Combining endpoint and change data did not affect the summary standardised mean difference in pairwise and network meta-analyses: An empirical study in depression.

When studies use different scales to measure continuous outcomes, standardised mean differences (SMD) are required to meta-analyse the data. However, outcomes are often reported as endpoint or change from baseline scores. Combining corresponding SMDs can be problematic and available guidance advises against this practice. We aimed to examine the impact of combining the two types of SMD in meta-analyses of depression severity. We used individual participant data on pharmacological interventions (89 studies, 27,409 participants) and internet-delivered cognitive behavioural therapy (iCBT; 61 studies, 13,687 participants) for depression to compare endpoint and change from baseline SMDs at the study level. Next, we performed pairwise (PWMA) and network meta-analyses (NMA) using endpoint SMDs, change from baseline SMDs, or a mixture of the two. Study-specific SMDs calculated from endpoint and change from baseline data were largely similar, although for iCBT interventions 25% of the studies at 3 months were associated with important differences between study-specific SMDs (median 0.01, IQR -0.10, 0.13) especially in smaller trials with baseline imbalances. However, when pooled, the differences between endpoint and change SMDs were negligible. Pooling only the more favourable of the two SMDs did not materially affect meta-analyses, resulting in differences of pooled SMDs up to 0.05 and 0.13 in the pharmacological and iCBT datasets, respectively. Our findings have implications for meta-analyses in depression, where we showed that the choice between endpoint and change scores for estimating SMDs had immaterial impact on summary meta-analytic estimates. Future studies should replicate and extend our analyses to fields other than depression.

Treatment methods for cervical intraepithelial neoplasia in England: A cost-effectiveness analysis.

OBJECTIVE: To compare the cost-effectiveness of different treatments for cervical intraepithelial neoplasia (CIN). DESIGN: A cost-effectiveness analysis based on data available in the literature and expert opinion. SETTING: England. POPULATION: Women treated for CIN. METHODS: We developed a decision-analytic model to simulate the clinical course of 1000 women who received local treatment for CIN and were followed up for 10 years after treatment. In the model we considered surgical complications as well as oncological and reproductive outcomes over the 10-year period. The costs calculated were those incurred by the National Health Service (NHS) of England. MAIN OUTCOME MEASURES: Cost per one CIN2+ recurrence averted (oncological outcome); cost per one preterm birth averted (reproductive outcome); overall cost per one adverse oncological or reproductive outcome averted. RESULTS: For young women of reproductive age, large loop excision of the transformation zone (LLETZ) was the most cost-effective treatment overall at all willingness-to-pay thresholds. For postmenopausal women, LLETZ remained the most cost-effective treatment up to a threshold of £31,500, but laser conisation became the most cost-effective treatment above that threshold. CONCLUSIONS: LLETZ is the most cost-effective treatment for both younger and older women. However, for older women, more radical excision with laser conisation could also be considered if the NHS is willing to spend more than £31,500 to avert one CIN2+ recurrence.

Combining randomized and non-randomized data to predict heterogeneous effects of competing treatments.

Some patients benefit from a treatment while others may do so less or do not benefit at all. We have previously developed a two-stage network meta-regression prediction model that synthesized randomized trials and evaluates how treatment effects vary across patient characteristics. In this article, we extended this model to combine different sources of types in different formats: aggregate data (AD) and individual participant data (IPD) from randomized and non-randomized evidence. In the first stage, a prognostic model is developed to predict the baseline risk of the outcome using a large cohort study. In the second stage, we recalibrated this prognostic model to improve our predictions for patients enrolled in randomized trials. In the third stage, we used the baseline risk as effect modifier in a network meta-regression model combining AD, IPD randomized clinical trial to estimate heterogeneous treatment effects. We illustrated the approach in the re-analysis of a network of studies comparing three drugs for relapsing-remitting multiple sclerosis. Several patient characteristics influence the baseline risk of relapse, which in turn modifies the effect of the drugs. The proposed model makes personalized predictions for health outcomes under several treatment options and encompasses all relevant randomized and non-randomized evidence.

