Early Treatment with Pegylated Interferon Lambda for Covid-19.

BACKGROUND: The efficacy of a single dose of pegylated interferon lambda in preventing clinical events among outpatients with acute symptomatic coronavirus disease 2019 (Covid-19) is unclear. METHODS: We conducted a randomized, controlled, adaptive platform trial involving predominantly vaccinated adults with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Brazil and Canada. Outpatients who presented with an acute clinical condition consistent with Covid-19 within 7 days after the onset of symptoms received either pegylated interferon lambda (single subcutaneous injection, 180 µg) or placebo (single injection or oral). The primary composite outcome was hospitalization (or transfer to a tertiary hospital) or an emergency department visit (observation for >6 hours) due to Covid-19 within 28 days after randomization. RESULTS: A total of 933 patients were assigned to receive pegylated interferon lambda (2 were subsequently excluded owing to protocol deviations) and 1018 were assigned to receive placebo. Overall, 83% of the patients had been vaccinated, and during the trial, multiple SARS-CoV-2 variants had emerged. A total of 25 of 931 patients (2.7%) in the interferon group had a primary-outcome event, as compared with 57 of 1018 (5.6%) in the placebo group, a difference of 51% (relative risk, 0.49; 95% Bayesian credible interval, 0.30 to 0.76; posterior probability of superiority to placebo, >99.9%). Results were generally consistent in analyses of secondary outcomes, including time to hospitalization for Covid-19 (hazard ratio, 0.57; 95% Bayesian credible interval, 0.33 to 0.95) and Covid-19-related hospitalization or death (hazard ratio, 0.59; 95% Bayesian credible interval, 0.35 to 0.97). The effects were consistent across dominant variants and independent of vaccination status. Among patients with a high viral load at baseline, those who received pegylated interferon lambda had lower viral loads by day 7 than those who received placebo. The incidence of adverse events was similar in the two groups. CONCLUSIONS: Among predominantly vaccinated outpatients with Covid-19, the incidence of hospitalization or an emergency department visit (observation for >6 hours) was significantly lower among those who received a single dose of pegylated interferon lambda than among those who received placebo. (Funded by FastGrants and others; TOGETHER ClinicalTrials.gov number, NCT04727424.).

Effect of Early Treatment with Ivermectin among Patients with Covid-19.

BACKGROUND: The efficacy of ivermectin in preventing hospitalization or extended observation in an emergency setting among outpatients with acutely symptomatic coronavirus disease 2019 (Covid-19), the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is unclear. METHODS: We conducted a double-blind, randomized, placebo-controlled, adaptive platform trial involving symptomatic SARS-CoV-2-positive adults recruited from 12 public health clinics in Brazil. Patients who had had symptoms of Covid-19 for up to 7 days and had at least one risk factor for disease progression were randomly assigned to receive ivermectin (400 µg per kilogram of body weight) once daily for 3 days or placebo. (The trial also involved other interventions that are not reported here.) The primary composite outcome was hospitalization due to Covid-19 within 28 days after randomization or an emergency department visit due to clinical worsening of Covid-19 (defined as the participant remaining under observation for >6 hours) within 28 days after randomization. RESULTS: A total of 3515 patients were randomly assigned to receive ivermectin (679 patients), placebo (679), or another intervention (2157). Overall, 100 patients (14.7%) in the ivermectin group had a primary-outcome event, as compared with 111 (16.3%) in the placebo group (relative risk, 0.90; 95% Bayesian credible interval, 0.70 to 1.16). Of the 211 primary-outcome events, 171 (81.0%) were hospital admissions. Findings were similar to the primary analysis in a modified intention-to-treat analysis that included only patients who received at least one dose of ivermectin or placebo (relative risk, 0.89; 95% Bayesian credible interval, 0.69 to 1.15) and in a per-protocol analysis that included only patients who reported 100% adherence to the assigned regimen (relative risk, 0.94; 95% Bayesian credible interval, 0.67 to 1.35). There were no significant effects of ivermectin use on secondary outcomes or adverse events. CONCLUSIONS: Treatment with ivermectin did not result in a lower incidence of medical admission to a hospital due to progression of Covid-19 or of prolonged emergency department observation among outpatients with an early diagnosis of Covid-19. (Funded by FastGrants and the Rainwater Charitable Foundation; TOGETHER ClinicalTrials.gov number, NCT04727424.).

Adaptive designs in public health: Vaccine and cluster randomized trials go Bayesian.

