Selective publication of antidepressant trials and its influence on apparent efficacy: Updated comparisons and meta-analyses of newer versus older trials

Unpublished trials of alprazolam XR and their influence on its apparent efficacy for panic disorder

OBJECTIVE

To test for publication bias with alprazolam, the most widely prescribed benzodiazepine, by comparing its efficacy for panic disorder using trial results from (1) the published literature and (2) the US Food and Drug Administration (FDA).

METHODS

From FDA reviews, we included data from all phase 2/3 efficacy trials of alprazolam extended-release (Xanax XR) for the treatment of panic disorder. A search for matching publications was performed using PubMed and Google Scholar. Publication bias was examined by comparing: (1) overall trial results (positive or not) according to the FDA v. corresponding publications; (2) effect size (Hedges's g) based on FDA data v. published data.

RESULTS

The FDA review showed that five trials were conducted, only one of which (20%) was positive. Of the four not-positive trials, two were published conveying a positive outcome; the other two were not published. Thus, according to the published literature, three trials were conducted and all (100%) were positive. Alprazolam's effect size calculated using FDA data was 0.33 (CI95% 0.07-0.60) v. 0.47 (CI95% 0.30-0.65) using published data, an increase of 0.14, or 42%.

CONCLUSIONS

Publication bias substantially inflates the apparent efficacy of alprazolam XR.

Barriers and best practices to improving clinical trials transparency at UK public research institutions: A qualitative interview study

OBJECTIVES

Since 2017, the UK government has made concerted efforts to ensure the dissemination of clinical trials conducted at public research institutions. This study aims to understand how stakeholders within these institutions responded to these pressures and modified internal policies and processes while identifying best practices and barriers to improved transparency practice.

METHODS

Research governance and trial management staff from UK public research institutions (i.e., Universities and NHS Trusts) in England, Scotland and Wales participated in semi-structured interviews. Interviews were analysed using thematic analysis, aided by the framework method.

RESULTS

Between November 2020 and July 2021, 14 individual participants were recruited from 11 different institutions. They worked in research governance, administration, and management. Almost universally, new policies and procedures have been established to ensure investigators are aware of, and supported in, fulfilling their transparency commitments, however challenges remain. Trials of medicinal products, as the most closely regulated research, consequently received the most attention. National professional networks aid in sharing knowledge and best practice within this community.

CONCLUSIONS

Investment in the institutional governance of transparency is essential to achieving optimal transparency practices. Universities and hospitals share responsibility for ensuring research is performed and reported to regulatory standards. Facing political pressure, public research institutions in the UK have made efforts to improve their transparency practice which can provide key insights for similar efforts elsewhere.

Towards transparency: adoption of WHO best practices in clinical trial registration and reporting among top medical research funders in the USA

OBJECTIVE

To assess to what extent the clinical trial policies of the largest public and philanthropic funders of clinical research in the United States meet WHO best practices in trial registration and reporting.

METHODS

Public and philanthropic funders of clinical trials in the USA with >US$50 million annual spend were selected. The funders were assessed using an 11-item scoring tool based on WHO Joint Statement benchmarks. These 11 items fell into 4 categories, namely: trial registration, academic publication, monitoring and sanctions. An additional item captured whether and how funders referred to Consolidated Standards of Reporting Trials (CONSORT) within their trial policies. Each funder was independently assessed by two or three researchers. Funders were contacted to flag possible errors and omissions. Ambiguous or difficult-to-score items were settled by an independent adjudicator.

RESULTS

Fourteen funders were assessed. Our cross-sectional study found that, on average, funders have only implemented 4.1/11 (37%) of WHO best practices in clinical trial transparency. The most frequently adopted requirement was open access publishing (14/14 funders). The least frequently adopted were (1) requiring trial ID to appear in all publications (2/14 funders, 14%) and (2) making compliance reports public (2/14 funders, 14%). Public funders, on average, adopted more policy elements (5.2/11 items, 47%) than philanthropic funders (2.8/11 items, 25%). Only one funder's policy documents mentioned the CONSORT statement.

CONCLUSIONS

There is a significant variation between the number of best practice policy items adopted by medical research funders in the USA. Many funders fell significantly short of WHO Joint Statement benchmarks. Each funder could benefit from policy revision and strengthening.

