Expected Sample Size Research Articles

Bayesian single-to-double arm transition design using both short-term and long-term endpoints.

The choice between single-arm designs versus randomized double-arm designs has been contentiously debated in the literature of phase II oncology trials. Recently, as a compromise, the single-to-double arm transition design was proposed, combining the two designs into one trial over two stages. Successful implementation of the two-stage transition design requires a suspension period at the end of the first stage to collect the response data of the already enrolled patients. When the evaluation of the primary efficacy endpoint is overly long, the between-stage suspension period may unfavorably prolong the trial duration and cause a delay in treating future eligible patients. To accelerate the trial, we propose a Bayesian single-to-double arm design with short-term endpoints (BSDS), where an intermediate short-term endpoint is used for making early termination decisions at the end of the single-arm stage, followed by an evaluation of the long-term endpoint at the end of the subsequent double-arm stage. Bayesian posterior probabilities are used as the primary decision-making tool at the end of the trial. Design calibration steps are proposed for this Bayesian monitoring process to control the frequentist operating characteristics and minimize the expected sample size. Extensive simulation studies have demonstrated that our design has comparable power and average sample size but a much shorter trial duration than conventional single-to-double arm design. Applications of the design are illustrated using two phase II oncology trials with binary endpoints.

Response adaptive randomization design for a two-stage study with binary response

ABSTRACT Response adaptive randomization has the potential to treat more participants in better treatments in a trial to benefit participants. We propose optimal response adaptive randomization designs for a two-stage study with binary response, having the smallest expected sample size or the fewest expected number of failures. Equal randomization is used in the first stage, and data from the first stage is used to determine the adaptive sample size ratio in the second stage. In the proposed optimal designs, the type I error rate and the statistical power are calculated from the asymptotic normal distributions. The new designs that minimize the expected number of failures have the advantage over the existing optimal randomized designs to substantially reduce the number of failures.

A stable sequential multiple test for Koopman–Darmois family

Assuming that data are collected sequentially from multiple streams whose density functions belong to the Koopman–Darmois family, we implement simultaneous testing on multiple hypotheses with respect to parameters. To stabilize the expected sample sizes (ESSs) at all possible values of the true parameters, we intersect individual 2-SPRT plans and propose reasonable thresholds to balance stopping rules among streams. Under two types of constrained familywise error probabilities, we prove that our method has bounded maximum expected sample sizes (MESSs) and achieves asymptotic optimality in the sense of minimizing MESSs. Simulation results demonstrate the stability of our method, in the sense of achieving smaller MESSs than those of the baseline methods. We further apply our method to a real data set.

Building a Foundation for More Flexible A/B Testing: Applications of Interim Monitoring to Large Scale Data

The use of error spending functions and stopping rules has become a powerful tool for conducting interim analyses. The implementation of an interim analysis is broadly desired not only in traditional clinical trials but also in A/B tests. Although many papers have summarized error spending approaches, limited work has been done in the context of large-scale data that assists in finding the “optimal” boundary. In this paper, we summarized fifteen boundaries that consist of five error spending functions that allow early termination for futility, difference, or both, as well as a fixed sample size design without interim monitoring. The simulation is based on a practical A/B testing problem comparing two independent proportions. We examine sample sizes across a range of values from 500 to 250,000 per arm to reflect different settings where A/B testing may be utilized. The choices of optimal boundaries are summarized using a proposed loss function that incorporates different weights for the expected sample size under a null experiment with no difference between variants, the expected sample size under an experiment with a difference in the variants, and the maximum sample size needed if the A/B test did not stop early at an interim analysis. The results are presented for simulation settings based on adequately powered, under-powered, and over-powered designs with recommendations for selecting the “optimal” design in each setting.

Optimization of the two-stage group sequential three-arm gold-standard design for non-inferiority trials.