Clinical trials in public health-particularly those conducted in low- and middle-income countries-often involve communicable and non-communicable diseases with high disease burden and unmet needs. Trials conducted in these regions often are faced with resource limitations, so improving the efficiencies of these trials is critical. Adaptive trial designs have the potential to save trial time and resources and reduce the number of patients receiving ineffective interventions. In this paper, we provide a detailed account of the implementation of vaccine and cluster randomized trials within the framework of Bayesian adaptive trials, with emphasis on computational efficiency and flexibility with regard to stopping rules and allocation ratios. We offer an educated approach to selecting prior distributions and a data-driven empirical Bayes method for plug-in estimates for nuisance parameters.

Oral Fluvoxamine With Inhaled Budesonide for Treatment of Early-Onset COVID-19 : A Randomized Platform Trial.

BACKGROUND: Previous trials have demonstrated the effects of fluvoxamine alone and inhaled budesonide alone for prevention of disease progression among outpatients with COVID-19. OBJECTIVE: To determine whether the combination of fluvoxamine and inhaled budesonide would increase treatment effects in a highly vaccinated population. DESIGN: Randomized, placebo-controlled, adaptive platform trial. (ClinicalTrials.gov: NCT04727424). SETTING: 12 clinical sites in Brazil. PARTICIPANTS: Symptomatic adults with confirmed SARS-CoV-2 infection and a known risk factor for progression to severe disease. INTERVENTION: Patients were randomly assigned to either fluvoxamine (100 mg twice daily for 10 days) plus inhaled budesonide (800 mcg twice daily for 10 days) or matching placebos. MEASUREMENTS: The primary outcome was a composite of emergency setting retention for COVID-19 for more than 6 hours, hospitalization, and/or suspected complications due to clinical progression of COVID-19 within 28 days of randomization. Secondary outcomes included health care attendance (defined as hospitalization for any cause or emergency department visit lasting >6 hours), time to hospitalization, mortality, patient-reported outcomes, and adverse drug reactions. RESULTS: Randomization occurred from 15 January to 6 July 2022. A total of 738 participants were allocated to oral fluvoxamine plus inhaled budesonide, and 738 received placebo. The proportion of patients observed in an emergency setting for COVID-19 for more than 6 hours or hospitalized due to COVID-19 was lower in the treatment group than the placebo group (1.8% [95% credible interval {CrI}, 1.1% to 3.0%] vs. 3.7% [95% CrI, 2.5% to 5.3%]; relative risk, 0.50 [95% CrI, 0.25 to 0.92]), with a probability of superiority of 98.7%. No relative effects were found between groups for any of the secondary outcomes. More adverse events occurred in the intervention group than the placebo group, but no important differences between the groups were detected. LIMITATION: Low event rate overall, consistent with contemporary trials in vaccinated populations. CONCLUSION: Treatment with oral fluvoxamine plus inhaled budesonide among high-risk outpatients with early COVID-19 reduced the incidence of severe disease requiring advanced care. PRIMARY FUNDING SOURCE: Latona Foundation, FastGrants, and Rainwater Charitable Foundation.

Assessing the performance of group-based trajectory modeling method to discover different patterns of medication adherence.

It is well known that medication adherence is critical to patient outcomes and can decrease patient mortality. The Pharmacy Quality Alliance (PQA) has recognized and identified medication adherence as an important indicator of medication-use quality. Hence, there is a need to use the right methods to assess medication adherence. The PQA has endorsed the proportion of days covered (PDC) as the primary method of measuring adherence. Although easy to calculate, the PDC has however several drawbacks as a method of measuring adherence. PDC is a deterministic approach that cannot capture the complexity of a dynamic phenomenon. Group-based trajectory modeling (GBTM) is increasingly proposed as an alternative to capture heterogeneity in medication adherence. The main goal of this paper is to demonstrate, through a simulation study, the ability of GBTM to capture treatment adherence when compared to its deterministic PDC analogue and to the nonparametric longitudinal K-means. A time-varying treatment was generated as a quadratic function of time, baseline, and time-varying covariates. Three trajectory models are considered combining a cat's cradle effect, and a rainbow effect. The performance of GBTM was compared to the PDC and longitudinal K-means using the absolute bias, the variance, the c-statistics, the relative bias, and the relative variance. For all explored scenarios, we find that GBTM performed better in capturing different patterns of medication adherence with lower relative bias and variance even under model misspecification than PDC and longitudinal K-means.

Alone, together: On the benefits of Bayesian borrowing in a meta-analytic setting.

