What are the chances for personalised treatment with antidepressants? Detection of patient-by-treatment interaction with a variance ratio meta-analysis

Understanding variability: the role of meta-analysis of variance

Meta-analyses traditionally compare the difference in means between groups for one or more outcomes of interest. However, they do not compare the spread of data (variability), which could mean that important effects and/or subgroups are missed. To address this, methods to compare variability meta-analytically have recently been developed, making it timely to review them and consider their strengths, weaknesses, and implementation. Using published data from trials in major depression, we demonstrate how the spread of data can impact both overall effect size and the frequency of extreme observations within studies, with potentially important implications for conclusions of meta-analyses, such as the clinical significance of findings. We then describe two methods for assessing group differences in variability meta-analytically: the variance ratio (VR) and coefficient of variation ratio (CVR). We consider the reporting and interpretation of these measures and how they differ from the assessment of heterogeneity between studies. We propose general benchmarks as a guideline for interpreting VR and CVR effects as small, medium, or large. Finally, we discuss some important limitations and practical considerations of VR and CVR and consider the value of integrating variability measures into meta-analyses.

Can the potential benefit of individualizing treatment be assessed using trial summary statistics alone?

Individualizing treatment assignment can improve outcomes for diseases with patient-to-patient variability in comparative treatment effects. When a clinical trial demonstrates that some patients improve on treatment while others do not, it is tempting to assume that treatment effect heterogeneity exists. However, if outcome variability is mainly driven by factors other than variability in the treatment effect, investigating the extent to which covariate data can predict differential treatment response is a potential waste of resources. Motivated by recent meta-analyses assessing the potential of individualizing treatment for major depressive disorder using only summary statistics, we provide a method that uses summary statistics widely available in published clinical trial results to bound the benefit of optimally assigning treatment to each patient. We also offer alternate bounds for settings in which trial results are stratified by another covariate. Our upper bounds can be especially informative when they are small, as there is then little benefit to collecting additional covariate data. We demonstrate our approach using summary statistics from a depression treatment trial. Our methods are implemented in the rct2otrbounds R package.

Is personalization of psychological pain treatments necessary? Evidence from a Bayesian variance ratio meta-analysis

ABSTRACT

This is the first study to empirically determine the potential for data-driven personalization in the context of chronic primary pain (CPP). Effect sizes of psychological treatments for individuals with CPP are small to moderate on average. Aiming for better treatment outcomes for the individual patient, the call to personalize CPP treatment increased over time. However, empirical evidence that personalization of psychological treatments can optimize treatment outcomes in CPP is needed. This study seeks to estimate heterogeneity of treatment effect for cognitive behavioral therapy (CBT) as the psychological treatment approach for CPP with the greatest evidence base. For this purpose, a Bayesian variance ratio meta-regression is conducted using updated data from 2 recently published meta-analyses with randomized controlled trials comparing CBT delivered face-to-face to treatment-as-usual or waiting list controls. Heterogeneity in patients with CPP would be reflected by a larger overall variance in the post-treatment score compared with the control group. We found first evidence for an individual treatment effect in CBT compared with the control group. The estimate for the intercept was 0.06, indicating a 6% higher variance of end point values in the intervention groups. However, this result warrants careful consideration. Further research is needed to shed light on the heterogeneity of psychological treatment studies and thus to uncover the full potential of data-driven personalized psychotherapy for patients with CPP.A Bayesian variance ratio meta-regression indicates empirical evidence that data-driven personalized psychotherapy for patients with chronic primary pain could increase effects of cognitive behavioral therapy.

Lifting the veil off treatment effect heterogeneity

Clinicians often suspect that a treatment effect can vary across individuals. However, they usually lack "evidence-based" guidance regarding potential heterogeneity of treatment effects (HTE). Potentially actionable HTE is rarely discovered in clinical trials and is widely believed (or rationalized) by researchers to be rare. Conventional statistical methods to test for possible HTE are extremely conservative and tend to reinforce this belief. In truth, though, there is no realistic way to know whether a common, or average, effect estimated from a clinical trial is relevant for all, or even most, patients. This absence of evidence, misinterpreted as evidence of absence, may be resulting in sub-optimal treatment for many individuals. We first summarize the historical context in which current statistical methods for randomized controlled trials (RCTs) were developed, focusing on the conceptual and technical limitations that shaped, and restricted, these methods. In particular, we explain how the common-effect assumption came to be virtually unchallenged. Second, we propose a simple graphical method for exploratory data analysis that can provide useful visual evidence of possible HTE. The basic approach is to display the complete distribution of outcome data rather than relying uncritically on simple summary statistics. Modern graphical methods, unavailable when statistical methods were initially formulated a century ago, now render fine-grained interrogation of the data feasible. We propose comparing observed treatment-group data to "pseudo data" engineered to mimic that which would be expected under a particular HTE model, such as the common-effect model. A clear discrepancy between the distributions of the common-effect pseudo data and the actual treatment-effect data provides prima facie evidence of HTE to motivate additional confirmatory investigation. Artificial data are used to illustrate implications of ignoring heterogeneity in practice and how the graphical method can be useful.