Influence of study characteristics, methodological rigour and publication bias on efficacy of pharmacotherapy in obsessive-compulsive disorder: a systematic review and meta-analysis of randomised, placebo-controlled trials

QUESTION

We examined the effect of study characteristics, risk of bias and publication bias on the efficacy of pharmacotherapy in randomised controlled trials (RCTs) for obsessive-compulsive disorder (OCD).

STUDY SELECTION AND ANALYSIS

We conducted a systematic search of double-blinded, placebo-controlled, short-term RCTs with selective serotonergic reuptake inhibitors (SSRIs) or clomipramine. We performed a random-effect meta-analysis using change in the Yale-Brown Obsessive-Compulsive Scale (YBOCS) as the primary outcome. We performed meta-regression for risk of bias, intervention, sponsor status, number of trial arms, use of placebo run-in, dosing, publication year, age, severity, illness duration and gender distribution. Furthermore, we analysed publication bias using a Bayesian selection model.

FINDINGS

We screened 3729 articles and included 21 studies, with 4102 participants. Meta-analysis showed an effect size of -0.59 (Hedges' G, 95% CI -0.73 to -0.46), equalling a 4.2-point reduction in the YBOCS compared with placebo. The most recent trial was performed in 2007 and most trials were at risk of bias. We found an indication for publication bias, and subsequent correction for this bias resulted in a depleted effect size. In our meta-regression, we found that high risk of bias was associated with a larger effect size. Clomipramine was more effective than SSRIs, even after correcting for risk of bias. After correction for multiple testing, other selected predictors were non-significant.

CONCLUSIONS

Our findings reveal superiority of clomipramine over SSRIs, even after adjusting for risk of bias. Effect sizes may be attenuated when considering publication bias and methodological rigour, emphasising the importance of robust studies to guide clinical utility of OCD pharmacotherapy.

PROSPERO REGISTRATION NUMBER

CRD42023394924.

REPRESENTATION AND EXTRAPOLATION: EVIDENCE FROM CLINICAL TRIALS. THE QUARTERLY JOURNAL OF ECONOMICS 2024;139:575-635. [PMID: 38859982 PMCID: PMC11164133 DOI: 10.1093/qje/qjad036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

This article examines the consequences and causes of low enrollment of Black patients in clinical trials. We develop a simple model of similarity-based extrapolation that predicts that evidence is more relevant for decision-making by physicians and patients when it is more representative of the group being treated. This generates the key result that the perceived benefit of a medicine for a group depends not only on the average benefit from a trial but also on the share of patients from that group who were enrolled in the trial. In survey experiments, we find that physicians who care for Black patients are more willing to prescribe drugs tested in representative samples, an effect substantial enough to close observed gaps in the prescribing rates of new medicines. Black patients update more on drug efficacy when the sample that the drug is tested on is more representative, reducing Black-white patient gaps in beliefs about whether the drug will work as described. Despite these benefits of representative data, our framework and evidence suggest that those who have benefited more from past medical breakthroughs are less costly to enroll in the present, leading to persistence in who is represented in the evidence base.

Non-linear processing and reinforcement learning to predict rTMS treatment response in depression

BACKGROUND

Forecasting the efficacy of repetitive transcranial magnetic stimulation (rTMS) therapy can lead to substantial time and cost savings by preventing futile treatments. To achieve this objective, we've formulated a machine learning approach aimed at categorizing patients with major depressive disorder (MDD) into two groups: individuals who respond (R) positively to rTMS treatment and those who do not respond (NR).

METHODS

Preceding the commencement of treatment, we obtained resting-state EEG data from 106 patients diagnosed with MDD, employing 32 electrodes for data collection. These patients then underwent a 7-week course of rTMS therapy, and 54 of them exhibited positive responses to the treatment. Employing Independent Component Analysis (ICA) on the EEG data, we successfully pinpointed relevant brain sources that could potentially serve as markers of neural activity within the dorsolateral prefrontal cortex (DLPFC). These identified sources were further scrutinized to estimate the sources of activity within the sensor domain. Then, we integrated supplementary physiological data and implemented specific criteria to yield more realistic estimations when compared to conventional EEG analysis. In the end, we selected components corresponding to the DLPFC region within the sensor domain. Features were derived from the time-series data of these relevant independent components. To identify the most significant features, we used Reinforcement Learning (RL). In categorizing patients into two groups - R and NR to rTMS treatment - we utilized three distinct classification algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). We assessed the performance of these classifiers through a ten-fold cross-validation method. Additionally, we conducted a statistical test to evaluate the discriminative capacity of these features between responders and non-responders, opening the door for further exploration in this field.