If design parameters are chosen appropriately, group sequential trial designs are known to be able to reduce the expected sample size under the alternative hypothesis compared to single-stage designs. The same holds true for the so-called 'gold-standard' design for non-inferiority trials, a design involving an experimental group, an active control group, and a placebo group. However, choosing design parameters that maximize the advantages of a two-stage approach for the three-arm gold-standard design for non-inferiority trials is not a straightforward task. In particular, optimal choices of futility boundaries for this design have not been thoroughly discussed in existing literature. We present a variation of the hierarchical testing procedure, which allows for the incorporation of binding futility boundaries at interim analyses. We show that this procedure maintains strong control of the family-wise type I error rate. Within this framework, we consider the futility and efficacy boundaries as well as the sample size allocation ratios as optimization parameters. This allows the investigation of the efficiency gain from including the option to stop for futility in addition to the ability to stop for efficacy. To analyze the extended designs, optimality criteria that include the design's performance under the alternative as well as the null hypothesis are introduced. On top of this, we discuss methods to limit the allocation of placebo patients in the trial while maintaining relatively good operating characteristics. The results of our numerical optimization procedure are discussed and a comparison of different approaches to designing a three-arm gold-standard non-inferiority trial is provided.

A survey of registered pharmacological clinical trials on rare neurological diseases in children in 2010-2020.

To clarify the current state of methodology of clinical trials for rare neurological diseases in children, and to provide a basis for the further optimization of the trial design. Data of clinical trials for the rare neurological diseases with childhood onset (searched through https://rarediseases.info.nih.gov/diseases and www.Orpha.net) registered on the Clinicaltrils.gov from January 2010 to June 2020 was collected. Analysis on the methodology of the clinical trials were performed, focusing on initiator of the studies, multi or single research center, study design, sample size, and the endpoint using in the trial. A total of 162 clinical trials were included, covering only 7.3% (61/835) of rare neurological diseases in children. 101 (62.3%) were initiated by pharmaceutical companies, and 61 (37.7%) by investigators. Most (95.4%) of global multicenter studies were initiated by pharmaceutical companies, whereas most (70.0%) of single-center studies were initiated by investigators (χ 2 = 61.635, P < 0.001). Of the 162 trials, 74 (45.7%) were open-label single-arm trials, 68 (42.0%) were randomized double-blind parallel controlled trials (RCT), 12 (7.4%) were randomized crossover trials. Most of RCTs (73.5%) and 54.1% of open-label single-arm trials were initiated by pharmaceutical companies. The proportion of RCTs in clinical trials for diseases with a prevalence of ≥1/10,000 (62.5%) was higher than that in diseases with prevalence ≤1/1,000,000 (12.0%) or 1/1,000,000~1/10,000 (43.1%) (χ 2 = 14.790, P = 0.001). The median expected sample size of the studies was 34 (4-500). 132 (132/162, 81.5%) studies enrolled fewer than 100 cases. Diseases with a prevalence of ≥1/10,000 had significantly larger sample sizes than other prevalence classes (P < 0.001, P = 0.003). There were few clinical trials targeting on treatment of rare neurological diseases in children. Trials on rare diseases used fewer participants, and high-quality randomized controlled trials were less common. It is necessary to conduct global multicenter recruitment and choose optimal study designs to improve the level of evidence in clinical trials on rare diseases.

69 Triage Administration of Ondansetron for Gastroenteritis in children; a randomized controlled trial

Abstract Background Acute gastroenteritis is an important reason for emergency department (ED) consultation in children. Ondansetron is reported to be effective in reducing vomiting in children with gastroenteritis, leading to less intravenous rehydration and hospital admission. Objectives The aim of this study was to assess the effectiveness of triage nurse-initiated administration of ondansetron for children with suspected gastroenteritis in the paediatric ED to reduce the number of patients requiring observation following the first physician assessment. Design/Methods This was a randomized controlled trial performed in a tertiary care paediatric ED. All children 6 months to 17 years old who presented to the ED with at least four episodes of non-billous, non-bloody vomiting in the previous 24 hours and the last vomiting occurring within the previous 2h were eligible. The intervention consisted of administration of a liquid formulation of an adapted to weight dose of ondansetron at triage compared to a color- and taste-matched placebo. The primary outcome was the number of patients requiring observation after the first physician’s evaluation. Secondary outcomes were the number of episodes of vomiting after receiving the intervention, length of stay in the ED, comfort, and the proportion of children who returned for a medical visit within 48 hours. A sample size of 248 participants was initially identified to have a power of 90% to find a 20% difference in the proportion of children needing observation following physician evaluation. Results Because of multiple external factors, including the COVID-19 pandemic, recruitment was stopped before the expected sample size was reached. A total of 91 patients were included and randomized to receive ondansetron (n= 44) or a placebo (n=47) just after triage. The baseline characteristics of the participants was similar between the two groups. A total of 40 (45%) participants were discharged immediately after the first evaluation by the treating physician. This was similar for both groups 44% vs. 45%; (95% CI for the difference -20 to 19%). There was no difference between the two groups for the total length of stay (median 232 vs. 227 minutes; p= 0.677) and for the length of stay after being seen by the physician (72 vs. 68 minutes; p=0.821). There was no statistical difference between the two groups in the number of vomiting episodes (difference of 15%; 95% CI -2, 31), and proportion of participants needing a rescue medication (difference: 19%; 95%CI:-0.6 to 36%) or an intravenous rehydration (difference: 8; 95%CI:-6, 22). Conclusion This study failed to demonstrate any benefit in using ondansetron at triage for children with presumed gastroenteritis.