Heterogeneity of Treatment Effects in Internet- and Mobile-Based Interventions for Depression: A Systematic Review and Meta-Analysis

Importance

While the effects of internet- and mobile-based interventions (IMIs) for depression have been extensively studied, no systematic evidence is available regarding the heterogeneity of treatment effects (HTEs), indicating to what extent patient-by-treatment interactions exist and personalized treatment models might be necessary.

Objective

To investigate the HTEs in IMIs for depression as well as their efficacy and effectiveness.

Data Sources

A systematic search in Embase, MEDLINE, Central, and PsycINFO for randomized clinical trials and supplementary reference searches was conducted on October 13, 2019, and updated March 25, 2022. The search string included various terms related to digital psychotherapy, depression, and randomized clinical trials.

Study Selection

Titles, abstracts, and full texts were reviewed by 2 independent researchers. Studies of all populations with at least 1 intervention group receiving an IMI for depression and at least 1 control group were eligible, if they assessed depression severity as a primary outcome and followed a randomized clinical trial (RCT) design.

Data Extraction and Synthesis

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines. Risk of bias was evaluated using the Cochrane Risk of Bias Tool. HTE was investigated using logarithmic variance ratios (lnVR) and effect sizes using Hedges g. Three-level bayesian meta-regressions were conducted.

Main Outcomes and Measures

Heterogeneity of treatment effects was the primary outcome of this study; magnitudes of treatment effect sizes were the secondary outcome. Depression severity was measured by different self-report and clinician-rated scales in the included RCTs.

Results

The systematic review of 102 trials included 19 758 participants (mean [SD] age, 39.9 [10.58] years) with moderate depression severity (mean [SD] in Patient Health Questionnaire-9 score, 12.81 [2.93]). No evidence for HTE in IMIs was found (lnVR = -0.02; 95% credible interval [CrI], -0.07 to 0.03). However, HTE was higher in more severe depression levels (β̂ = 0.04; 95% CrI, 0.01 to 0.07). The effect size of IMI was medium (g = -0.56; 95% CrI, -0.46 to -0.66). An interaction effect between guidance and baseline severity was found (β̂ = -0.24, 95% CrI, -0.03 to -0.46).

Conclusions and Relevance

In this systematic review and meta-analysis of RCTs, no evidence for increased patient-by-treatment interaction in IMIs among patients with subthreshold to mild depression was found. Guidance did not increase effect sizes in this subgroup. However, the association of baseline severity with HTE and its interaction with guidance indicates a more sensitive, guided, digital precision approach would benefit individuals with more severe symptoms. Future research in this population is needed to explore personalization strategies and fully exploit the potential of IMI.

The magnitude and variability of neurocognitive performance in first-episode psychosis: a systematic review and meta-analysis of longitudinal studies

Neurocognitive deficits are a core feature of psychotic disorders, but it is unclear whether they affect all individuals uniformly. The aim of this systematic review and meta-analysis was to synthesize the evidence on the magnitude, progression, and variability of neurocognitive functioning in individuals with first-episode psychosis (FEP). A multistep literature search was conducted in several databases up to November 1, 2022. Original studies reporting on neurocognitive functioning in FEP were included. The researchers extracted the data and clustered the neurocognitive tasks according to the seven Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) domains and six additional domains. Random-effect model meta-analyses, assessment of publication biases and study quality, and meta-regressions were conducted. The primary effect size reported was Hedges g of (1) neurocognitive functioning in individuals at FEP measuring differences with healthy control (HC) individuals or (2) evolution of neurocognitive impairment across study follow-up intervals. Of 30,384 studies screened, 54 were included, comprising 3,925 FEP individuals and 1,285 HC individuals. Variability analyses indicated greater variability in FEP compared to HC at baseline and follow-up. We found better neurocognitive performance in the HC group at baseline and follow-up but no differences in longitudinal neurocognitive changes between groups. Across the 13 domains, individuals with FEP showed improvement from baseline in all studied domains, except for visual memory. Metaregressions showed some differences in several of the studied domains. The findings suggest that individuals with FEP have marked cognitive impairment, but there is greater variability in cognitive functioning in patients than in HC. This suggests that subgroups of individuals suffer severe disease-related cognitive impairments, whereas others may be much less affected. While these impairments seem stable in the medium term, certain indicators may suggest potential further decline in the long term for a specific subgroup of individuals, although more research is needed to clarify this. Overall, this study highlights the need for tailored neurocognitive interventions for individuals with FEP based on their specific deficits and progression.