Methods proposed for monitoring the implementation of evidence-based research: a cross-sectional study.

OBJECTIVES: Evidence-based research (EBR) is the systematic and transparent use of prior research to inform a new study so that it answers questions that matter in a valid, efficient, and accessible manner. This study surveyed experts about existing (e.g., citation analysis) and new methods for monitoring EBR and collected ideas about implementing these methods. STUDY DESIGN AND SETTING: We conducted a cross-sectional study via an online survey between November 2022 and March 2023. Participants were experts from the fields of evidence synthesis and research methodology in health research. Open-ended questions were coded by recurring themes; descriptive statistics were used for quantitative questions. RESULTS: Twenty-eight expert participants suggested that citation analysis should be supplemented with content evaluation (not just what is cited but also in which context), content expert involvement, and assessment of the quality of cited systematic reviews. They also suggested that citation analysis could be facilitated with automation tools. They emphasized that EBR monitoring should be conducted by ethics committees and funding bodies before the research starts. Challenges identified for EBR implementation monitoring were resource constraints and clarity on responsibility for EBR monitoring. CONCLUSION: Ideas proposed in this study for monitoring the implementation of EBR can be used to refine methods and define responsibility but should be further explored in terms of feasibility and acceptability. Different methods may be needed to determine if the use of EBR is improving over time.

Estimating the smallest worthwhile difference of antidepressants: a cross-sectional survey.

BACKGROUND: Approximately 30% of patients experience substantial improvement in depression after 2 months without treatment, and 45% with antidepressants. The smallest worthwhile difference (SWD) refers to an intervention's smallest beneficial effect over a comparison patients deem worthwhile given treatment burdens (harms, expenses and inconveniences), but is undetermined for antidepressants. OBJECTIVE: Estimating the SWD of commonly prescribed antidepressants for depression compared to no treatment. METHODS: The SWD was estimated as a patient-required difference in response rates between antidepressants and no treatment after 2 months. An online cross-sectional survey using Prolific, MQ Mental Health and Amazon Mechanical Turk crowdsourcing services in the UK and USA between October 2022 and January 2023 garnered participants (N=935) that were a mean age of 44.1 (SD=13.9) and 66% women (n=617). FINDINGS: Of 935 participants, 124 reported moderate-to-severe depressive symptoms but were not in treatment, 390 were in treatment and 495 reported absent-to-mild symptoms with or without treatment experiences. The median SWD was a 20% (IQR=10-30%) difference in response rates for people with moderate-to-severe depressive symptoms, not in treatment, and willing to consider antidepressants, and 25% (IQR=10-35%) for the full sample. CONCLUSIONS: Our observed SWDs mean that the current 15% antidepressant benefit over no treatment was sufficient for one in three people to accept antidepressants given the burdens, but two in three expected greater treatment benefits. IMPLICATIONS: While a minority may be satisfied with the best currently available antidepressants, more effective and/or less burdensome medications are needed, with more attention given to patient perspectives.

Estimating and visualising the trade-off between benefits and harms on multiple clinical outcomes in network meta-analysis.

BACKGROUND: The relative treatment effects estimated from network meta-analysis can be employed to rank treatments from the most preferable to the least preferable option. These treatment hierarchies are typically based on ranking metrics calculated from a single outcome. Some approaches have been proposed in the literature to account for multiple outcomes and individual preferences, such as the coverage area inside a spie chart, that, however, does not account for a trade-off between efficacy and safety outcomes. We present the net-benefit standardised area within a spie chart, [Formula: see text] to explore the changes in treatment performance with different trade-offs between benefits and harms, according to a particular set of preferences. METHODS: We combine the standardised areas within spie charts for efficacy and safety/acceptability outcomes with a value λ specifying the trade-off between benefits and harms. We derive absolute probabilities and convert outcomes on a scale between 0 and 1 for inclusion in the spie chart. RESULTS: We illustrate how the treatments in three published network meta-analyses perform as the trade-off λ varies. The decrease of the [Formula: see text] quantity appears more pronounced for some drugs, e.g. haloperidol. Changes in treatment performance seem more frequent when SUCRA is employed as outcome measures in the spie charts. CONCLUSIONS: [Formula: see text] should not be interpreted as a ranking metric but it is a simple approach that could help identify which treatment is preferable when multiple outcomes are of interest and trading-off between benefits and harms is important.