RESULTS

We identified EEG features that can anticipate the response to rTMS treatment. The most robust discriminators included EEG beta power, the sum of bispectrum diagonal elements in the delta and beta frequency bands. When these features were combined into a single vector, the classification of responders and non-responders achieved impressive performance, with an accuracy of 95.28 %, specificity at 94.23 %, sensitivity reaching 96.29 %, and precision standing at 94.54 %, all achieved using SVM.

CONCLUSIONS

The results of this study suggest that the proposed approach, utilizing power, non-linear, and bispectral features extracted from relevant independent component time-series, has the capability to forecast the treatment outcome of rTMS for MDD patients based solely on a single pre-treatment EEG recording session. The achieved findings demonstrate the superior performance of our method compared to previous techniques.

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.

Are European clinical trial funders policies on clinical trial registration and reporting improving? A cross-sectional study

Objectives

Assess the extent to which the clinical trial registration and reporting policies of 25 of the world's largest public and philanthropic medical research funders meet best practice benchmarks as stipulated by the 2017 WHO Joint Statement, and document changes in the policies and monitoring systems of 19 European funders over the past year.

Design Setting Participants

Cross-sectional study, based on assessments of each funder's publicly available documentation plus validation of results by funders. Our cohort includes 25 of the largest medical research funders in Europe, Oceania, South Asia, and Canada.

Interventions

Scoring all 25 funders using an 11-item assessment tool based on WHO best practice benchmarks, grouped into three primary categories: trial registries, academic publication, and monitoring, plus validation of results by funders.

Main outcome measures

How many of the 11 WHO best practice items each of the 25 funders has put into place, and changes in the performance of 19 previously assessed funders over the preceding year.

Results

The 25 funders we assessed had put into place an average of 5/11 (49%) WHO best practices. Only 6/25 funders (24%) took the PI's past reporting record into account during grant application reviews. Funders' performance varied widely from 0/11 to 11/11 WHO best practices adopted. Of the 19 funders for which 2021(2) baseline data was available, 10/19 (53%) had strengthened their policies over the preceding year.

Conclusions

Most medical research funders need to do more to curb research waste and publication bias by strengthening their clinical trial policies.

Machine learning approaches and non-linear processing of extracted components in frontal region to predict rTMS treatment response in major depressive disorder

Predicting the therapeutic result of repetitive transcranial magnetic stimulation (rTMS) treatment could save time and costs as ineffective treatment can be avoided. To this end, we presented a machine-learning-based strategy for classifying patients with major depression disorder (MDD) into responders (R) and nonresponders (NR) to rTMS treatment. Resting state EEG data were recorded using 32 electrodes from 88 MDD patients before treatment. Then, patients underwent 7 weeks of rTMS, and 46 of them responded to treatment. By applying Independent Component Analysis (ICA) on EEG, we identified the relevant brain sources as possible indicators of neural activity in the dorsolateral prefrontal cortex (DLPFC). This was served through estimating the generators of activity in the sensor domain. Subsequently, we added physiological information and placed certain terms and conditions to offer a far more realistic estimation than the classic EEG. Ultimately, those components mapped in accordance with the region of the DLPFC in the sensor domain were chosen. Features extracted from the relevant ICs time series included permutation entropy (PE), fractal dimension (FD), Lempel-Ziv Complexity (LZC), power spectral density, correlation dimension (CD), features based on bispectrum, frontal and prefrontal cordance, and a combination of them. The most relevant features were selected by a Genetic Algorithm (GA). For classifying two groups of R and NR, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) were applied to predict rTMS treatment response. To evaluate the performance of classifiers, a 10-fold cross-validation method was employed. A statistical test was used to assess the capability of features in differentiating R and NR for further research. EEG characteristics that can predict rTMS treatment response were discovered. The strongest discriminative indicators were EEG beta power, the sum of bispectrum diagonal elements in delta and beta bands, and CD. The Combined feature vector classified R and NR with a high performance of 94.31% accuracy, 92.85% specificity, 95.65% sensitivity, and 92.85% precision using SVM. This result indicates that our proposed method with power and nonlinear and bispectral features from relevant ICs time-series can predict the treatment outcome of rTMS for MDD patients only by one session pretreatment EEG recording. The obtained results show that the proposed method outperforms previous methods.