How amenable is type 2 diabetes treatment for precision diabetology? A meta-regression of glycaemic control data from 174 randomised trials

AIMS/HYPOTHESIS

There are two prerequisites for the precision medicine approach to be beneficial for treated individuals. First, there must be treatment heterogeneity; second, in the case of treatment heterogeneity, we need to detect clinical predictors to identify people who would benefit from one treatment more than from others. There is an established meta-regression approach to assess these two prerequisites that relies on measuring the variability of a clinical outcome after treatment in placebo-controlled randomised trials. Our aim was to apply this approach to the treatment of type 2 diabetes.

METHODS

We performed a meta-regression analysis using information from 174 placebo-controlled randomised trials with 178 placebo and 272 verum (i.e. active treatment) arms including 86,940 participants with respect to the variability of glycaemic control as assessed by HbA1c after treatment and its potential predictors.

RESULTS

The adjusted difference in log(SD) values between the verum and placebo arms was 0.037 (95% CI: 0.004, 0.069). That is, we found a small increase in the variability of HbA1c values after treatment in the verum arms. In addition, one potentially relevant predictor for explaining this increase, drug class, was observed, and GLP-1 receptor agonists yielded the largest differences in log(SD) values.

CONCLUSIONS/INTERPRETATION

The potential of the precision medicine approach in the treatment of type 2 diabetes is modest at best, at least with regard to an improvement in glycaemic control. Our finding of a larger variability after treatment with GLP-1 receptor agonists in individuals with poor glycaemic control should be replicated and/or validated with other clinical outcomes and with different study designs.

FUNDING

The research reported here received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

DATA AVAILABILITY

Two datasets (one for the log[SD] and one for the baseline-corrected log[SD]) to reproduce the analyses from this paper are available on https://zenodo.org/record/7956635 .

Evaluation of randomized controlled trials: a primer and tutorial for mental health researchers

BACKGROUND

Considered one of the highest levels of evidence, results of randomized controlled trials (RCTs) remain an essential building block in mental health research. They are frequently used to confirm that an intervention "works" and to guide treatment decisions. Given their importance in the field, it is concerning that the quality of many RCT evaluations in mental health research remains poor. Common errors range from inadequate missing data handling and inappropriate analyses (e.g., baseline randomization tests or analyses of within-group changes) to unduly interpretations of trial results and insufficient reporting. These deficiencies pose a threat to the robustness of mental health research and its impact on patient care. Many of these issues may be avoided in the future if mental health researchers are provided with a better understanding of what constitutes a high-quality RCT evaluation.

METHODS

In this primer article, we give an introduction to core concepts and caveats of clinical trial evaluations in mental health research. We also show how to implement current best practices using open-source statistical software.

RESULTS

Drawing on Rubin's potential outcome framework, we describe that RCTs put us in a privileged position to study causality by ensuring that the potential outcomes of the randomized groups become exchangeable. We discuss how missing data can threaten the validity of our results if dropouts systematically differ from non-dropouts, introduce trial estimands as a way to co-align analyses with the goals of the evaluation, and explain how to set up an appropriate analysis model to test the treatment effect at one or several assessment points. A novice-friendly tutorial is provided alongside this primer. It lays out concepts in greater detail and showcases how to implement techniques using the statistical software R, based on a real-world RCT dataset.

DISCUSSION

Many problems of RCTs already arise at the design stage, and we examine some avoidable and unavoidable "weak spots" of this design in mental health research. For instance, we discuss how lack of prospective registration can give way to issues like outcome switching and selective reporting, how allegiance biases can inflate effect estimates, review recommendations and challenges in blinding patients in mental health RCTs, and describe problems arising from underpowered trials. Lastly, we discuss why not all randomized trials necessarily have a limited external validity and examine how RCTs relate to ongoing efforts to personalize mental health care.