Condom use and HIV testing among adults in Switzerland: repeated national cross-sectional surveys 2007, 2012, and 2017.

BACKGROUND: Monitoring of HIV and sexually transmitted infection (STI) prevention is important for guiding national sexual health programmes for both the general population and key populations. The objectives of this study were to examine trends and patterns of condom use at last intercourse and lifetime HIV testing in 2007, 2012 and 2017 in Switzerland, and to explore factors associated with these behaviours in men and women with opposite-sex partners and with same sex partners. METHODS: We analysed data from the 2007, 2012 and 2017 Swiss Health Survey. For each time point, outcome and population group, we conducted a descriptive analysis of weighted data and conducted multivariable logistic regression to obtain adjusted odds ratios (aOR) with 95% confidence intervals (CI) and compared outcomes between the timepoints. RESULTS: In total, 46,320 people were interviewed: 21,847 men and 23,141 women, who reported having sex only with partners of the opposite sex, 633 men who reported sex with a male partner and 699 women who reported sex with a female partner. Among the three surveys the prevalence of condom did not change but varied from 22 to 26% of men and 15 to 21% in women with only opposite-sex partners (aOR men, 0.93, 95% CI 0.82, 1.06; women 0.98, 95% CI 0.86 to 1.11). In men with any same sex partner the prevalence of condom use was 40% in 2007, 33% in 2012 and 54% in 2017 (aOR 1.80, 95% CI 0.97, 3.34). In multivariable analysis, the factor most strongly associated with condom use was sex with an occasional partner at last intercourse. HIV testing ever increased across all three survey years in people with opposite sex partners: 2017 vs. 2007, aOR men with only opposite-sex partners 1.64 (95% CI 1.49, 1.82), women with only opposite-sex partners 1.67 (1.51, 1.85), men with any same sex partner 0.98 (0.49, 1.96), women with any same sex partner 1.31 (0.74, 2.30). CONCLUSIONS: Monitoring of condom use, and HIV testing should continue and contribute to the development of the national sexual health programme. Stronger promotion of condoms for people with opposite-sex partners might be needed, since overall condom use at last intercourse has not changed since 2007.

Semi-automated assessment of the risk of bias due to missing evidence in network meta-analysis: a guidance paper for the ROB-MEN web-application.

Network meta-analysis compares multiple interventions and estimates the relative treatment effects between all interventions, combining both direct and indirect evidence. Recently, a framework was developed to assess the Risk Of Bias due to Missing Evidence in Network meta-analysis (ROB-MEN) which is part of the more comprehensive framework to evaluate the Confidence In the evidence for Network Meta-Analysis (CINeMA). To produce an overall risk of bias judgement for each network estimate, ROB-MEN: performs an assessment of the bias due to missing evidence in each possible pairwise comparison; combines the assessment with the contribution from the direct pairwise comparisons; considers the potential for small-study effects. To facilitate and semi-automate this process, ROB-MEN has been implemented in a user-friendly web-application ( https://cinema.ispm.unibe.ch/rob-men ). Here we provide a tutorial detailing the functionality and use of the application consisting of data upload, analysis configuration, output visualisation, and production of the tool's output tables for recording the risk of bias assessment. We also illustrate an example application using the demo dataset available for download on the application's homepage. The ROB-MEN web-application is open-source and freely available ( https://github.com/esm-ispm-unibe-ch/rob-men ).