Pharmacogenetics of anxiety and depression in Alzheimer's disease

Anxiety and depression coexist with cognitive impairment in Alzheimer's disease along with other concomitant disorders (>60%), which require multipurpose treatments. Polypharmaceutical regimens cause drug-drug interactions and adverse drug reactions, potentially avoidable in number and severity with the implementation of pharmacogenetic procedures. The accumulation of defective variants (>30 genes per patient in more than 50% of cases) in pharmagenes (pathogenic, mechanistic, metabolic, transporter, pleiotropic) influences the therapeutic response to antidementia, antidepressant and anxiolytic drugs in polyvalent regimens. APOE, CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4, CYP3A5, CYP4F2, COMT, MAOB, CHAT, GSTP1, NAT2, SLC30A8, SLCO1B1, ADRA2A, ADRB2, BCHE, GABRA1, HMGCR, HTR2C, IFNL3, NBEA, UGT1A1, ABCB1, ABCC2, ABCG2, SLC6A2, SLC6A3, SLC6A4, MTHFR and OPRM1 variants affect anxiety and depression in Alzheimer's disease.

Is 'another' psychiatry possible?

In this paper, we examine a number of approaches that propose new models for psychiatric theory and practices: in the way that they incorporate 'social' dimensions, in the way they involve 'communities' in treatment, in the ways that they engage mental health service users, and in the ways that they try to shift the power relations within the psychiatric encounter. We examine the extent to which 'alternatives' - including 'Postpsychiatry', 'Open Dialogue', the 'Power, Threat and Meaning Framework' and Service User Involvement in Research - really do depart from mainstream models in terms of theory, practice and empirical research and identify some shortcomings in each. We propose an approach which seeks more firmly to ground mental distress within the lifeworld of those who experience it, with a particular focus on the biopsychosocial niches within which we make our lives, and the impact of systematic disadvantage, structural violence and other toxic exposures within the spaces and places that constitute and constrain many everyday lives. Further, we argue that a truly alternative psychiatry requires psychiatric professionals to go beyond simply listening to the voices of service users: to overcome epistemic injustice requires professionals to recognise that those who have experience of mental health services have their own expertise in accounting for their distress and in evaluating alternative forms of treatment. Finally we suggest that, if 'another psychiatry' is possible, this requires a radical reimagination of the role and responsibilities of the medically trained psychiatrist within and outside the clinical encounter.

Perspectives on the success rate of current antidepressant pharmacotherapy

INTRODUCTION

There has been growing debate about the effectiveness of traditional antidepressants for the treatment of depression, and whether the clinical trials literature overstates the value of existing agents. Antidepressant efficacy is limited by suboptimal remission rates, lack of robust efficacy across diverse depressed subgroups, slow onset, and challenges managing tolerability. Clinicians can better navigate uncertainties in this area by recognizing patient-specific clinical and prognostic factors that influence the likelihood of antidepressant drug response.

AREAS COVERED

The author summarizes pertinent literature regarding drug-placebo differences in antidepressant outcome as well as patient-specific factors that influence antidepressant drug responsivity across subtypes of depressive disorders.

EXPERT OPINION

Standardized effect sizes for most monoaminergic antidepressants are relatively modest. At least one-third of treatment response derives from nonspecific (yet substantial) placebo effects, limiting the ability to compare antidepressant medication effects to that of "no treatment." Patients with high baseline depressive symptom severity are less likely to respond to placebo but may be more responsive to antidepressant pharmacotherapy than is the case in mild forms of depression. Patient satisfaction with antidepressant response must take into consideration not only efficacy for reducing symptoms but also drug tolerability/acceptability and tangible improvement in functional outcome and quality of life.