Review on Surface-Modified Electrodes for the Enhanced Electrochemical Detection of Selective Serotonin Reuptake Inhibitors (SSRIs)

Selective serotonin re-uptake inhibitors (SSRIs) are one of the most commonly prescribed classes of antidepressants used for the treatment of moderate to severe depressive disorder, personality disorders and various phobias. This class of antidepressants was created with improved margins of safety. However, genetic polymorphism may be responsible for the high variability in patients' responses to treatment, ranging from failure to delayed therapeutic responses to severe adverse effects of treatment. It is crucial that the appropriate amount of SSRI drugs is administered to ensure the optimum therapeutic efficacy and intervention to minimise severe and toxic effects in patients, which may be the result of accidental and deliberate cases of poisoning. Determining SSRI concentration in human fluids and the environment with high sensitivity, specificity and reproducibility, and at a low cost and real-time monitoring, is imperative. Electrochemical sensors with advanced functional materials have drawn the attention of researchers as a result of these advantages over conventional techniques. This review article aims to present functional materials such as polymers, carbon nanomaterials, metal nanomaterials as well as composites for surface modification of electrodes for sensitive detection and quantification of SSRIs, including fluoxetine, citalopram, paroxetine, fluvoxamine and sertraline. Sensor fabrication, sensor/analyte interactions, design rationale and properties of functional material and the electrocatalytic effect of the modified electrode on SSRI detection are discussed.

Is it worth it to personalize the treatment of PTSD? - A variance-ratio meta-analysis and estimation of treatment effect heterogeneity in RCTs of PTSD

Several evidence-based treatments for posttraumatic stress disorder (PTSD) are recommended by international guidelines (e.g., APA, NICE). While their average effects are in general high, non-response rates indicate differential treatment effects. Here, we used a large database of RCTs on psychotherapy for PTSD to determine a reliable estimate of this heterogeneity in treatment effects (HTE) by applying Bayesian variance ratio meta-analysis. In total, 66 studies with a total of 8803 patients were included in our study. HTE was found for all psychological treatments, with varying degrees of certainty, only slight differences between psychological treatments, and active control groups yielding a smaller variance ratio compared to waiting list control groups. Across all psychological treatment and control group types, the estimate for the intercept was 0.12, indicating a 12% higher variance of posttreatment values in the intervention groups after controlling for differences in treatment outcomes. This study is the first to determine the maximum increase in treatment effects of psychological treatments for PTSD by personalization. The results indicate that there is comparatively high heterogeneity in treatment effects across all psychological treatment and control groups, which in turn allow personalizing psychological treatments by using treatment selection approaches.

Response to acute monotherapy for major depressive disorder in randomized, placebo controlled trials submitted to the US Food and Drug Administration: individual participant data analysis

OBJECTIVES

To characterize individual participant level response distributions to acute monotherapy for major depressive disorder in randomized, placebo controlled trials submitted to the US Food and Drug Administration from 1979 to 2016.

DESIGN

Individual participant data analysis.

POPULATION

232 randomized, double blind, placebo controlled trials of drug monotherapy for major depressive disorder submitted by drug developers to the FDA between 1979 and 2016, comprising 73 388 adult and child participants meeting the inclusion criteria for efficacy studies on antidepressants.

MAIN OUTCOME MEASURES

Responses were converted to Hamilton Rating Scale for Depression (HAMD17) equivalent scores where other measures were used to assess efficacy. Multivariable analyses examined the effects of age, sex, baseline severity, and year of the study on improvements in depressive symptoms in the antidepressant and placebo groups. Response distributions were analyzed with finite mixture models.

RESULTS

The random effects mean difference between drug and placebo favored drug (1.75 points, 95% confidence interval 1.63 to 1.86). Differences between drug and placebo increased significantly (P<0.001) with greater baseline severity. After controlling for participant characteristics at baseline, no trends in treatment effect or placebo response over time were found. The best fitting model of response distributions was three normal distributions, with mean improvements from baseline to end of treatment of 16.0, 8.9, and 1.7 points. These distributions were designated Large, Non-specific, and Minimal responses, respectively. Participants who were treated with a drug were more likely to have a Large response (24.5% v 9.6%) and less likely to have a Minimal response (12.2.% v 21.5%).

CONCLUSIONS

The trimodal response distributions suggests that about 15% of participants have a substantial antidepressant effect beyond a placebo effect in clinical trials, highlighting the need for predictors of meaningful responses specific to drug treatment.