It is common practice to use hierarchical Bayesian model for the informing of a pediatric randomized controlled trial (RCT) by adult data, using a prespecified borrowing fraction parameter (BFP). This implicitly assumes that the BFP is intuitive and corresponds to the degree of similarity between the populations. Generalizing this model to any K ≥ 1 historical studies, naturally leads to empirical Bayes meta-analysis. In this paper we calculate the Bayesian BFPs and study the factors that drive them. We prove that simultaneous mean squared error reduction relative to an uninformed model is always achievable through application of this model. Power and sample size calculations for a future RCT, designed to be informed by multiple external RCTs, are also provided. Potential applications include inference on treatment efficacy from independent trials involving either heterogeneous patient populations or different therapies from a common class.

Long-Term Effect of Group Support Psychotherapy on Depression and HIV Treatment Outcomes: Secondary Analysis of a Cluster Randomized Trial in Uganda.

OBJECTIVE: We aimed to determine the effect of group support psychotherapy (GSP) compared with group HIV education (GHE) on depression and HIV treatment outcomes 24 months after treatment. We further aimed to investigate the mediating role of depression and antiretroviral therapy (ART) adherence in the relationship between GSP and viral load suppression. METHODS: Thirty HIV clinics across three districts were randomly assigned to deliver either GSP or GHE for depression. Depression and optimal (≥95%) ART adherence was assessed at baseline and 6, 12, 18, and 24 months after treatment. Viral load was drawn from the medical charts at baseline and 12 and 24 months after treatment. Multilevel mixed-effects regression models and generalized structural equation modeling were used to estimate 24-month outcomes and mediation effects. RESULTS: Participants ( N = 1140) were enrolled from HIV clinics offering either GSP ( n = 578 [51%]) or GHE ( n = 562 [49%]). Fewer GSP than GHE participants met the criteria for depression at 24 months after treatment (1% versus 25%; adjusted odds ratio [aOR] = 0.002, 95% confidence interval [CI] = 0.0002-0.018). More GSP than GHE participants reported optimal (≥95%) ART adherence (96% versus 88%; aOR = 20.88, 95% CI = 5.78-75.33) and improved viral suppression (96% versus 88%; aOR = 3.38, 95% CI = 1.02-11.02). The indirect effects of GSP through sequential reduction in depression and improvement in ART adherence at 12 months may partially explain the higher viral suppression rates at 24 months in GSP than GHE groups. CONCLUSION: In settings where the HIV epidemic persists, depression treatment with GSP may be critical for optimal HIV treatment outcomes.Trial Registration: The Pan African Clinical Trials Registry, number PACTR201608001738234.

Utilizing Bayesian predictive power in clinical trial design.

The Bayesian paradigm provides an ideal platform to update uncertainties and carry them over into the future in the presence of data. Bayesian predictive power (BPP) reflects our belief in the eventual success of a clinical trial to meet its goals. In this paper we derive mathematical expressions for the most common types of outcomes, to make the BPP accessible to practitioners, facilitate fast computations in adaptive trial design simulations that use interim futility monitoring, and propose an organized BPP-based phase II-to-phase III design framework.

Matched vs Nonmatched Placebos in a Randomized Trial of COVID-19 Treatments.

Importance: Matched placebo interventions are complex and resource intensive. Recent evidence suggests matched placebos may not always be necessary. Previous studies have predominantly evaluated potential bias of nonmatched placebos (ie, differing on dose, frequency of administration, or formulation) in pain and mental health, but to date no systematic examination has been conducted in infectious disease. Objective: To test for differences between nonmatched and matched placebo arms with respect to clinical outcome measures across multiple therapeutics for COVID-19. Design, Setting, and Participants: In a comparative effectiveness research study, a post hoc analysis was conducted of data on individual patients enrolled in a large, multiarm, platform randomized clinical trial in symptomatic adult outpatients with COVID-19 between January 15, 2021, to September 28, 2023, in which the outcomes of both matched and nonmatched placebo groups were reported. Bayesian and frequentist covariate-adjusted techniques were compared with 7 intervention-placebo pairs. Exposures: Seven matched and nonmatched placebo pairs (for a total of 7 comparisons) were evaluated throughout the primary platform trial. Comparisons were made between treatment and its associated matched (concurrent) placebo, as well as with nonmatched placebo (alone and in combination) assessed at a similar time point. Main Outcomes and Measures: Outcomes assessed included hospitalizations, EuroQol 5-Dimension 5-level scores, and PROMIS Global-10 scores. Results: A total of 7 intervention-control pairs (N = 2684) were assessed, including 1620 (60.4%) women, with mean (SD) age, 47 (15.2) years; the most common comorbidities were obesity (41.9%) and hypertension (37.9%). In a meta-analysis with decoupled SEs, accounting for overlapping placebo patients, the overall odds ratio (OR) of nonmatched compared with matched placebo was 1.01 (95% credible interval, 0.77-1.32), with posterior probability of equivalence, defined as 0.8 ≤ OR ≤ 1.2 (a deviation from perfect equivalence ie, OR = 1, by no more than 0.2) of 85.4%, implying no significant difference. Unadjusted analysis of the event rate difference between all nonmatched and matched placebo groups did not identify any notable differences across all 7 treatment-placebo combinations assessed. Similar analysis that was conducted for patient-reported quality of life outcomes did not yield statistically significant differences. Conclusions and Relevance: In this post hoc study of a randomized clinical platform trial, pooling matched and nonmatched placebo patient data did not lead to inconsistencies in treatment effect estimation for any of the investigational drugs. These findings may have significant implications for future platform trials, as the use of nonmatched placebo may improve statistical power, or reduce barriers to placebo implementation.