Tool to assess risk of bias in studies estimating the prevalence of mental health disorders (RoB-PrevMH).

OBJECTIVE: There is no standard tool for assessing risk of bias (RoB) in prevalence studies. For the purposes of a living systematic review during the COVID-19 pandemic, we developed a tool to evaluate RoB in studies measuring the prevalence of mental health disorders (RoB-PrevMH) and tested inter-rater reliability. METHODS: We decided on items and signalling questions to include in RoB-PrevMH through iterative discussions. We tested the reliability of assessments by different users with two sets of prevalence studies. The first set included a random sample of 50 studies from our living systematic review. The second set included 33 studies from a systematic review of the prevalence of post-traumatic stress disorders, major depression and generalised anxiety disorder. We assessed the inter-rater agreement by calculating the proportion of agreement and Kappa statistic for each item. RESULTS: RoB-PrevMH consists of three items that address selection bias and information bias. Introductory and signalling questions guide the application of the tool to the review question. The inter-rater agreement for the three items was 83%, 90% and 93%. The weighted kappa scores were 0.63 (95% CI 0.54 to 0.73), 0.71 (95% CI 0.67 to 0.85) and 0.32 (95% CI -0.04 to 0.63), respectively. CONCLUSIONS: RoB-PrevMH is a brief, user-friendly and adaptable tool for assessing RoB in studies on prevalence of mental health disorders. Initial results for inter-rater agreement were fair to substantial. The tool's validity, reliability and applicability should be assessed in future projects.

How to update a living systematic review and keep it alive during a pandemic: a practical guide.

BACKGROUND: The covid-19 pandemic has highlighted the role of living systematic reviews. The speed of evidence generated during the covid-19 pandemic accentuated the challenges of managing high volumes of research literature. METHODS: In this article, we summarise the characteristics of ongoing living systematic reviews on covid-19, and we follow a life cycle approach to describe key steps in a living systematic review. RESULTS: We identified 97 living systematic reviews on covid-19, published up to 7th November 2022, which focused mostly on the effects of pharmacological interventions (n = 46, 47%) or the prevalence of associated conditions or risk factors (n = 30, 31%). The scopes of several reviews overlapped considerably. Most living systematic reviews included both observational and randomised study designs (n = 45, 46%). Only one-third of the reviews has been updated at least once (n = 34, 35%). We address practical aspects of living systematic reviews including how to judge whether to start a living systematic review, methods for study identification and selection, data extraction and evaluation, and give recommendations at each step, drawing from our own experience. We also discuss when it is time to stop and how to publish updates. CONCLUSIONS: Methods to improve the efficiency of searching, study selection, and data extraction using machine learning technologies are being developed, their performance and applicability, particularly for reviews based on observational study designs should improve, and ways of publishing living systematic reviews and their updates will continue to evolve. Finally, knowing when to end a living systematic review is as important as knowing when to start.

New living evidence resource of human and non-human studies for early intervention and research prioritisation in anxiety, depression and psychosis.

In anxiety, depression and psychosis, there has been frustratingly slow progress in developing novel therapies that make a substantial difference in practice, as well as in predicting which treatments will work for whom and in what contexts. To intervene early in the process and deliver optimal care to patients, we need to understand the underlying mechanisms of mental health conditions, develop safe and effective interventions that target these mechanisms, and improve our capabilities in timely diagnosis and reliable prediction of symptom trajectories. Better synthesis of existing evidence is one way to reduce waste and improve efficiency in research towards these ends. Living systematic reviews produce rigorous, up-to-date and informative evidence summaries that are particularly important where research is emerging rapidly, current evidence is uncertain and new findings might change policy or practice. Global Alliance for Living Evidence on aNxiety, depressiOn and pSychosis (GALENOS) aims to tackle the challenges of mental health science research by cataloguing and evaluating the full spectrum of relevant scientific research including both human and preclinical studies. GALENOS will also allow the mental health community-including patients, carers, clinicians, researchers and funders-to better identify the research questions that most urgently need to be answered. By creating open-access datasets and outputs in a state-of-the-art online resource, GALENOS will help identify promising signals early in the research process. This will accelerate translation from discovery science into effective new interventions for anxiety, depression and psychosis, ready to be translated in clinical practice across the world.