Transgenerational effects of obesogenic diets in rodents: A meta-analysis

Obesity is a major health condition that affects millions worldwide. There is an increased interest in understanding the adverse outcomes associated with obesogenic diets. A multitude of studies have investigated the transgenerational impacts of maternal and parental obesogenic diets on subsequent generations of offspring, but results have largely been mixed. We conducted a systematic review and meta-analysis on rodent studies to elucidate how obesogenic diets impact the mean and variance of grand-offspring traits. Our study focused on transgenerational effects (i.e., F2 and F3 generations) in one-off and multigenerational exposure studies. From 33 included articles, we obtained 407 effect sizes representing pairwise comparisons of control and treatment grand-offspring groups pertaining to measures of body weight, adiposity, glucose, insulin, leptin, and triglycerides. We found evidence that male and female grand-offspring descended from grandparents exposed to an obesogenic diet displayed phenotypes consistent with metabolic syndrome, especially in cases where the obesogenic diet was continued across generations. Further, we found stronger evidence for the effects of grand-maternal than grand-paternal exposure on grand-offspring traits. A high-fat diet in one-off exposure studies did not seem to impact phenotypic variation, whereas in multigenerational exposure studies it reduced variation in several traits.

Detecting Heterogeneity of Intervention Effects Using Analysis and Meta-analysis of Differences in Variance Between Trial Arms

BACKGROUND

Randomized controlled trials (RCTs) with continuous outcomes usually only examine mean differences in response between trial arms. If the intervention has heterogeneous effects, then outcome variances will also differ between arms. Power of an individual trial to assess heterogeneity is lower than the power to detect the same size of main effect.

METHODS

We describe several methods for assessing differences in variance in trial arms and apply them to a single trial with individual patient data and to meta-analyses using summary data. Where individual data are available, we use regression-based methods to examine the effects of covariates on variation. We present an additional method to meta-analyze differences in variances with summary data.

RESULTS

In the single trial, there was agreement between methods, and the difference in variance was largely due to differences in prevalence of depression at baseline. In two meta-analyses, most individual trials did not show strong evidence of a difference in variance between arms, with wide confidence intervals. However, both meta-analyses showed evidence of greater variance in the control arm, and in one example, this was perhaps because mean outcome in the control arm was higher.

CONCLUSIONS

Using meta-analysis, we overcame low power of individual trials to examine differences in variance using meta-analysis. Evidence of differences in variance should be followed up to identify potential effect modifiers and explore other possible causes such as varying compliance.

Examining Side Effect Variability of Antipsychotic Treatment in Schizophrenia Spectrum Disorders: A Meta-analysis of Variance

Side effects of antipsychotic drugs play a key role in nonadherence of treatment in schizophrenia spectrum disorders (SSD). While clinical observations suggest that side effect variability between patients may be considerable, statistical evidence is required to confirm this. Here, we hypothesized to find larger side effect variability under treatment compared with control. We included double-blind, placebo-controlled, randomized controlled trials (RCTs) of adults with a diagnosis of SSD treated with 1 out of 14 antipsychotics. Standard deviations of the pre-post treatment differences of weight gain, prolactin levels, and corrected QT (QTc) times were extracted. The outcome measure was the variability ratio of treatment to control for individual antipsychotic drugs and the overall variability ratio of treatment to control across RCTs. Individual variability ratios were weighted by the inverse-variance method and entered into a random-effects model. We included N = 16 578 patients for weight gain, N = 16 633 patients for prolactin levels, and N = 10 384 patients for QTc time. Variability ratios (VR) were significantly increased for weight gain (VR = 1.08; 95% CI: 1.02-1.14; P = .004) and prolactin levels (VR = 1.38; 95% CI: 1.17-1.62; P < .001) but did not reach significance for QTc time (VR = 1.05; 95% CI: 0.98-1.12; P = 0.135). We found marked differences between individual antipsychotics and increased variability in side effects in patients under treatment with antipsychotics suggesting that subgroups of patients or individual patients may benefit from treatment allocation through stratified or personalized medicine.

The Safety and Efficacy of Microbial Ecosystem Therapeutic-2 in People With Major Depression: Protocol for a Phase 2, Double-Blind, Placebo-Controlled Study

Background

The gut-brain axis is a bidirectional signaling pathway between the gastrointestinal tract and the brain; it is being studied because of its potential influence in mediating mood, anxiety, and other neuropsychiatric symptoms. Previous research examining the effects of gut microbiota on neuropsychiatric disorders suggests that gut repopulation treatments such as probiotics, microbe therapy, and fecal microbiota transplantation show promising results in treating symptoms of anxiety and depression. This study explores the use of an alternative gut repopulation treatment to fecal microbiota transplantation, known as Microbial Ecosystem Therapeutic (MET)-2, as an intervention against symptoms of depression. MET-2 is a daily, orally administered capsule containing 40 bacterial strains purified from a single healthy donor.

Objective

The primary aim of this study is to assess changes in mood in people with major depression that occur pre-, post-, and during the administration of MET-2. The secondary aims are to assess changes in anxiety symptoms, blood biomarker concentrations, and the level of repopulation of healthy gut bacteria as a response to treatment.