Effect of spirulina on risk of hospitalization among patients with COVID-19: the TOGETHER randomized trial.

BACKGROUND: Algae-derived nutraceuticals, such as spirulina, have been reported to have biological activities that may minimize clinical consequences to COVID-19 infections. OBJECTIVES: This study aimed to determine whether spirulina is an effective treatment for high-risk patients with early COVID-19 in an outpatient setting. METHODS: The TOGETHER trial is a placebo-controlled, randomized, platform trial conducted in Brazil. Eligible participants were symptomatic adults with a positive rapid test for SARS-CoV-2 older than 50 y or with a known risk factor for disease severity. Patients were randomly assigned to receive placebo or spirulina (1 g twice daily for 14 d). The primary end point was hospitalization defined as either retention in a COVID-19 emergency setting for >6 h or transfer to tertiary hospital owing to COVID-19 at 28 d. Secondary outcomes included time-to-hospitalization, mortality, and adverse drug reactions. We used a Bayesian framework to compare spirulina with placebo. RESULTS: We recruited 1126 participants, 569 randomly assigned to spirulina and 557 to placebo. The median age was 49.0 y, and 65.3% were female. The primary outcome occurred in 11.2% in the spirulina group and 8.1% in the placebo group (odds ratio [OR]: 1.24; 95% credible interval: 0.84, 1.86). There were no differences in emergency department visit (OR: 1.21; 95% credible interval: 0.81, 1.83), nor time to symptom relief (hazard ratio: 0.90; 95% credible interval: 0.79, 1.03). Spirulina also not demonstrate important treatment effects in the prespecified subgroups defined by age, sex, BMI, days since symptom onset, or vaccination status. CONCLUSIONS: Spirulina has no any clinical benefits as an outpatient therapy for COVID-19 compared with placebo with respect to reducing the retention in an emergency setting or COVID-19-related hospitalization. There are no differences between spirulina and placebo for other secondary outcomes. This trial was registered at clinicaltrials.gov as NCT04727424.

Network meta-interpolation: Effect modification adjustment in network meta-analysis using subgroup analyses.

Effect modification (EM) may cause bias in network meta-analysis (NMA). Existing population adjustment NMA methods use individual patient data to adjust for EM but disregard available subgroup information from aggregated data in the evidence network. Additionally, these methods often rely on the shared effect modification (SEM) assumption. In this paper, we propose Network Meta-Interpolation (NMI): a method using subgroup analyses to adjust for EM that does not assume SEM. NMI balances effect modifiers across studies by turning treatment effect (TE) estimates at the subgroup- and study level into TE and standard errors at EM values common to all studies. In an extensive simulation study, we simulate two evidence networks consisting of four treatments, and assess the impact of departure from the SEM assumption, variable EM correlation across trials, trial sample size and network size. NMI was compared to standard NMA, network meta-regression (NMR) and Multilevel NMR (ML-NMR) in terms of estimation accuracy and credible interval (CrI) coverage. In the base case non-SEM dataset, NMI achieved the highest estimation accuracy with root mean squared error (RMSE) of 0.228, followed by standard NMA (0.241), ML-NMR (0.447) and NMR (0.541). In the SEM dataset, NMI was again the most accurate method with RMSE of 0.222, followed by ML-NMR (0.255). CrI coverage followed a similar pattern. NMI's dominance in terms of estimation accuracy and CrI coverage appeared to be consistent across all scenarios. NMI represents an effective option for NMA in the presence of study imbalance and available subgroup data.

Bayesian hierarchical model-based network meta-analysis to overcome survival extrapolation challenges caused by data immaturity.