A two-stage design for comparing binomial treatments with a standard

We propose a two-stage selection and testing procedure for comparing success rates of several populations among each other and against a desired standard success rate to identify which treatment has the highest rate of success that is also higher than the standard. The design combines elements of both hypothesis testing and statistical selection. As a hybrid two-stage procedure, it allows for dropping the poorly performing treatments early on the basis of interim analysis results or for early termination if none of the experimental treatments seems promising. Because this procedure is not a pure hypothesis testing procedure, power and size are redefined to account for its hybrid nature. Using these definitions, we determine the design parameters for given size and power values. When multiple designs meet these requirements, we will recommend the set of design parameters that produces the lowest expected sample size.

Belimumab use during pregnancy: Interim results of the belimumab pregnancy registry.

Belimumab is approved for active, autoantibody-positive systemic lupus erythematosus (SLE) and lupus nephritis, but limited data exist regarding its use in pregnancy. The Belimumab Pregnancy Registry (BPR, GSK Study BEL114256; NCT01532310) was created to evaluate pregnancy and infant outcomes following belimumab exposure. Individuals with SLE exposed to belimumab from 4months before and/or during pregnancy can enroll into the BPR. The primary outcome is major birth defects; secondary outcomes include miscarriages, stillbirths, elective termination, pre-term birth, neonatal death, small for gestational age, and adverse infant outcomes during the first year of life. Belimumab exposure timing, concomitant medications, and other potential confounding factors are also collected. Data up to March 8, 2021, are reported descriptively. From an expected sample size target of 500 prospective pregnancies with a known outcome, only 55 were enrolled in the study. Among these, two pregnancy losses and 53 pregnancies with a live birth outcome were reported. Ten of the 53 live birth pregnancies resulted in a major birth defect. Ten pregnancies were enrolled after the pregnancy outcome occurred and were examined retrospectively (four live births with no defects, four miscarriages, and two elective terminations). There was no indication or pattern of birth defects associated with belimumab. Low recruitment numbers for the BPR and incomplete information limit the conclusions regarding belimumab exposure during pregnancy. There was no pattern or common mechanism of birth defects associated with belimumab within the BPR data.

Bounded-width confidence interval following optimal sequential analysis of adverse events with binary data.

In sequential testing with binary data, sample size and time to detect a signal are the key performance measures to optimize. While the former should be optimized in Phase III clinical trials, minimizing the latter is of major importance in post-market drug and vaccine safety surveillance of adverse events. The precision of the relative risk estimator on termination of the analysis is a meaningful design criterion as well. This paper presents a linear programming framework to find the optimal alpha spending that minimizes expected time to signal, or expected sample size as needed. The solution enables (a) to bound the width of the confidence interval following the end of the analysis, (b) designs with outer signaling thresholds and inner non-signaling thresholds, and (c) sequential designs with variable Bernoulli probabilities. To illustrate, we use real data on the monitoring of adverse events following the H1N1 vaccination. The numerical results are obtained using the R Sequential package.

Ecosystem Sustainability for Improved Refugees’ Livelihood: A Case of Kalobeyei Integrated Settlement, in Turkana County, Kenya

Ecosystem degradation in and around refugees’ settlements caused a dire problem for the livelihood of refugees. Therefore this study sought to examine the perceived relationship between ecosystem sustainability and refugees’ livelihood in Kalobeyei integrated settlement. The study used a mixed method and descriptive survey research design. The study employed Cochran's (1977) sample size computation for ordered categorical data at a 95 % confidence level and an acceptable error margin of ± 4.2 %, generating an expected sample size of 137. Quantitative data were collected using a questionnaire from among 80 participants, and qualitative data was collected by means of focus group discussions involving 48 respondents. Interviews were held among 9 key informants. Thus, the total sample size was 137. The results showed that respondents perceived a high impact of the ecosystem on their livelihood as such, there is an imbalance between utilization and conservation of the ecosystem. The respondents of the study are very optimistic that harmonization of ecosystem sustainability and refugees’ livelihood is possible in the Kalobeyei settlement. The study advocates for the use of sustained supervision of the dryland forests by applying quantifiable measures that monitor resource utilization and conservation and the implementation of the Kenyan refugee bill act 2019 passed in 2021. This advocacy for the sustainability of the ecosystem in order to improve the livelihood in Kalobeyei integrated settlement.