Predicting outcomes at the individual patient level: what is the best method?

OBJECTIVE: When developing prediction models, researchers commonly employ a single model which uses all the available data (end-to-end approach). Alternatively, a similarity-based approach has been previously proposed, in which patients with similar clinical characteristics are first grouped into clusters, then prediction models are developed within each cluster. The potential advantage of the similarity-based approach is that it may better address heterogeneity in patient characteristics. However, it remains unclear whether it improves the overall predictive performance. We illustrate the similarity-based approach using data from people with depression and empirically compare its performance with the end-to-end approach. METHODS: We used primary care data collected in general practices in the UK. Using 31 predefined baseline variables, we aimed to predict the severity of depressive symptoms, measured by Patient Health Questionnaire-9, 60 days after initiation of antidepressant treatment. Following the similarity-based approach, we used k-means to cluster patients based on their baseline characteristics. We derived the optimal number of clusters using the Silhouette coefficient. We used ridge regression to build prediction models in both approaches. To compare the models' performance, we calculated the mean absolute error (MAE) and the coefficient of determination (R2) using bootstrapping. RESULTS: We analysed data from 16 384 patients. The end-to-end approach resulted in an MAE of 4.64 and R2 of 0.20. The best-performing similarity-based model was for four clusters, with MAE of 4.65 and R2 of 0.19. CONCLUSIONS: The end-to-end and the similarity-based model yielded comparable performance. Due to its simplicity, the end-to-end approach can be favoured when using demographic and clinical data to build prediction models on pharmacological treatments for depression.

Estimating Patient-Specific Relative Benefit of Adding Biologics to Conventional Rheumatoid Arthritis Treatment: An Individual Participant Data Meta-Analysis.

Importance: Current evidence remains ambiguous regarding whether biologics should be added to conventional treatment of rheumatoid arthritis for specific patients, which may cause potential overuse or treatment delay. Objectives: To estimate the benefit of adding biologics to conventional antirheumatic drugs for the treatment of rheumatoid arthritis given baseline characteristics. Data Sources: Cochrane CENTRAL, Scopus, MEDLINE, and the World Health Organization International Clinical Trials Registry Platform were searched for articles published from database inception to March 2, 2022. Study Selection: Randomized clinical trials comparing certolizumab plus conventional antirheumatic drugs with placebo plus conventional drugs were selected. Data Extraction and Synthesis: Individual participant data of the prespecified outcomes and covariates were acquired from the Vivli database. A 2-stage model was fitted to estimate patient-specific relative outcomes of adding certolizumab vs conventional drugs only. Stage 1 was a penalized logistic regression model to estimate the baseline expected probability of the outcome regardless of treatment using baseline characteristics. Stage 2 was a bayesian individual participant data meta-regression model to estimate the relative outcomes for a particular baseline expected probability. Patient-specific results were displayed interactively on an application based on a 2-stage model. Main Outcomes and Measures: The primary outcome was low disease activity or remission at 3 months, defined by 3 disease activity indexes (ie, Disease Activity Score based on the evaluation of 28 joints, Clinical Disease Activity Index, or Simplified Disease Activity Index). Results: Individual participant data were obtained from 3790 patients (2996 female [79.1%] and 794 male [20.9%]; mean [SD] age, 52.7 [12.3] years) from 5 large randomized clinical trials for moderate to high activity rheumatoid arthritis with usable data for 22 prespecified baseline covariates. Overall, adding certolizumab was associated with a higher probability of reaching low disease activity. The odds ratio for patients with an average baseline expected probability of the outcome was 6.31 (95% credible interval, 2.22-15.25). However, the benefits differed in patients with different baseline characteristics. For example, the estimated risk difference was smaller than 10% for patients with either low or high baseline expected probability. Conclusions and Relevance: In this individual participant data meta-analysis, adding certolizumab was associated with more effectiveness for rheumatoid arthritis in general. However, the benefit was uncertain for patients with low or high baseline expected probability, for whom other evaluations were necessary. The interactive application displaying individual estimates may help with treatment selection.

