immediate potential to improve outcomes. • Researchers should study the entire clinical-translational spectrum to advance CBTs. [ABSTRACT FROM AUTHOR]

One of the main debates in the study of psychotherapy is whether specific techniques are best indicated for different problems or whether “common factors” better account for the efficacy of psychotherapy. Evidence for the superiority of specific techniques is mixed and limited to a handful of diagnoses. By contrast, evidence for the importance of common factors is riddled with methodological weaknesses and may be of limited clinical utility. The stagnation in this debate may reflect that the research methods heretofore employed have reached a plateau in their ability to advance knowledge regarding psychotherapy processes. The articles of the special issue move beyond simple bivariate relationship and attempt to model the real-world complexity involved in the process of psychotherapy. It is argued that these types of investigations, which model the interactions of patient characteristics as well as multiple specific and “common factors,” are the best way to advance the state of knowledge regarding psychotherapy processes. [ABSTRACT FROM AUTHOR]

Background: Anxiety symptoms are common in bipolar disorder. We explored the effect of anxiety on the outcome of acute and continuation pharmacotherapy of bipolar II depression.Methods: Data were derived from a randomized double-blind 12-week acute (N = 129) and 6-month continuation (N = 55) comparison of venlafaxine versus lithium monotherapy in bipolar II depression in adults. We distinguished between the items of the Hamilton Rating Scale for Depression (HRSD) that capture depression vs. anxiety (i.e., psychomotor agitation, psychic anxiety, somatic anxiety, hypochondriasis, and obsessive-compulsive concerns) and examined the effect of treatment on depression and anxiety. Additionally, we explored whether baseline anxiety or depression predicted changes over time in depression and anxiety ratings or moderated treatment outcomes. We also explored whether residual depressive and anxious symptoms predicted relapse during continuation therapy.Results: Venlafaxine was superior to lithium in reducing both depression and anxiety, though its effects on anxiety were more modest than those on depression. Baseline anxiety predicted change over time in anxiety, but not depression. By contrast, baseline depression did not predict change over time in depression or anxiety. Residual anxiety, specifically uncontrollable worry, was a stronger predictor of relapse than residual depression.Conclusion: Successful treatment of symptoms of anxiety in bipolar depression may protect against depressive relapse. [ABSTRACT FROM AUTHOR]

Prior studies have suggested that the association between the alliance and depression improvement varies as a function of prior history of depression. We sought to replicate these findings and extend them to short-term psychodynamic supportive psychotherapy (SPSP) in a sample of patients who were randomized to one of these treatments and were administered the Helping Alliance Questionnaire (N=282) at Week 5 of treatment. Overall, the alliance was a predictor of symptom change (d=0.33). In SPSP, the alliance was a modest but robust predictor of change, irrespective of prior episodes (d=0.25-0.33). By contrast, in CBT, the effects of the alliance on symptom change were large for patients with 0 prior episodes (d=0.86), moderate for those with 1 prior episode (d=0.49), and small for those with 2+ prior episodes (d=0.12). These findings suggest a complex interaction between patient features and common vs. specific therapy processes. In CBT, the alliance relates to change for patients with less recurrent depression whereas other CBT-specific processes may account for change for patients with more recurrent depression. [ABSTRACT FROM AUTHOR]

Background: Prognostic indices (PIs) combining variables to predict future depression risk may help guide the selection of treatments that differ in intensity. We develop a PI and show its promise in guiding treatment decisions between treatment as usual (TAU), treatment starting with a low-intensity treatment (brief therapy (BT)), or treatment starting with a high-intensity treatment intervention (cognitive-behavioral therapy (CBT)).Methods: We utilized data from depressed patients (N=622) who participated in a randomized comparison of TAU, BT, and CBT in which no statistically significant differences in the primary outcomes emerged between the three treatments. We developed a PI by predicting depression risk at follow-up using a LASSO-style bootstrap variable selection procedure. We then examined between-treatment differences in outcome as a function of the PI.Results: Unemployment, depression severity, hostility, sleep problems, and lower positive emotionality at baseline predicted a lower likelihood of recovery across treatments. The PI incorporating these variables produced a fair classification accuracy (c=0.73). Among patients with a high PI (75% percent of the sample), recovery rates were high and did not differ between treatments (79-86%). Among the patients with the poorest prognosis, recovery rates were substantially higher in the CBT condition (60%) than in TAU (39%) or BT (44%).Limitations: No information on additional treatment sought. Prospective tests needed.Conclusion: Replicable PIs may aid treatment selection and help streamline stepped models of care. Differences between treatments for depression that differ in intensity may only emerge for patients with the poorest prognosis. [ABSTRACT FROM AUTHOR]