Aim: This research evaluated standard Weibull mixture cure (WMC) network meta-analysis (NMA) with Bayesian hierarchical (BH) WMC NMA to inform long-term survival of therapies. Materials & methods: Four trials in previously treated metastatic non-small-cell lung cancer with PD-L1 >1% were used comparing docetaxel with nivolumab, pembrolizumab and atezolizumab. Cure parameters related to a certain treatment class were assumed to share a common distribution. Results: Standard WMC NMA predicted cure rates were 0.03 (0.01; 0.07), 0.18 (0.12; 0.24), 0.07 (0.02; 0.15) and 0.03 (0.00; 0.09) for docetaxel, nivolumab, pembrolizumab and atezolizumab, respectively, with corresponding incremental life years (LY) of 3.11 (1.65; 4.66), 1.06 (0.41; 2.37) and 0.42 (-0.57; 1.68). The Bayesian hierarchical-WMC-NMA rates were 0.06 (0.03; 0.10), 0.17 (0.11; 0.23), 0.12 (0.05; 0.20) and 0.12 (0.03; 0.23), respectively, with incremental LY of 2.35 (1.04; 3.93), 1.67 (0.68; 2.96) and 1.36 (-0.05; 3.64). Conclusion: BH-WMC-NMA impacts incremental mean LYs and cost-effectiveness ratios, potentially affecting reimbursement decisions.

The importance of appropriate selection of clinical endpoints in outpatient COVID-19 clinical trials.

Clinical trial endpoints must be carefully and intentionally selected so that the results of the trial can be used to inform policy- and decision-making. The relative importance of potential endpoints often depends on the stakeholder, with patients having different preferences to policymakers and regulators. The set up of clinical trials for COVID-19 was problematic, as endpoints that could be reasonably measured did not always match the efficacy endpoints usually required by guideline panels. Thus, different endpoints were used, which made the timely comparison and evaluation of interventions difficult. Here we discuss the evolution of the COVID-19 landscape and the effect this is having on the selection of consistent and measurable clinical trial endpoints. Using appropriate endpoints is crucial for researchers to offer the most reliable, valid, and interpretable results possible.

Fluvoxamine for the Early Treatment of COVID-19: A Meta-analysis of Randomized Clinical Trials.

Fluvoxamine is widely prescribed as an antidepressant. Recent studies show the drug may have a clinical benefit in treating COVID-19. We aimed to perform a meta-analysis of the existing randomized trials of fluvoxamine compared with placebo on the early treatment of COVID-19 patients. We included only randomized clinical trials enrolling ambulatory patients with early-stage disease (symptoms > 7 days) for the prevention of hospitalization. We searched MEDLINE, and clinicaltrials.gov databases to identify trials and extract data with clarifications from the study investigators. We performed a fixed-effects meta-analysis and sensitivity analyses via R to evaluate the pooled estimate of hospitalization. We included three randomized trials: STOP COVID 1 and 2, and the TOGETHER Trial. The studies included a total of 2,196 patients. The STOP COVID trials measured clinical deterioration whereas the TOGETHER Trial measured hospitalization as the primary outcome. All trials reported on hospitalization up to day 28. The meta-analysis results show that patients receiving fluvoxamine were 31% less likely to experience clinical deterioration or hospitalization compared with placebo (risk ratio, 0.69; 95% CI, 0.54-0.88). A sensitivity analysis using the definition of hospitalization resulted in a risk reduction of 21% (95% CI, 0.60-1.03). Data from three randomized controlled trials show that fluvoxamine was associated with a reduction in the primary outcome measure (either clinical deterioration or composite outcome of hospitalization or extended emergency setting observation), although analysis of hospitalization-only was not statistically significant. More evidence from future trials is still needed to support the findings of this meta-analysis.

Economic Evaluation of Cost and Time Required for a Platform Trial vs Conventional Trials.