Long-term efficacy of antipsychotic drugs in initially acutely ill adults with schizophrenia: systematic review and network meta-analysis.

Most acute phase antipsychotic drug trials in schizophrenia last only a few weeks, but patients must usually take these drugs much longer. We examined the long-term efficacy of antipsychotic drugs in acutely ill patients using network meta-analysis. We searched the Cochrane Schizophrenia Group register up to March 6, 2022 for randomized, blinded trials of at least 6-month duration on all second-generation and 18 first-generation antipsychotics. The primary outcome was change in overall symptoms of schizophrenia; secondary outcomes were all-cause discontinuation; change in positive, negative and depressive symptoms; quality of life, social functioning, weight gain, antiparkinson medication use, akathisia, serum prolactin level, QTc prolongation, and sedation. Confidence in the results was assessed by the CINeMA (Confidence in Network Meta-Analysis) framework. We included 45 studies with 11,238 participants. In terms of overall symptoms, olanzapine was on average more efficacious than ziprasidone (standardized mean difference, SMD=0.37, 95% CI: 0.26-0.49), asenapine (SMD=0.33, 95% CI: 0.21-0.45), iloperidone (SMD=0.32, 95% CI: 0.15-0.49), paliperidone (SMD=0.28, 95% CI: 0.11-0.44), haloperidol (SMD=0.27, 95% CI: 0.14-0.39), quetiapine (SMD=0.25, 95% CI: 0.12-0.38), aripiprazole (SMD=0.16, 95% CI: 0.04-0.28) and risperidone (SMD=0.12, 95% CI: 0.03-0.21). The 95% CIs for olanzapine versus aripiprazole and risperidone included the possibility of trivial effects. The differences between olanzapine and lurasidone, amisulpride, perphenazine, clozapine and zotepine were either small or uncertain. These results were robust in sensitivity analyses and in line with other efficacy outcomes and all-cause discontinuation. Concerning weight gain, the impact of olanzapine was higher than all other antipsychotics, with a mean difference ranging from -4.58 kg (95% CI: -5.33 to -3.83) compared to ziprasidone to -2.30 kg (95% CI: -3.35 to -1.25) compared to amisulpride. Our data suggest that olanzapine is more efficacious than a number of other antipsychotic drugs in the longer term, but its efficacy must be weighed against its side effect profile.

Antipsychotic drugs and their effects on cognitive function: protocol for a systematic review, pairwise, and network meta-analysis.

BACKGROUND: There is evidence that antipsychotic drugs differ in their effect on the cognitive symptoms of schizophrenia. So far, there is no comprehensive systematic review available that would enable providers and patients to make informed choices regarding this important aspect of treatment. With a large number of substances available, conventional pairwise meta-analyses will not be sufficient to inform this choice. To fill this gap, we will conduct a network meta-analysis (NMA), integrating direct and indirect comparisons from randomized controlled trials (RCTs) to rank antipsychotics according to their effect on cognitive functioning. METHODS: In our NMA, we will include RCTs in patients with schizophrenia or schizophrenia-like psychoses comparing one antipsychotic agent with another antipsychotic agent or placebo that measures cognitive function. We will include studies on patients of every age group, in any phase of illness (e.g., acute or stable, first episode or chronic schizophrenia, in- or outpatients) with an intervention time of at least 3 weeks. The primary outcome will be the composite score of cognitive functioning, preferentially measured with the test battery developed by the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) initiative. The secondary outcomes include the seven cognitive domains that the composite score is composed of, as well as functioning and quality of life. Study selection and data extraction will be conducted by at least two independent reviewers. We will use the Cochrane Risk of Bias tool 2 to determine the risk of bias in studies, and we will evaluate the confidence in the results using Confidence in Network Meta-Analysis (CINeMA). We will perform NMA using R (package netmeta). We will conduct subgroup and sensitivity analyses to explore the heterogeneity and assess the robustness of our findings. DISCUSSION: This systematic review and network meta-analysis aims to inform evidence-based antipsychotic treatment choice for cognitive deficits in schizophrenia patients by analyzing existing RCTs on this subject. The results have the potential to support patients' and physicians' decision-making processes based on the latest available evidence. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42022312483.