Methods

In this study, we will recruit 60 adults aged between 18 and 45 years old with major depression and randomly assign them to treatment or placebo groups. Patients in the treatment group will receive MET-2 once a day for 6 weeks, whereas patients in the placebo group will receive a matching placebo for 6 weeks. Participants will complete biweekly visits during the treatment period and a follow-up visit at 2 weeks post treatment. As a primary outcome measure, participants’ mood will be assessed using the Montgomery-Asberg Depression Rating Scale. Secondary outcome measures include changes in mood, anxiety, early stress, gastrointestinal symptoms, and tolerability of MET-2 treatment using a series of clinical scales and changes in blood markers, particularly immunoglobulins (Igs; IgA, IgG, and IgM) and inflammatory markers (C-reactive protein, tumor necrosis factor-α, transforming growth factor-β, interleukin-6, and interleukin-10). Changes in the relative abundance, diversity, and level of engraftment in fecal samples will be assessed using 16S rRNA sequencing. All data will be integrated to identify biomarkers that could indicate disease state or predict improvement in depressive symptoms in response to MET-2 treatment.

Results

Given the association between the gut microbiome and depression, we hypothesized that participants receiving MET-2 would experience greater improvement in depressive symptoms than those receiving placebo owing to the recolonization of the gut microbiome with healthy bacteria modulating the gut-brain axis connection.

Conclusions

This study is the first of its kind to evaluate the safety and efficacy of a microbial therapy such as MET-2 in comparison with placebo for major depressive disorder. We hope that this study will also reveal the potential capabilities of microbial therapies to treat other psychiatric illnesses and mood disorders.

Trial Registration

ClinicalTrials.gov NCT04602715; https://clinicaltrials.gov/ct2/show/NCT04602715

International Registered Report Identifier (IRRID)

DERR1-10.2196/31439

Beneficial and harmful effects of antidepressants versus placebo, 'active placebo', or no intervention for adults with major depressive disorder: a protocol for a systematic review of published and unpublished data with meta-analyses and trial sequential analyses

BACKGROUND

Major depressive disorder is one of the most common, burdensome, and costly psychiatric disorders worldwide. Antidepressants are frequently used to treat major depressive disorder. It has been shown repeatedly that antidepressants seem to reduce depressive symptoms with a statistically significant effect, but the clinical importance of the effect sizes seems questionable. Both beneficial and harmful effects of antidepressants have not previously been sufficiently assessed. The main objective of this review will be to evaluate the beneficial and harmful effects of antidepressants versus placebo, 'active placebo', or no intervention for adults with major depressive disorder.

METHODS/DESIGN

A systematic review with meta-analysis will be reported as recommended by Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), bias will be assessed with the Cochrane Risk of Bias tool-version 2 (ROB2), our eight-step procedure will be used to assess if the thresholds for clinical significance are crossed, Trial Sequential Analysis will be conducted to control for random errors, and the certainty of the evidence will be assessed with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. To identify relevant trials, we will search both for published and unpublished trials in major medical databases from their inception to the present. Clinical study reports will be obtained from regulatory authorities and pharmaceutical companies. Two review authors will independently screen the results of the literature searches, extract data, and perform risk of bias assessment. We will include any published or unpublished randomised clinical trial comparing one or more antidepressants with placebo, 'active placebo', or no intervention for adults with major depressive disorder. The following active agents will be included: agomelatine, amineptine, amitriptyline, bupropion, butriptyline, cianopramine, citalopram, clomipramine, dapoxetine, demexiptiline, desipramine, desvenlafaxine, dibenzepin, dosulepin, dothiepin, doxepin, duloxetine, escitalopram, fluoxetine, fluvoxamine, imipramine, iprindole, levomilnacipran, lofepramine, maprotiline, melitracen, metapramine, milnacipran, mirtazapine, nefazodone, nortriptyline, noxiptiline, opipramol, paroxetine, protriptyline, quinupramine, reboxetine, sertraline, trazodone, tianeptine, trimipramine, venlafaxine, vilazodone, and vortioxetine. Primary outcomes will be depressive symptoms, serious adverse events, and quality of life. Secondary outcomes will be suicide or suicide attempt, suicidal ideation, and non-serious adverse events.

DISCUSSION

As antidepressants are commonly used to treat major depressive disorder in adults, a systematic review evaluating their beneficial and harmful effects is urgently needed. This review will inform best practice in treatment and clinical research of this highly prevalent and burdensome disorder.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42020220279.

