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.

Optimal duration of antibiotic treatment for community-acquired pneumonia in adults: a systematic review and duration-effect meta-analysis.

OBJECTIVES: To find the optimal treatment duration with antibiotics for community-acquired pneumonia (CAP) in adults. DESIGN: Systematic review and duration-effect meta-analysis. DATA SOURCES: MEDLINE, Embase and CENTRAL through 25 August 2021. ELIGIBILITY CRITERIA: All randomised controlled trials comparing the same antibiotics used at the same daily dosage but for different durations for CAP in adults. Both outpatients and inpatients were included but not those admitted to intensive care units. We imposed no date, language or publication status restriction. DATA EXTRACTION AND SYNTHESIS: Data extraction by two independent reviewers. We conducted a random-effects, one-stage duration-effect meta-analysis with restricted cubic splines. We tested the non-inferiority with the prespecified non-inferiority margin of 10% examined against 10 days . The primary outcome was clinical improvement on day 15 (range 7-45 days). SECONDARY OUTCOMES: all-cause mortality, serious adverse events and clinical improvement on day 30 (15-60 days). RESULTS: We included nine trials (2399 patients with a mean (SD) age of 61.2 (22.1); 39% women). The duration-effect curve was monotonic with longer duration leading to a lower probability of improvement, and shorter treatment duration (3-9 days) was likely to be non-inferior to 10-day treatment. Harmful outcome curves indicated no association. The weighted average percentage of the primary outcome in the 10-day treatment arms was 68%. Using that average, the absolute clinical improvement rates of the following durations were: 3-day treatment 75% (95% CI: 68% to 81%), 5-day treatment 72% (95% CI: 66% to 78%) and 7-day treatment 69% (95% CI: 61% to 76%). CONCLUSIONS: Shorter treatment duration (3-5 days) probably offers the optimal balance between efficacy and treatment burden for treating CAP in adults if they achieved clinical stability. However, the small number of included studies and the overall moderate-to-high risk of bias may compromise the certainty of the results. Further research on the shorter duration range is required. PROSPERO REGISTRATION NUMBER: CRD 42021273357.

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).

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.

Optimal dose of brexpiprazole for augmentation therapy of antidepressant-refractory depression: A systematic review and dose-effect meta-analysis.

BACKGROUND: Brexpiprazole augmentation is an effective treatment strategy for antidepressant-refractory depression, but its optimal dosage remains unclear. AIMS: To find the optimal dosage of brexpiprazole as augmentation of other antidepressants. METHODS: We searched multiple electronic databases (from inception to September 16th, 2021) to identify double-blind, randomized placebo-controlled fixed-dose trials evaluating brexpiprazole augmentation therapy in adults (≥18 years old, both genders) with major depressive disorder not adequately responding to one or more antidepressant treatment. Our outcomes of interest at 8 weeks (range 4-12 weeks) were efficacy (treatment response defined as 50% or greater reduction in depression severity), tolerability (dropouts due to adverse effects) and acceptability (dropouts for any reason). We performed a random-effects, one-stage dose-effect meta-analysis with restricted cubic splines. RESULTS: Six studies met the inclusion criteria, including 1671 participants in total. The dose-efficacy curve showed an increase up to doses around 2 mg (odds ratio [OR] 1.52, 95% confidence interval [CI] 1.12-2.06) and then a decreasing trend through the higher licensed dose up to 3 mg (OR 1.40, 95% CI 0.95-2.08). The shape of the dose-tolerability curve was comparable to that of the efficacy and the dose-acceptability curve showed a monotonic increasing trend but both had wide confidence bands. CONCLUSIONS: One to two milligrams of brexpiprazole as augmentation treatment may achieve an optimal balance between efficacy, tolerability, and acceptability in the acute treatment of antidepressant-refractory depression. However, the small number of included studies limit the reliability of the results. Further research is required to validate the findings.

A dose-effect network meta-analysis model with application in antidepressants using restricted cubic splines.

Network meta-analysis has been used to answer a range of clinical questions about the preferred intervention for a given condition. Although the effectiveness and safety of pharmacological agents depend on the dose administered, network meta-analysis applications typically ignore the role that drugs dosage plays in the results. This leads to more heterogeneity in the network. In this paper, we present a suite of network meta-analysis models that incorporate the dose-effect relationship using restricted cubic splines. We extend existing models into a dose-effect network meta-regression to account for study-level covariates and for groups of agents in a class-effect dose-effect network meta-analysis model. We apply our models to a network of aggregate data about the efficacy of 21 antidepressants and placebo for depression. We find that all antidepressants are more efficacious than placebo after a certain dose. Also, we identify the dose level at which each antidepressant's effect exceeds that of placebo and estimate the dose beyond which the effect of antidepressants no longer increases. When covariates were introduced to the model, we find that studies with small sample size tend to exaggerate antidepressants efficacy for several of the drugs. Our dose-effect network meta-analysis model with restricted cubic splines provides a flexible approach to modelling the dose-effect relationship in multiple interventions. Decision-makers can use our model to inform treatment choice.