Background: Controversy exists over antidepressant use in bipolar II depression.Aims: To compare the safety and effectiveness of antidepressantv.mood stabiliser monotherapy for bipolar type II major depressive episodes.Method: Randomised, double-blind, parallel-group, 12-week comparison of venlafaxine (n= 65)v.lithium (n= 64) monotherapy in adult out-patients (trial registration numberNCT00602537).Results: Primary outcome - venlafaxine produced a greater response rate (67.7%)v lithium (34.4%,P<0.001). Secondary outcomes - venlafaxine produced a greater remission rate (58.5%v 28.1%,P<0.001); greater decline in depression symptom scores over time (β = -5.32, s.e. = 1.16, χ(2)= 21.19,P<0.001); greater reduction in global severity scores over time (β = -1.05, s.e. = 0.22, w(2)= 22.33,P<0.001); and greater improvement in global change scores (β = -1.31, s.e. = 0.32, χ(2)= 16.95,P<0.001) relative to lithium. No statistically significant or clinically meaningful differences in hypomanic symptoms were observed between treatments.Conclusions: These findings suggest that short-term venlafaxine monotherapy may provide effective antidepressant treatment for bipolar II depression without a statistically significant increase in hypomanic symptoms relative to lithium. [ABSTRACT FROM AUTHOR]

The authors comment on the article by Wampold and colleagues in the 2017 issue if the journal which criticizes three meta-analysis suggesting superior effects of cognitive behavior therapy (CBT) over other psychotherapy strategies for general psychopathology and social phobia. They explore arguments by Wampold and colleagues on the effectiveness of CBT than other psychotherapies and concerns over the use of the term CBT in medical literature.

Studies suggest that depression severity and duration interact to predict outcomes in depression treatment. To our knowledge, no study has explored this question in a sample with a placebo control, two therapies, and their combination nor with adolescents. We used data from the Treatment of Adolescent Depression Study (N = 439), in which adolescent were randomized to placebo (PBO), cognitive-behavioral therapy (CBT), antidepressants medications (MEDs), or their combination (COMB). We explore the interaction between depression severity, chronicity, and treatments (vs. placebo) in predicting outcomes. There was interaction between severity and chronicity when comparing COMB and CBT with PBO, but not MEDs. In non-chronic depression, the effects of CBT were inversely related to severity to the point that CBT appeared iatrogenic with more severe depression. In chronic depression, the effects of CBT did not vary by severity, but the relative effects of COMB grew, being smallest in milder, more dysthymic-like depression, and largest in chronic-severe depression. These findings support calls to classify depression by severity and chronicity as well efforts to risk stratify patients to different intensity of care according to these variables. • Depression severity and chronicity may interact to predict treatment outcomes. • We explored this question in the Treatment for Adolescent Depression Study (TADS). • In non-chronic depression, greater severity predicted lower response to CBT (vs. placebo). • In chronic depression, greater severity predicted better response to combination treatment. • Adding medications to CBT may potentiate its efficacy in high risk depression subgroups. [ABSTRACT FROM AUTHOR]

Increased quality of life (QoL) is rated by patients as a primary factor in determining recovery from psychopathology. Cognitive behavioral therapies (CBTs) are the most well-researched psychotherapies for internalizing disorders and appear effective at reducing symptoms even when delivered by trainees. Existing research suggests that the effects of CBTs on QoL are more modest than their effects on symptoms. However, little is known about the effects of trainee-delivered CBT on life satisfaction, a subjective measure of QoL. We analyzed data from 93 clients treated by students (n = 23) in a graduate-level training clinic using an intent-to-treat approach, completers case analyses, and random forest imputation. Across methods of handling missing data, improvements in anxiety, depression, and CBT skills were more marked than improvements in QoL. Exploratory analyses suggested baseline life satisfaction was the strongest predictor of end-of-treatment life satisfaction. Future research should explore alternatives to "standard" CBT for clients with low life satisfaction. [ABSTRACT FROM AUTHOR]