Measurement challenges and causes of incomplete results reporting of biomedical animal studies: Results from an interview study

Background

Existing evidence indicates that a significant amount of biomedical research involving animals remains unpublished. At the same time, we lack standards for measuring the extent of results reporting in animal research. Publication rates may vary significantly depending on the level of measurement such as an entire animal study, individual experiments within a study, or the number of animals used.

Methods

Drawing on semi-structured interviews with 18 experts and qualitative content analysis, we investigated challenges and opportunities for the measurement of incomplete reporting of biomedical animal research with specific reference to the German situation. We further investigate causes of incomplete reporting.

Results

The in-depth expert interviews revealed several reasons for why incomplete reporting in animal research is difficult to measure at all levels under the current circumstances. While precise quantification based on regulatory approval documentation is feasible at the level of entire studies, measuring incomplete reporting at the more individual experiment and animal levels presents formidable challenges. Expert-interviews further identified six drivers of incomplete reporting of results in animal research. Four of these are well documented in other fields of research: a lack of incentives to report non-positive results, pressures to ‘deliver’ positive results, perceptions that some data do not add value, and commercial pressures. The fifth driver, reputational concerns, appears to be far more salient in animal research than in human clinical trials. The final driver, socio-political pressures, may be unique to the field.

Discussion

Stakeholders in animal research should collaborate to develop a clear conceptualisation of complete reporting in animal research, facilitate valid measurements of the phenomenon, and develop incentives and rewards to overcome the causes for incomplete reporting.

Adoption of World Health Organization Best Practices in Clinical Trial Transparency Among European Medical Research Funder Policies

IMPORTANCE

Research funders can reduce research waste and publication bias by requiring their grantees to register and report clinical trials.

OBJECTIVE

To determine the extent to which 21 major European research funders' efforts to reduce research waste and publication bias in clinical trials meet World Health Organization (WHO) best practice benchmarks and to investigate areas for improvement.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study was based on 2 to 3 independent assessments of each funder's publicly available documentation and validation of results with funders during 2021. Included funders were the 21 largest nonmultilateral public and philanthropic medical research funders in Europe, with a combined budget of more than US $22 billion.

EXPOSURES

Scoring of funders using an 11-item assessment tool based on WHO best practice benchmarks, grouped into 4 broad categories: trial registries, academic publication, monitoring, and sanctions. Funder references to reporting standards were captured.

MAIN OUTCOMES AND MEASURES

The primary outcome was funder adoption or nonadoption of 11 policy and monitoring measures to reduce research waste and publication bias as set out by WHO best practices. The secondary outcomes were whether and how funder policies referred to reporting standards. Outcomes were preregistered after a pilot phase that used the same outcome measures.

RESULTS

Among 21 of the largest nonmultilateral public and philanthropic funders in Europe, some best practices were more widely adopted than others, with 14 funders (66.7%) mandating prospective trial registration and 6 funders (28.6%) requiring that trial results be made public on trial registries within 12 months of trial completion. Less than half of funders actively monitored whether trials were registered (9 funders [42.9%]) or whether results were made public (8 funders [38.1%]). Funders implemented a mean of 4 of 11 best practices in clinical trial transparency (36.4%) set out by WHO. The extent to which funders adopted WHO best practice items varied widely, ranging from 0 practices for the French Centre National de la Recherche Scientifique and the ministries of health of Germany and Italy to 10 practices (90.9%) for the UK National Institute of Health Research. Overall, 9 funders referred to reporting standards in their policies.

CONCLUSIONS AND RELEVANCE

This study found that many European medical research funder policy and monitoring measures fell short of WHO best practices. These findings suggest that funders worldwide may need to identify and address gaps in policies and processes.

Comparative effects of pharmacological interventions for the acute and long-term management of insomnia disorder in adults: a systematic review and network meta-analysis

BACKGROUND

Behavioural, cognitive, and pharmacological interventions can all be effective for insomnia. However, because of inadequate resources, medications are more frequently used worldwide. We aimed to estimate the comparative effectiveness of pharmacological treatments for the acute and long-term treatment of adults with insomnia disorder.