A Phase Ib Dose-finding Study of Panobinostat and Ruxolitinib in Myelofibrosis.

A Phase Ib Dose-finding Study of Panobinostat and Ruxolitinib in Myelofibrosis.

Continuous local anaesthetic wound infusion of bupivacaine for postoperative analgesia in neonates: a randomised control trial (CANWIN Study)

ObjectiveTo determine the effect of continuous wound infusion of local anaesthetic drug (bupivacaine) on total amount of systemic opioid use in the first 72 hours in newborn infants undergoing laparotomy.DesignA...

Effectiveness of a pay-it-forward intervention compared with user-paid vaccination to improve influenza vaccine uptake and community engagement among children and older adults in China: a quasi-experimental pragmatic trial

SummaryBackgroundChina has low seasonal influenza vaccination rates among priority populations. In this study, we aimed to evaluate a pay-it-forward strategy to increase influenza vaccine uptake in rural, suburban, and urban settings in China.MethodsWe performed a quasi-experimental pragmatic trial to examine the effectiveness of a pay-it-forward intervention (a free influenza vaccine and an opportunity to donate financially to support vaccination of other individuals) to increase influenza vaccine uptake compared with standard-of-care user-paid vaccination among children (aged between 6 months and 8 years) and older people (≥60 years) in China. Recruitment took place in the standard-of-care group until the expected sample size was reached and then in the pay-it-forward group in primary care clinics from a rural site (Yangshan), a suburban site (Zengcheng), and an urban site (Tianhe). Participants were introduced to the influenza vaccine by project staff using a pamphlet about influenza vaccination and were either asked to pay out-of-pocket at the standard market price (US$8·5–23·2; standard-of-care group) or to donate any amount anonymously (pay-it-forward group). Participants had to be eligible to receive an influenza vaccine and to have not received an influenza vaccine in the past year. The primary outcome was vaccine uptake. Secondary outcomes were vaccine confidence and costs (from the health-care provider perspective). Regression methods compared influenza vaccine uptake and vaccine confidence between the two groups. This trial is registered with ChiCTR, ChiCTR2000040048.FindingsFrom Sept 21, 2020, to March 3, 2021, 300 enrolees were recruited from patients visiting three primary care clinics. 55 (37%) of 150 people in the standard-of-care group (40 [53%] of 75 children and 15 [20%] of 75 older adults) and 111 (74%) of 150 in the pay-it-forward group (66 [88%] of 75 children and 45 [60%] of 75 older adults) received an influenza vaccine. People in the pay-it-forward group were more likely to receive an influenza vaccine compared with those in the standard-of-care group (adjusted odds ratio [aOR] 6·7 [95% CI 2·7–16·6] among children and 5·0 [2·3–10·8] among older adults). People in the pay-it-forward group had greater confidence in vaccine safety (aOR 2·2 [95% CI 1·2–3·9]), importance (3·1 [1·6–5·9]), and effectiveness (3·1 [1·7–5·7]). In the pay-it-forward group, 107 (96%) of 111 participants donated money for subsequent vaccinations. The pay-it-forward group had a lower economic cost (calculated as the cost without subtraction of donations) per person vaccinated (US$45·60) than did the standard-of-care group ($64·67).InterpretationThe pay-it-forward intervention seemed to be effective in improving influenza vaccine uptake and community engagement. Our data have implications for prosocial interventions to enhance influenza vaccine uptake in countries where influenza vaccines are available for a fee.FundingBill & Melinda Gates Foundation and the UK National Institute for Health Research.

