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Background The average treatment effect of antidepressants in major depression was found to be about 2 points on the 17-item Hamilton Depression Rating Scale, which lies below clinical relevance. Here, we searched for evidence of a relevant treatment effect heterogeneity that could justify the usage of antidepressants despite their low average treatment effect. Methods Bayesian meta-analysis of 169 randomized, controlled trials including 58,687 patients. We considered the effect sizes log variability ratio (lnVR) and log coefficient of variation ratio (lnCVR) to analyze the difference in variability of active and placebo response. We used Bayesian random-effects meta-analyses (REMA) for lnVR and lnCVR and fitted a random-effects meta-regression (REMR) model to estimate the treatment effect variability between antidepressants and placebo. Results The variability ratio was found to be very close to 1 in the best fitting models (REMR: 95% highest density interval (HDI) [0.98, 1.02], REMA: 95% HDI [1.00, 1.02]). The between-study standard deviation τ under the REMA with respect to lnVR was found to be low (95% HDI [0.00, 0.02]). Simulations showed that a large treatment effect heterogeneity is only compatible with the data if a strong correlation between placebo response and individual treatment effect is assumed. Conclusions The published data from RCTs on antidepressants for the treatment of major depression is compatible with a near-constant treatment effect. Although it is impossible to rule out a substantial treatment effect heterogeneity, its existence seems rather unlikely. Since the average treatment effect of antidepressants falls short of clinical relevance, the current prescribing practice should be re-evaluated.

Lithium has been the treatment of choice for patients with bipolar disorder (BD) for nearly 70 years. It is recommended by all relevant guidelines as a first-line treatment for maintenance therapy. In this review, we outline the current state of evidence for lithium in the treatment of BD over the lifespan. First, we summarize the evidence on efficacy in general, from relapse prevention to acute anti-manic treatment and its role in treating mood episodes with mixed features and bipolar depression. As patients are often treated for many years and different aspects have to be considered in different phases of life, we discuss the particularities of lithium in the treatment of paediatric BD, in older aged individuals and in pregnant women. Lastly, we discuss the evidence on lithium's proposed suicide-preventive effects, the dangers of rapid discontinuation and lithium's adverse effects, particularly with regard to long-term treatment.

Practitioners and researchers alike assume that there is individual variability in the effects of treatments for mental disorders. However, for psychotherapy, up to now this assumption has never been empirically tested. Using a large database of randomized-controlled trials on psychotherapy of depression in adults (306 trials including a total of 51,853 patients), we performed a Bayesian variance ratio metaregression. For the entire sample, we found a 9% higher variance in the intervention groups compared with the control groups. Depending on the depression scale used, this corresponds to a standard deviation of the individual treatment effect of 3 to 4 points. Subgroup analyses revealed that the effect variability of some types of therapy is larger than others. Our results are the first to indicate that patients do benefit differently from psychotherapy. We conclude that there is a sound basis for the paradigm of personalized psychotherapy, which brings about implications for both research and clinical practice. Public Health Significance StatementIn recent years, studies with high methodological quality have pointed out that the efficacy of psychotherapy in the treatment of depression is less satisfactory than previous research suggested. To optimize psychotherapy for non-responders, the paradigm of personalized therapy is coming into the research focus. In this study, we show for the first time that the effects of psychological interventions vary more than those of control conditions. This shows that differential response to treatments is inherent to intervention effects. Thus, it could indeed be beneficial to better tailor psychotherapy to individual patients. In practice, session-by-session outcome monitoring should be used to detect non-responding cases in ongoing treatments. Statistical methods guiding the selection of treatment components capitalize on the heterogeneity of treatment effects and are thus likely to improve outcomes. These findings pave the way for broad research and implementation of approaches that support personalization (e.g., monitoring and feedback systems) as well as a new shaping of training beyond the traditional schools of thought in psychotherapy.

Background: The average treatment effect of antidepressants in major depression was found to be about 2 points on the 17-item Hamilton Depression Rating Scale, which lies below clinical relevance. Here, we searched for evidence of a relevant treatment effect heterogeneity that could justify the usage of antidepressants despite their low average treatment effect. Methods: Bayesian meta-analysis of 169 randomized, controlled trials including 58,687 patients. We considered the effect sizes log variability ratio (lnVR) and log coefficient of variation ratio (lnCVR) to analyze the difference in variability of active and placebo response. We used Bayesian random-effects meta-analyses (REMA) for lnVR and lnCVR and fitted a random-effects meta-regression (REMR) model to estimate the treatment effect variability between antidepressants and placebo. Results: The variability ratio was found to be very close to 1 in the best fitting models (REMR: 95% HPD [0.98, 1.02], REMA: 95% HPD [1.00, 1.02]). The between-study variance τ2 under the REMA was found to be low (95% HPD [0.00, 0.00]). Simulations showed that a large treatment effect heterogeneity is only compatible with the data if a strong correlation between placebo response and individual treatment effect is assumed. Conclusions: The published data from RCTs on antidepressants for the treatment of major depression is compatible with a near-constant treatment effect. Although it is impossible to rule out a substantial treatment effect heterogeneity, its existence seems rather unlikely. Since the average treatment effect of antidepressants falls short of clinical relevance, the current prescribing practice should be re-evaluated.