Importance: Platform trial design allows the introduction of new interventions after the trial is initiated and offers efficiencies to clinical research. However, limited guidance exists on the economic resources required to establish and maintain platform trials. Objective: To compare cost (US dollars) and time requirements of conducting a platform trial vs a series of conventional (nonplatform) trials using a real-life example. Design, Setting, and Participants: For this economic evaluation, an online survey was administered to a group of international experts (146 participants) with publication records of platform trials to elicit their opinions on cost and time to set up and conduct platform, multigroup, and 2-group trials. Using the reported entry dates of 10 interventions into Systemic Therapy in Advancing Metastatic Prostate Cancer: Evaluation of Drug Efficacy, the longest ongoing platform trial, 3 scenarios were designed involving a single platform trial (scenario 1), 1 multigroup followed by 5 2-group trials (scenario 2), and a series of 10 2-group trials (scenario 3). All scenarios started with 5 interventions, then 5 more interventions were either added to the platform or evaluated independently. Simulations with the survey results as inputs were used to compare the platform vs conventional trial designs. Data were analyzed from July to September 2021. Exposure: Platform trial design. Main Outcomes and Measures: Total trial setup and conduct cost and cumulative duration. Results: Although setup time and cost requirements of a single trial were highest for the platform trial, cumulative requirements of setting up a series of multiple trials in scenarios 2 and 3 were larger. Compared with the platform trial, there was a median (IQR) increase of 216.7% (202.2%-242.5%) in cumulative setup costs for scenario 2 and 391.1% (365.3%-437.9%) for scenario 3. In terms of total cost, there was a median (IQR) increase of 17.4% (12.1%-22.5%) for scenario 2 and 57.5% (43.1%-69.9%) for scenario 3. There was a median (IQR) increase in cumulative trial duration of 171.1% (158.3%-184.3%) for scenario 2 and 311.9% (282.0%-349.1%) for scenario 3. Cost and time reductions in the platform trial were observed in both the initial and subsequently evaluated interventions. Conclusions and Relevance: Although setting up platform trials can take longer and be costly, the findings of this study suggest that having a single infrastructure can improve efficiencies with respect to costs and efforts.

Effect of early treatment with metformin on risk of emergency care and hospitalization among patients with COVID-19: The TOGETHER randomized platform clinical trial.

BACKGROUND: Observational studies have postulated a therapeutic role of metformin in treating COVID-19. We conducted an adaptive platform clinical trial to determine whether metformin is an effective treatment for high-risk patients with early COVID-19 in an outpatient setting. METHODS: The TOGETHER Trial is a placebo-controled, randomized, platform clinical trial conducted in Brazil. Eligible participants were symptomatic adults with a positive antigen test for SARS-CoV-2. We enroled eligible patients over the age of 50 years or with a known risk factor for disease severity. Patients were randomly assigned to receive either placebo or metformin (750 mg twice daily for 10 days or placebo, twice daily for 10 days). The primary outcome was hospitalization defined as either retention in a COVID-19 emergency setting for > 6 h or transfer to tertiary hospital due to COVID-19 at 28 days post randomization. Secondary outcomes included viral clearance at day 7, time to hospitalization, mortality, and adverse drug reactions. We used a Bayesian framework to determine probability of success of the intervention compared to placebo. FINDINGS: The TOGETHER Trial was initiated June 2, 2020. We randomized patients to metformin starting January 15, 2021. On April 3, 2021, the Data and Safety Monitoring Committee recommended stopping enrollment into the metformin arm due to futility. We recruited 418 participants, 215 were randomized to the metformin arm and 203 to the placebo arm. More than half of participants (56.0%) were over the age of 50 years and 57.2% were female. Median age was 52 years. The proportion of patients with the primary outcome at 28 days was not different between the metformin and placebo group (relative risk [RR] 1.14[95% Credible Interval 0.73; 1.81]), probability of superiority 0.28. We found no significant differences between the metformin and placebo group on viral clearance through to day 7 (Odds ratio [OR], 0.99, 95% Confidence Intervals 0.88-1.11) or other secondary outcomes. INTERPRETATION: In this randomized trial, metformin did not provide any clinical benefit to ambulatory patients with COVID-19 compared to placebo, with respect to reducing the need for retention in an emergency setting or hospitalization due to worsening COVID-19. There were also no differences between metformin and placebo observed for other secondary clinical outcomes.

Effect of early treatment with fluvoxamine on risk of emergency care and hospitalisation among patients with COVID-19: the TOGETHER randomised, platform clinical trial.