A tool to assess risk of bias in studies estimating the prevalence of mental health disorders (RoB-PrevMH).

Objective: Biases affect how certain we are about the available evidence, however no standard tool for assessing the risk of bias (RoB) in prevalence studies exists. For the purposes of a living systematic review on prevalence of mental health disorders during the COVID-19 pandemic, we developed a RoB tool to evaluate prevalence studies in mental health (RoB-PrevMH) and tested interrater reliability. Methods: We reviewed existing RoB tools for prevalence studies until September 2020, to develop a tool for prevalence studies in mental health. We tested the reliability of assessments by different users of RoB-PrevMH in 83 studies stemming from two systematic reviews of prevalence studies in mental health. We assessed the interrater agreement by calculating the proportion of agreement and Kappa statistic for each item. Results: RoB-PrevMH consists of three items that address selection bias and information bias. Introductory and signaling questions guide the application of the tool to the review question. The interrater agreement for the three items was 83%, 90% and 93%. The weighted kappa was 0.63 (95% CI 0.54 to 0.73), 0.71 (95% CI 0.67 to 0.85) and 0.32 (95% CI -0.04 to -0.63), respectively. Conclusions: We developed a brief, user friendly, and adaptable tool for assessing RoB in studies on prevalence of mental health disorders. Initial results for interrater agreement were fair to substantial. The tool's validity, reliability, and applicability should be assessed in future projects.

Efficacy of clozapine compared with other second-generation antipsychotic drugs in patients with treatment-resistant schizophrenia: protocol for a systematic review and individual patient data meta-analysis of randomised controlled trials.

INTRODUCTION: Guidelines recommend clozapine for treatment-resistant schizophrenia. However, meta-analysis of aggregate data (AD) did not demonstrate higher efficacy of clozapine compared with other second-generation antipsychotics but found substantial heterogeneity between trials and variation between participants in treatment effects. Therefore, we will conduct an individual participant data (IPD) meta-analysis to estimate the efficacy of clozapine compared with other second-generation antipsychotics while accounting for potentially important effect modifiers. METHODS AND ANALYSIS: In a systematic review, two reviewers will independently search Cochrane Schizophrenia Group's trial register (without restrictions in date, language or state of publication) and related reviews. We will include randomised controlled trials (RCTs) in participants with treatment-resistant schizophrenia comparing clozapine with other second-generation antipsychotics for at least 6 weeks. We will apply no restrictions in age, gender, origin, ethnicity or setting, but exclude open-label studies, studies from China, experimental studies and phase II of cross-over trials. IPD will be requested from trial authors and cross-check against published results. AD will be extracted in duplicate. Risk of bias will be assessed using Cochrane's Risk of Bias 2 tool.The primary outcome will be overall symptoms of schizophrenia.We will synthesise results using random-effects meta-analysis and meta-regression methods in a 3-level Bayesian model. The model combines IPD with AD when IPD is not available for all studies, and include participant, intervention and study design characteristics as potential effect modifiers. The effect size measures will be mean difference (or standardised mean difference when different scales were used). Confidence in the evidence will be assessed using GRADE. ETHICS AND DISSEMINATION: This project has been approved by the ethics commission of the Technical University of Munich (#612/21 S-NP). The results will be published open-access in a peer-review journal and a plain-language version of the results will be disseminated.If we need to amend this protocol, we will describe the change and give the rationale in a specific section in the resulting publication 'Changes with respect to the protocol'. SYSTEMATIC REVIEW REGISTRATION: PROSPERO (#CRD42021254986).