Meta-analysis of variation suggests that embracing variability improves both replicability and generalizability in preclinical research

The replicability of research results has been a cause of increasing concern to the scientific community. The long-held belief that experimental standardization begets replicability has also been recently challenged, with the observation that the reduction of variability within studies can lead to idiosyncratic, lab-specific results that cannot be replicated. An alternative approach is to, instead, deliberately introduce heterogeneity, known as "heterogenization" of experimental design. Here, we explore a novel perspective in the heterogenization program in a meta-analysis of variability in observed phenotypic outcomes in both control and experimental animal models of ischemic stroke. First, by quantifying interindividual variability across control groups, we illustrate that the amount of heterogeneity in disease state (infarct volume) differs according to methodological approach, for example, in disease induction methods and disease models. We argue that such methods may improve replicability by creating diverse and representative distribution of baseline disease state in the reference group, against which treatment efficacy is assessed. Second, we illustrate how meta-analysis can be used to simultaneously assess efficacy and stability (i.e., mean effect and among-individual variability). We identify treatments that have efficacy and are generalizable to the population level (i.e., low interindividual variability), as well as those where there is high interindividual variability in response; for these, latter treatments translation to a clinical setting may require nuance. We argue that by embracing rather than seeking to minimize variability in phenotypic outcomes, we can motivate the shift toward heterogenization and improve both the replicability and generalizability of preclinical research.

Individual Differences in Response to Antidepressants: A Meta-analysis of Placebo-Controlled Randomized Clinical Trials

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 Synthesis

Baseline 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 (bln σ̂) were derived for treatment groups (ie, antidepressant and placebo). A random-slope mixed-effects model was conducted to estimate the difference in bln σ̂ between 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.

Results

In the 91 eligible trials (18 965 participants), variability in response did not differ significantly between antidepressants and placebo (bln σ̂, 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 (bln σ̂, 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.

Treatment effect variability in brain stimulation across psychiatric disorders: A meta-analysis of variance

Noninvasive brain stimulation methods such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) are promising add-on treatments for a number of psychiatric conditions. Yet, some of the initial excitement is wearing off. Randomized controlled trials (RCT) have found inconsistent results. This inconsistency is suspected to be the consequence of variation in treatment effects and solvable by identifying responders in RCTs and individualizing treatment. However, is there enough evidence from RCTs that patients respond differently to treatment? This question can be addressed by comparing the variability in the active stimulation group with the variability in the sham group. We searched MEDLINE/PubMed and included all double-blinded, sham-controlled RCTs and crossover trials that used TMS or tDCS in adults with a unipolar or bipolar depression, bipolar disorder, schizophrenia spectrum disorder, or obsessive compulsive disorder. In accordance with the PRISMA guidelines to ensure data quality and validity, we extracted a measure of variability of the primary outcome. A total of 130 studies with 5748 patients were considered in the analysis. We calculated variance-weighted variability ratios for each comparison of active stimulation vs sham and entered them into a random-effects model. We hypothesized that treatment effect variability in TMS or tDCS would be reflected by increased variability after active compared with sham stimulation, or in other words, a variability ratio greater than one. Across diagnoses, we found only a minimal increase in variability after active stimulation compared with sham that did not reach statistical significance (variability ratio = 1.03; 95% CI, 0.97, 1.08, P = 0.358). In conclusion, this study found little evidence for treatment effect variability in brain stimulation, suggesting that the need for personalized or stratified medicine is still an open question.

[Network analysis by systemic text excavation of the concept of personalized psychiatry and precision]

OBJECTIVES

The current challenges of psychiatric nosology and semiology are part of an interdisciplinary and integrative framework. The paradigm of the personalized and precision psychiatry proposes to study this discipline according to new approaches and methodologies. Personalized and precision psychiatry therefore requires clarification of its concepts. To our knowledge, there is no systematic exploration of the literature on the application of the concepts of personalized and precision medicine in the field of psychiatry. This article proposes thus to explore the framework of personalized and precision medicine applied to psychiatry.

METHODS

We explored the framework of personalized and precision medicine applied to psychiatry by a textual network analysis. Firstly, we performed a systematic text-mining (Natural Language Processing) from an exhaustive review of the international literature with the terms "precision psychiatry" and "personalized psychiatry". Secondly, this analysis of textual data allowed us to build a textual network which made it possible to visualize the most proximal terms (the most frequently associated in the literature). Finally, we extracted from the network the main dimensions explored in the scientific literature, and we studied the relative importance of each term by analyzing the network centrality. In addition, a brief bibliometric analysis was conducted.