BACKGROUND: Recent evidence indicates a potential therapeutic role of fluvoxamine for COVID-19. In the TOGETHER trial for acutely symptomatic patients with COVID-19, we aimed to assess the efficacy of fluvoxamine versus placebo in preventing hospitalisation defined as either retention in a COVID-19 emergency setting or transfer to a tertiary hospital due to COVID-19. METHODS: This placebo-controlled, randomised, adaptive platform trial done among high-risk symptomatic Brazilian adults confirmed positive for SARS-CoV-2 included eligible patients from 11 clinical sites in Brazil with a known risk factor for progression to severe disease. Patients were randomly assigned (1:1) to either fluvoxamine (100 mg twice daily for 10 days) or placebo (or other treatment groups not reported here). The trial team, site staff, and patients were masked to treatment allocation. Our primary outcome was a composite endpoint of hospitalisation defined as either retention in a COVID-19 emergency setting or transfer to tertiary hospital due to COVID-19 up to 28 days post-random assignment on the basis of intention to treat. Modified intention to treat explored patients receiving at least 24 h of treatment before a primary outcome event and per-protocol analysis explored patients with a high level adherence (>80%). We used a Bayesian analytic framework to establish the effects along with probability of success of intervention compared with placebo. The trial is registered at ClinicalTrials.gov (NCT04727424) and is ongoing. FINDINGS: The study team screened 9803 potential participants for this trial. The trial was initiated on June 2, 2020, with the current protocol reporting randomisation to fluvoxamine from Jan 20 to Aug 5, 2021, when the trial arms were stopped for superiority. 741 patients were allocated to fluvoxamine and 756 to placebo. The average age of participants was 50 years (range 18-102 years); 58% were female. The proportion of patients observed in a COVID-19 emergency setting for more than 6 h or transferred to a teritary hospital due to COVID-19 was lower for the fluvoxamine group compared with placebo (79 [11%] of 741 vs 119 [16%] of 756); relative risk [RR] 0·68; 95% Bayesian credible interval [95% BCI]: 0·52-0·88), with a probability of superiority of 99·8% surpassing the prespecified superiority threshold of 97·6% (risk difference 5·0%). Of the composite primary outcome events, 87% were hospitalisations. Findings for the primary outcome were similar for the modified intention-to-treat analysis (RR 0·69, 95% BCI 0·53-0·90) and larger in the per-protocol analysis (RR 0·34, 95% BCI, 0·21-0·54). There were 17 deaths in the fluvoxamine group and 25 deaths in the placebo group in the primary intention-to-treat analysis (odds ratio [OR] 0·68, 95% CI: 0·36-1·27). There was one death in the fluvoxamine group and 12 in the placebo group for the per-protocol population (OR 0·09; 95% CI 0·01-0·47). We found no significant differences in number of treatment emergent adverse events among patients in the fluvoxamine and placebo groups. INTERPRETATION: Treatment with fluvoxamine (100 mg twice daily for 10 days) among high-risk outpatients with early diagnosed COVID-19 reduced the need for hospitalisation defined as retention in a COVID-19 emergency setting or transfer to a tertiary hospital. FUNDING: FastGrants and The Rainwater Charitable Foundation. TRANSLATION: For the Portuguese translation of the abstract see Supplementary Materials section.

Reporting and design of randomized controlled trials for COVID-19: A systematic review.

BACKGROUND: The novel coronavirus 2019 (COVID-19) pandemic has mobilized global research at an unprecedented scale. While challenges associated with the COVID-19 trial landscape have been discussed previously, no comprehensive reviews have been conducted to assess the reporting, design, and data sharing practices of randomized controlled trials (RCTs). PURPOSE: The purpose of this review was to gain insight into the current landscape of reporting, methodological design, and data sharing practices for COVID-19 RCTs. DATA SOURCES: We conducted three searches to identify registered clinical trials, peer-reviewed publications, and pre-print publications. STUDY SELECTION: After screening eight major trial registries and 7844 records, we identified 178 registered trials and 38 publications describing 35 trials, including 25 peer-reviewed publications and 13 pre-prints. DATA EXTRACTION: Trial ID, registry, location, population, intervention, control, study design, recruitment target, actual recruitment, outcomes, data sharing statement, and time of data sharing were extracted. DATA SYNTHESIS: Of 178 registered trials, 112 (62.92%) were in hospital settings, median planned recruitment was 100 participants (IQR: 60, 168), and the majority (n = 166, 93.26%) did not report results in their respective registries. Of 35 published trials, 31 (88.57%) were in hospital settings, median actual recruitment was 86 participants (IQR: 55.5, 218), 10 (28.57%) did not reach recruitment targets, and 27 trials (77.14%) reported plans to share data. CONCLUSIONS: The findings of our study highlight limitations in the design and reporting practices of COVID-19 RCTs and provide guidance towards more efficient reporting of trial results, greater diversity in patient settings, and more robust data sharing.

Effect of Early Treatment With Hydroxychloroquine or Lopinavir and Ritonavir on Risk of Hospitalization Among Patients With COVID-19: The TOGETHER Randomized Clinical Trial.