Metabolic side effects in persons with schizophrenia during mid- to long-term treatment with antipsychotics: a network meta-analysis of randomized controlled trials.

Metabolic side effects of antipsychotic drugs can have serious health consequences and may increase mortality. Although persons with schizophrenia often take these drugs for a long time, their mid- to long-term metabolic effects have been studied little so far. This study aimed to evaluate the mid- to long-term metabolic side effects of 31 antipsychotics in persons with schizophrenia by applying a random-effects Bayesian network meta-analysis. We searched the Cochrane Schizophrenia Group's Study-Based Register of Trials (up to April 27, 2020) and PubMed (up to June 14, 2021). We included published and unpublished, open and blinded randomized controlled trials with a study duration >13 weeks which compared any antipsychotic in any form of administration with another antipsychotic or with placebo in participants diagnosed with schizophrenia. The primary outcome was weight gain measured in kilograms. Secondary outcomes included "number of participants with weight gain", fasting glucose, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides. We identified 137 eligible trials (with 35,007 participants) on 31 antipsychotics, with a median follow-up of 45 weeks. Chlorpromazine produced the most weight gain (mean difference to placebo: 5.13 kg, 95% credible interval, CrI: 1.98 to 8.30), followed by clozapine (4.21 kg, 95% CrI: 3.03 to 5.42), olanzapine (3.82 kg, 95% CrI: 3.15 to 4.50), and zotepine (3.87 kg, 95% CrI: 2.14 to 5.58). The findings did not substantially change in sensitivity and network meta-regression analyses, although enriched design, drug company sponsorship, and the use of observed case instead of intention-to-treat data modified the mean difference in weight gain to some extent. Antipsychotics with more weight gain were often also among the drugs with worse outcome in fasting glucose and lipid parameters. The confidence in the evidence ranged from low to moderate. In conclusion, antipsychotic drugs differ in their propensity to induce metabolic side effects in mid- to long-term treatment. Given that schizophrenia is often a chronic disorder, these findings should be given more consideration than short-term data in drug choice.

Synthesizing cross-design evidence and cross-format data using network meta-regression.

In network meta-analysis (NMA), we synthesize all relevant evidence about health outcomes with competing treatments. The evidence may come from randomized clinical trials (RCT) or non-randomized studies (NRS) as individual participant data (IPD) or as aggregate data (AD). We present a suite of Bayesian NMA and network meta-regression (NMR) models allowing for cross-design and cross-format synthesis. The models integrate a three-level hierarchical model for synthesizing IPD and AD into four approaches. The four approaches account for differences in the design and risk of bias (RoB) in the RCT and NRS evidence. These four approaches variously ignoring differences in RoB, using NRS to construct penalized treatment effect priors and bias-adjustment models that control the contribution of information from high RoB studies in two different ways. We illustrate the methods in a network of three pharmacological interventions and placebo for patients with relapsing-remitting multiple sclerosis. The estimated relative treatment effects do not change much when we accounted for differences in design and RoB. Conducting network meta-regression showed that intervention efficacy decreases with increasing participant age. We also re-analysed a network of 431 RCT comparing 21 antidepressants, and we did not observe material changes in intervention efficacy when adjusting for studies' high RoB. We re-analysed both case studies accounting for different study RoB. In summary, the described suite of NMA/NMR models enables the inclusion of all relevant evidence while incorporating information on the within-study bias in both observational and experimental data and enabling estimation of individualized treatment effects through the inclusion of participant characteristics.