Antipsychotic-Induced Weight Gain: Dose-Response Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: Weight gain is among the most important side-effects of antipsychotics. It is, however, unclear whether it is associated with antipsychotic doses. We aimed to fill this gap with a dose-response meta-analysis. METHODS: We searched multiple electronic databases (last update search June 2021) for all fixed-dose studies that investigated 16 second-generation antipsychotics and haloperidol in adults with acute exacerbation of schizophrenia or with negative symptoms. We estimated the dose-response curves by conducting random-effects dose-response meta-analyses. We used the restricted cubic spline to model the dose-response relationship. The primary outcome was mean weight gain in kg from baseline to endpoint, the secondary outcome was the number of patients with clinically important weight gain. FINDINGS: Ninety-seven studies with 333 dose arms (36 326 participants) provided data for meta-analyses. Most studies were short-term with median duration of 6 weeks (range 4 to 26 weeks). In patients with acute exacerbation, amisulpride, aripiprazole, brexpiprazole, cariprazine, haloperidol, lumateperone, and lurasidone produced mild weight gain in comparison to placebo (mean difference at any dose≤1 kg), while more significant weight gain was observed by all other drugs. For most drugs, dose-response curves showed an initial dose-related increase in weight which plateaued at higher doses, while for others there was no plateau and some even had bell-shaped curves, meaning less weight gain to be associated with higher doses. INTERPRETATION: Second-generation antipsychotics do not only differ in their propensity to produce weight gain, but also in the shapes of their dose-response curves. This information is important for dosing decisions in clinical practice.

Optimal dose of aripiprazole for augmentation therapy of antidepressant-refractory depression: preliminary findings based on a systematic review and dose-effect meta-analysis.

BACKGROUND: Aripiprazole augmentation is proven effective for antidepressant-refractory depression, but its licensed dose range is wide and optimal dosage remains unclear. AIMS: To find the optimal dosage of aripiprazole augmentation. METHOD: Multiple electronic databases were searched (from inception to 16 February 2021) to identify all assessor-masked randomised controlled trials evaluating aripiprazole augmentation therapy in adults (≥18 years old, both genders) with major depressive disorder showing inadequate response to at least one antidepressant treatment. A random-effects, one-stage dose-effect meta-analysis with restricted cubic splines was conducted. Outcomes were efficacy (treatment response: ≥50% reduction in depression severity), tolerability (drop-out due to adverse effects) and acceptability (drop-out for any reason) after 8 weeks of treatment (range 4-12 weeks). RESULTS: Ten studies met the inclusion criteria. All were individually randomised, placebo-controlled, multi-centre, parallel studies including 2625 participants in total. The maximum target dose-efficacy curve showed an increase up to doses between 2 mg (odds ratio OR = 1.46, 95% CI 1.15-1.85) and 5 mg (OR = 1.93, 95% CI 1.33-2.81), and then a non-increasing trend through the higher licensed doses up to 20 mg (OR = 1.90, 95% CI 1.52-2.37). Tolerability showed a similar trend with greater uncertainty. Acceptability showed no significant difference through the examined dose range. Certainty of evidence was low to moderate. CONCLUSIONS: Low-dose aripiprazole as augmentation treatment might achieve the optimal balance between efficacy, tolerability and acceptability in the acute treatment of antidepressant-refractory depression. However, the small number of included studies and the overall moderate to high risk of bias seriously compromise the reliability of the results. Further research is required to investigate the benefits of low versus high dose.

Dose-effect meta-analysis for psychopharmacological interventions using randomised data.

OBJECTIVE: The current practice in meta-analysis of the effects of psychopharmacological interventions ignors the administered dose or restricts the analysis in a dose range. This may introduce unnecessary uncertainty and heterogeneity. Methods have been developed to integrate the dose-effect models in meta-analysis. METHODS: We describe the two-stage and the one-stage models to conduct a dose-effect meta-analysis using common or random effects methods. We illustrate the methods on a dataset of selective serotonin reuptake inhibitor antidepressants. The dataset comprises 60 randomised controlled trials. The dose-effect is measured on an odds ratio scale and is modelled using restricted cubic splines to detect departure from linearity. RESULTS: The estimated summary curve indicates that the probability of response increases up to 30 mg/day of fluoxetine-equivalent which results in reaching 50% probability to respond. Beyond 40 mg/day, no further increase in the response is observed. The one-stage model includes all studies, resulting in slightly less uncertainty than the two-stage model where only part of the data is analysed. CONCLUSIONS: The dose-effect meta-analysis enables clinicians to understand how the effect of a drug changes as a function of its dose. Such analysis should be conducted in practice using the one-stage model that incorporates evidence from all available studies.