BackgroundPsychotherapy is an evidence-based treatment for depression, but its average effect is modest. Thus, identifying subgroups that respond especially well to psychotherapy is an important goal. This would allow maximizing the efficacy of interventions. However, the extent of treatment effect heterogeneity (TEH) has not yet been systematically investigated. A reliable, evidence-based estimate of this heterogeneity would allow a more accurate assessment of the potential effects of enhancement by personalization.MethodsUsing a large database of randomized-controlled trials on psychotherapy for depression in adults (k = 306), we performed a Bayesian variance ratio meta-regression. Based on the results, we determined the theoretically maximum possible extent of variability of individual outcomes. Exploratory subgroup analyses were conducted for different types of psychotherapy. We determined the extent of expected TEH given the evidence by employing an analytical approach.OutcomesWe found for the entire sample a 9% higher variance in the intervention groups compared to the control groups, indicating TEH in psychotherapy for depression. Depending on the depression scale used, this corresponds to a standard deviation of the individual treatment effect of 3-4 points. Subgroup analyses revealed that due to a large number of studies, the evidence for TEH is strongest in cognitive-behavioural therapy, while the largest TEH was observed in behavioural activation therapy.InterpretationOur results show that the treatment effect of psychotherapy for depression varies. This is a clear indication that patients benefit differently from different interventions. Clinical or statistical methods that select the optimal intervention for each patient are likely to increase the success of treatment.

IMPORTANCE Antidepressants are commonly used to treat major depressive disorder (MDD). Antidepressant outcomes can vary based on individual differences; however, it is unclear whether specific factors determine this variability or whether it is at random. OBJECTIVE To investigate the assumption of systematic variability in symptomatic response to antidepressants and to assess whether variability is associated with MDD severity, antidepressant class, or study publication year.DATA SOURCES Data used were updated from a network meta-analysis of treatment with licensed antidepressants in adults with MDD. The Cochrane Central Register of Controlled Trials, CINAHL, Embase, LILACS database, MEDLINE, MEDLINE In-Process, and PsycInfo were searched from inception to March 21, 2019. Additional sources were international trial registries and sponsors, drug companies and regulatory agencies' websites, and reference lists of published articles. Data were analyzed between June 8, 2020, and June 13, 2020.STUDY SELECTION Analysis was restricted to double-blind, randomized placebo-controlled trials with depression scores available at the study's end point. DATA EXTRACTION AND SYNTHESISBaseline means, number of participants, end point means and SDs of total depression scores, antidepressant type, and publication year were extracted.MAIN OUTCOMES AND MEASURES Log SDs (b ln σ ˆ) were derived for treatment groups (ie, antidepressant and placebo). A random-slope mixed-effects model was conducted to estimate the difference in b ln σ ˆbetween treatment groups while controlling for end point mean. Secondary models determined whether differences in variability between groups were associated with baseline MDD severity; antidepressant class (selective serotonin reuptake inhibitors and other related drugs; serotonin and norepinephrine reuptake inhibitors; norepinephrine-dopamine reuptake inhibitors; noradrenergic agents; or other antidepressants); and publication year. RESULTSIn the 91 eligible trials (18 965 participants), variability in response did not differ significantly between antidepressants and placebo (b ln σ ˆ, 1.02; 95% CI, 0.99-1.05; P = .19). This finding is consistent with a range of treatment effect SDs (up to 16.10), depending on the association between the antidepressant and placebo effects. Variability was not associated with baseline MDD severity or publication year. Responses to noradrenergic agents were 11% more variable than responses to selective serotonin reuptake inhibitors (b ln σ ˆ, 1.11; 95% CI, 1.01-1.21; P = .02).CONCLUSIONS AND RELEVANCE Although this study cannot rule out the possibility of treatment effect heterogeneity, it does not provide empirical support for personalizing antidepressant treatment based solely on total depression scores. Future studies should explore whether individual symptom scores or biomarkers are associated with variability in response to antidepressants.

Fig. 1. Crude incidence rates (IR) of suicide, suicide attempts and violent crime in patients with affective disorder and conscripts in relation to time since initiation of antidepressant medication (AD).