RESULTS

We show that personalized and precision psychiatry refers to six dimensions found in the textual network analysis which correspond to the scientific fields which study personalized and precision psychiatry: genetics, pharmacogenetics, artificial intelligence, therapeutic trials, biomarkers and staging. We explore how each dimension relates to the mechanization of psychiatric disorders. However, precision and personalized psychiatry, which tries to refine the levels of mechanistic explanations for psychiatry, suffers from a conceptual heterogeneity. Indeed, textual analysis also allows us to find terms referring to a set of heterogeneous concepts. Many methodological fields and epistemological concepts are invoked in this literature, without standardization.

CONCLUSIONS

The paradox of personalized and precision psychiatry is to associate a strong conceptual heterogeneity with a well-defined mechanistic component. Heterogeneity found in literature on personalized and precision psychiatry testifies to the lack of a pluralist and integrative theoretical framework. This framework could be based on a naturalizing but non-reducing formalism, aware of the societal challenges of the sciences and their implementation in the research and clinical systems of psychiatry.

On the treatment effect heterogeneity of antidepressants in major depression: A Bayesian meta-analysis and simulation study

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.

Withdrawal effects confounding in clinical trials: another sign of a needed paradigm shift in psychopharmacology research

Randomized controlled trials' ability to produce evidence useful for people to decide whether to take, continue taking, or stop taking psychotropic drugs has been intensely critiqued, along with the trials' commercial, ideological, and regulatory contexts. This article applies the critique to the topic of withdrawal effects confounding the outcomes of relapse-prevention trials where prescribed psychotropic drugs are discontinued. Until recently, the complexity and reach of withdrawal and post-withdrawal effects were neglected by mainstream psychiatry, but not by lay users of prescribed psychotropics. This article discusses withdrawal effects as part of the pharmacology of psychotropic drugs but shaped by psychosocial factors, and possibly shaping the presentation of psychological distress generally. It outlines biases and misconceptions in assumptions, design, and reporting of general efficacy trials and findings from a recent review of 80 discontinuation trials. In theory, relapse-prevention trials are tautological and exaggerate efficacy. In publications, they pay little attention to the central feature of their design, favor abrupt or rapid discontinuations, do not attend to environmental factors, and provide insufficient data to allow re-analyses. Thus, relapse-prevention RCTs likely confound the detection of their main outcome of interest: "relapse." Using slower tapers, active placebo controls, and specific covariates in analyses would reduce the risk of withdrawal confounding, and better reporting would reduce the opaqueness of trials. The crisis in psychopharmacology is fueled partly by the disconnect between claims of therapeutic efficacy from so-called best-evidence methods despite unchanging population-level indicators of psychiatric sickness. Only by "stacking the deck" against trial sponsors' hoped-for outcomes can psychopharmacology trials regain scientific credibility.

Individual response to antidepressants for depression in adults-a meta-analysis and simulation study

BACKGROUND

The observation that some patients appear to respond better to antidepressants for depression than others encourages the assumption that the effect of antidepressants differs between individuals and that treatment can be personalized.

OBJECTIVE

To compare the outcome variance in patients receiving antidepressants with the outcome variance in patients receiving placebo in randomized controlled trials (RCTs) of adults with major depressive disorder (MDD) and to illustrate, using simulated data, components of variation of RCTs.

METHODS

From a dataset comprising 522 RCTs of antidepressants for adult MDD, we selected the placebo-controlled RCTs reporting outcomes on the 17 or 21 item Hamilton Depression Rating Scale or the Montgomery-Asberg Depression Rating Scale and extracted the means and SDs of raw endpoint scores or baseline to endpoint changes scores on eligible depression symptom rating scales. We conducted inverse variance random-effects meta-analysis with the variability ratio (VR), the ratio between the outcome variance in the group of patients receiving antidepressants and the outcome variance in the group receiving placebo, as the primary outcome. An increased variance in the antidepressant group would indicate individual differences in response to antidepressants.

RESULTS

We analysed 222 RCTs that investigated 19 different antidepressants compared with placebo in 345 comparisons, comprising a total of 61144 adults with an MDD diagnosis. Across all comparisons, the VR for raw endpoint scores was 0.98 (95% CI 0.96 to 1.00, I2 = 0%) and 1.00 (95% CI 0.99 to 1.02, I2 = 0%) for baseline-to-endpoint change scores.

CONCLUSION

Based on these data, we cannot reject the null hypothesis of equal variances in the antidepressant group and the placebo group. Given that RCTs cannot provide direct evidence for individual treatment effects, it may be most reasonable to assume that the average effect of antidepressants applies also to the individual patient.