Importance: Data on the efficacy of hydroxychloroquine or lopinavir-ritonavir for the treatment of high-risk outpatients with COVID-19 in developing countries are needed. Objective: To determine whether hydroxychloroquine or lopinavir-ritonavir reduces hospitalization among high-risk patients with early symptomatic COVID-19 in an outpatient setting. Design, Setting, and Participants: This randomized clinical trial was conducted in Brazil. Recently symptomatic adults diagnosed with respiratory symptoms from SARS-CoV-2 infection were enrolled between June 2 and September 30, 2020. The planned sample size was 1476 patients, with interim analyses planned after 500 patients were enrolled. The trial was stopped after the interim analysis for futility with a sample size of 685 patients. Statistical analysis was performed in December 2020. Interventions: Patients were randomly assigned to hydroxychloroquine (800 mg loading dose, then 400 mg daily for 9 days), lopinavir-ritonavir (loading dose of 800 mg and 200 mg, respectively, every 12 hours followed by 400 mg and 100 mg, respectively, every 12 hours for the next 9 days), or placebo. Main Outcomes and Measures: The primary outcomes were COVID-19-associated hospitalization and death assessed at 90 days after randomization. COVID-19-associated hospitalization was analyzed with a Cox proportional hazards model. The trial included the following secondary outcomes: all-cause hospitalization, viral clearance, symptom resolution, and adverse events. Results: Of 685 participants, 632 (92.3%) self-identified as mixed-race, 377 (55.0%) were women, and the median (range) age was 53 (18-94) years. A total of 214 participants were randomized to hydroxychloroquine; 244, lopinavir-ritonavir; and 227, placebo. At first interim analysis, the data safety monitoring board recommended stopping enrollment of both hydroxychloroquine and lopinavir-ritonavir groups because of futility. The proportion of patients hospitalized for COVID-19 was 3.7% (8 participants) in the hydroxychloroquine group, 5.7% (14 participants) in the lopinavir-ritonavir group, and 4.8% (11 participants) in the placebo group. We found no significant differences between interventions for COVID-19-associated hospitalization (hydroxychloroquine: hazard ratio [HR], 0.76 [95% CI, 0.30-1.88]; lopinavir-ritonavir: HR, 1.16 [95% CI, 0.53-2.56] as well as for the secondary outcome of viral clearance through day 14 (hydroxychloroquine: odds ratio [OR], 0.91 [95% CI, 0.82-1.02]; lopinavir-ritonavir: OR, 1.04 [95% CI, 0.94-1.16]). At the end of the trial, there were 3 fatalities recorded, 1 in the placebo group and 2 in the lopinavir-ritonavir intervention group. Conclusions and Relevance: In this randomized clinical trial, neither hydroxychloroquine nor lopinavir-ritonavir showed any significant benefit for decreasing COVID-19-associated hospitalization or other secondary clinical outcomes. This trial suggests that expedient clinical trials can be implemented in low-income settings even during the COVID-19 pandemic. Trial Registration: ClinicalTrials.gov Identifier: NCT04403100.

The Efficacy and Safety of Piribedil Relative to Pramipexole for the Treatment of Early Parkinson Disease: A Systematic Literature Review and Network Meta-Analysis.

OBJECTIVES: Patients with early Parkinson disease (PD) frequently defer initiation of levodopa treatment to minimize long-term complications. Nonergoline dopamine agonists, such as pramipexole and piribedil, are frequent first-line therapies for early PD patients, yet limited head-to-head randomized controlled trial (RCT) evidence exists for dopamine agonists in this population. We therefore conducted a systematic literature review and network meta-analysis. METHODS: MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials were systematically searched (until January 7, 2020), identifying RCTs assessing the efficacy of piribedil or pramipexole in early PD. Eligible trial data were incorporated into fixed- and random-effects Bayesian network meta-analyses. RESULTS: No RCTs were identified directly comparing piribedil with pramipexole, but 6 trials provided data for pramipexole versus placebo and 2 compared piribedil versus placebo, facilitating indirect comparisons. Across all time points assessed, no significant differences were found between pramipexole and piribedil for change in the Unified Parkinson's Disease Rating Scale (UPDRS) score from baseline. Piribedil and pramipexole demonstrated superiority relative to placebo for UPDRS II/III change at weeks 22 to 30. No significant differences were noted between the treatments at weeks 20 to 35 for anxiety, constipation, hypotension, nausea, and somnolence. Sensitivity analyses on adjustment for dose titration periods and baseline risk yielded the same pattern of results. CONCLUSIONS: No significant differences were found for pramipexole versus piribedil in the UPDRS II/III scores from baseline in early PD, with similar safety profiles.