METHODS

In this systematic review and network meta-analysis, we searched the Cochrane Central Register of Controlled Trials, MEDLINE, PubMed, Embase, PsycINFO, WHO International Clinical Trials Registry Platform, ClinicalTrials.gov, and websites of regulatory agencies from database inception to Nov 25, 2021, to identify published and unpublished randomised controlled trials. We included studies comparing pharmacological treatments or placebo as monotherapy for the treatment of adults (≥18 year) with insomnia disorder. We assessed the certainty of evidence using the confidence in network meta-analysis (CINeMA) framework. Primary outcomes were efficacy (ie, quality of sleep measured by any self-rated scale), treatment discontinuation for any reason and due to side-effects specifically, and safety (ie, number of patients with at least one adverse event) both for acute and long-term treatment. We estimated summary standardised mean differences (SMDs) and odds ratios (ORs) using pairwise and network meta-analysis with random effects. This study is registered with Open Science Framework, https://doi.org/10.17605/OSF.IO/PU4QJ.

FINDINGS

We included 170 trials (36 interventions and 47 950 participants) in the systematic review and 154 double-blind, randomised controlled trials (30 interventions and 44 089 participants) were eligible for the network meta-analysis. In terms of acute treatment, benzodiazepines, doxylamine, eszopiclone, lemborexant, seltorexant, zolpidem, and zopiclone were more efficacious than placebo (SMD range: 0·36-0·83 [CINeMA estimates of certainty: high to moderate]). Benzodiazepines, eszopiclone, zolpidem, and zopiclone were more efficacious than melatonin, ramelteon, and zaleplon (SMD 0·27-0·71 [moderate to very low]). Intermediate-acting benzodiazepines, long-acting benzodiazepines, and eszopiclone had fewer discontinuations due to any cause than ramelteon (OR 0·72 [95% CI 0·52-0·99; moderate], 0·70 [0·51-0·95; moderate] and 0·71 [0·52-0·98; moderate], respectively). Zopiclone and zolpidem caused more dropouts due to adverse events than did placebo (zopiclone: OR 2·00 [95% CI 1·28-3·13; very low]; zolpidem: 1·79 [1·25-2·50; moderate]); and zopiclone caused more dropouts than did eszopiclone (OR 1·82 [95% CI 1·01-3·33; low]), daridorexant (3·45 [1·41-8·33; low), and suvorexant (3·13 [1·47-6·67; low]). For the number of individuals with side-effects at study endpoint, benzodiazepines, eszopiclone, zolpidem, and zopiclone were worse than placebo, doxepin, seltorexant, and zaleplon (OR range 1·27-2·78 [high to very low]). For long-term treatment, eszopiclone and lemborexant were more effective than placebo (eszopiclone: SMD 0·63 [95% CI 0·36-0·90; very low]; lemborexant: 0·41 [0·04-0·78; very low]) and eszopiclone was more effective than ramelteon (0.63 [0·16-1·10; very low]) and zolpidem (0·60 [0·00-1·20; very low]). Compared with ramelteon, eszopiclone and zolpidem had a lower rate of all-cause discontinuations (eszopiclone: OR 0·43 [95% CI 0·20-0·93; very low]; zolpidem: 0·43 [0·19-0·95; very low]); however, zolpidem was associated with a higher number of dropouts due to side-effects than placebo (OR 2·00 [95% CI 1·11-3·70; very low]).

INTERPRETATION

Overall, eszopiclone and lemborexant had a favorable profile, but eszopiclone might cause substantial adverse events and safety data on lemborexant were inconclusive. Doxepin, seltorexant, and zaleplon were well tolerated, but data on efficacy and other important outcomes were scarce and do not allow firm conclusions. Many licensed drugs (including benzodiazepines, daridorexant, suvorexant, and trazodone) can be effective in the acute treatment of insomnia but are associated with poor tolerability, or information about long-term effects is not available. Melatonin, ramelteon, and non-licensed drugs did not show overall material benefits. These results should serve evidence-based clinical practice.

FUNDING

UK National Institute for Health Research Oxford Health Biomedical Research Centre.

Reporting bias in clinical trials: Progress toward transparency and next steps

Mayookha Mitra-Majumdar and Aaron Kesselheim reflect on steps taken to combat reporting bias in clinical trials over the last two decades.