Examination of Dosing of Antipsychotic Drugs for Relapse Prevention in Patients With Stable Schizophrenia: A Meta-analysis.

Importance: The doses of antipsychotic drugs needed for relapse prevention in schizophrenia is a debated issue. Objective: To examine dose-response findings in a meta-analysis of randomized clinical trials. Data Sources: Studies were identified through the Cochrane Schizophrenia Group's Study-Based Register of Trials (March 9, 2020), PubMed (January 1, 2021), and previous reviews. First authors and/or pharmaceutical companies were contacted for additional information. Study Selection: Two reviewers independently selected randomized clinical trials that compared fixed doses of a second-generation antipsychotic, haloperidol, or fluphenazine for relapse prevention in patients with stable schizophrenia. Data Extraction and Synthesis: Using the Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline, all parameters in duplicate were extracted and frequentist dose-response random-effects meta-analyses were conducted. Main Outcomes and Measures: Study-defined relapse (primary outcome), rehospitalization, Positive and Negative Syndrome Scale or Brief Psychiatric Rating Scale total score reduction from baseline, all-cause discontinuation, and dropouts due to adverse events. Results: Evidence from 72 dose arms from 26 studies with 4776 participants was analyzed. The efficacy-related dose-response curves had a hyperbolic shape meaning that the probability to relapse decreased rapidly with doses of up to 5-mg/d risperidone equivalent (relative relapse risk, 0.43; 95% CI, 0.31-0.57; standardized mean difference for Positive and Negative Syndrome Scale total score reduction, -0.55; 95% CI, -0.68 to -0.41), but flattened thereafter. In contrast, dropouts due to adverse events continued to increase beyond this dose (relative risk at 5 mg/d, 1.38; 95% CI, 0.87-2.55; relative risk at 15 mg/d, 2.68; 95% CI, 1.49-4.62). In a subgroup analysis of patients in remission, a plateau was reached earlier, at approximately 2.5-mg/d risperidone equivalent. Conclusions and Relevance: The findings of this meta-analysis suggest that doses higher than approximately 5-mg/d risperidone equivalent may provide limited additional benefit for relapse prevention but more adverse events. For patients in remission or who are receiving high-potency first-generation antipsychotics, doses as low as 2.5-mg/d risperidone equivalent may be sufficient. However, caution is needed at this low dose end when further decreases of dose may be accompanied by a disproportionally higher relapse risk. Moreover, the observations are averages, and factors such as slow or rapid metabolism, age, illness stage, comorbidities, and drug-drug interactions suggest that individual patients will often need higher or lower doses.

A Bayesian dose-response meta-analysis model: A simulations study and application.

Dose-response models express the effect of different dose or exposure levels on a specific outcome. In meta-analysis, where aggregated-level data is available, dose-response evidence is synthesized using either one-stage or two-stage models in a frequentist setting. We propose a hierarchical dose-response model implemented in a Bayesian framework. We develop our model assuming normal or binomial likelihood and accounting for exposures grouped in clusters. To allow maximum flexibility, the dose-response association is modelled using restricted cubic splines. We implement these models in R using JAGS and we compare our approach to the one-stage dose-response meta-analysis model in a simulation study. We found that the Bayesian dose-response model with binomial likelihood has lower bias than the Bayesian model with normal likelihood and the frequentist one-stage model when studies have small sample size. When the true underlying shape is log-log or half-sigmoid, the performance of all models depends on choosing an appropriate location for the knots. In all other examined situations, all models perform very well and give practically identical results. We also re-analyze the data from 60 randomized controlled trials (15,984 participants) examining the efficacy (response) of various doses of serotonin-specific reuptake inhibitor (SSRI) antidepressant drugs. All models suggest that the dose-response curve increases between zero dose and 30-40 mg of fluoxetine-equivalent dose, and thereafter shows small decline. We draw the same conclusion when we take into account the fact that five different antidepressants have been studied in the included trials. We show that implementation of the hierarchical model in Bayesian framework has similar performance to, but overcomes some of the limitations of the frequentist approach and offers maximum flexibility to accommodate features of the data.

Investigation of solar noise impact on the performance of underwater wireless optical communication links.

We investigate the effect of environmental noise, caused by solar radiations under water, on the performance of underwater wireless optical communication (UWOC) systems. Presenting an analytical and generic model for this noise, we examine its impact on the link performance in terms of the bit error rate (BER). This study is conducted for different photo-detector types in the aim of highlighting practical limitations of establishing UWOC links in the presence of subsea solar noise. We show how the solar noise can impact the performance of UWOC links for relatively low operation depths. The results we present provide valuable insight for the design of UWOC links, which are likely to be established at relatively low depths. They can be exploited not only for the purpose of practical UWOC system deployment but also for in-pool experimental set-ups, since they elucidate the effect of ambient light on the measurements.