Smartphone-Based Gait Cadence to Identify Older Adults with Decreased Functional Capacity

Background: Functional capacity assessment is a critical step in the preoperative evaluation to identify patients at increased risk of cardiac complications and disability after major noncardiac surgery. Smartphones offer the potential to objectively measure functional capacity but are limited by inaccuracy in patients with poor functional capacity. Open-source methods exist to analyze accelerometer data to estimate gait cadence (steps/min), which is directly associated with activity intensity. Here, we used an updated Step Test smartphone application with an open-source method to analyze accelerometer data to estimate gait cadence and functional capacity in older adults. Methods: We performed a prospective observational cohort study within the Frailty, Activity, Body Composition and Energy Expenditure in Aging study at the University of Chicago. Participants completed the Duke Activity Status Index (DASI) and performed an in-clinic 6-min walk test (6MWT) while using the Step Test application on a study smartphone. Gait cadence was measured from the raw accelerometer data using an adaptive empirical pattern transformation method, which has been previously validated. A 6MWT distance of 370 m was used as an objective threshold to identify patients at high risk. We performed multivariable logistic regression to predict walking distance using a priori explanatory variables. Results: Sixty patients were enrolled in the study. Thirty-seven patients completed the protocol and were included in the final data analysis. The median (IQR) age of the overall cohort was 71 (69–74) years, with a body mass index of 31 (27–32). There were no differences in any clinical characteristics or functional measures between participants that were able to walk 370 m during the 6MWT and those that could not walk that distance. Median (IQR) gait cadence for the entire cohort was 110 (102–114) steps/min during the 6MWT. Median (IQR) gait cadence was higher in participants that walked more than 370 m during the 6MWT 112 (108–118) versus 106 (96–114) steps/min; p = 0.0157). The final multivariable model to identify participants that could not walk 370 m included only median gait cadence. The Youden’s index cut-point was 107 steps/min with a sensitivity of 0.81 (95% CI: 0.77, 0.85) and a specificity of 0.57 (95% CI: 0.55, 0.59) and an AUCROC of 0.69 (95% CI: 0.51, 0.87). Conclusions: Our pilot study demonstrates the feasibility of using gait cadence as a measure to estimate functional capacity. Our study was limited by a smaller than expected sample size due to COVID-19, and thus, a prospective study with preoperative patients that measures outcomes is necessary to validate our findings.

A randomized Bayesian optimal phase II design with binary endpoint

ABSTRACT In this paper, we propose a randomized Bayesian optimal phase II (RBOP2) design with a binary endpoint (e.g., response rate). A beta-binomial distribution is used to model the binary endpoint for a two-arm phase II trial. Posterior probabilities of the endpoint of interest are evaluated at each interim look and used in the decision to stop the trial due to futility. Compared with other Bayesian designs, the proposed RBOP2 design has the following merits: (i) strongly controls the type I error rate at a pre-defined level; (ii) optimizes the stopping boundaries, thus maximizing the power to detect treatment effects and minimizing the expected sample size for futile treatment; (iii) does not limit the number of interim looks, thus enabling frequent trial monitoring; and (iv) allows the stopping boundaries to be pre-defined in the protocol and is easy to implement. We conduct simulation studies to compare the proposed design with a group sequential design and other Bayesian randomized designs and evaluate its operating characteristics under different scenarios.

Parallel-group, randomised, controlled, non-inferiority trial of high-flow nasal cannula versus non-invasive ventilation for emergency patients with acute cardiogenic pulmonary oedema: study protocol

IntroductionHigh-flow nasal cannula (HFNC) is an innovative oxygen-delivering technique, which has been shown to effectively decrease the intubation risk in patients with hypoxaemic respiratory failure of various aetiologies compared with...

Adversarially robust sequential hypothesis testing

The problem of sequential hypothesis testing is studied, where samples are taken sequentially, and the goal is to distinguish between the null hypothesis where the samples are generated according to a distribution p and the alternative hypothesis where the samples are generated according to a distribution q. The defender (decision maker) aims to distinguish the two hypotheses using as few samples as possible subject to false alarm constraints. The problem is studied under the adversarial setting, where the data generating distributions under the two hypotheses are manipulated by an adversary, whose goal is to deteriorate the performance of the defender—for example, increasing the probability of error and expected sample sizes—with minimal cost. Specifically, under the null hypothesis, the adversary picks a distribution with cost and under the alternative hypothesis, the adversary picks a distribution with cost This problem is formulated as a non-zero-sum game between the defender and the adversary. A pair of strategies (the adversary’s strategy and the defender’s strategy) is proposed and proved to be a Nash equilibrium pair for the non-zero-sum game between the adversary and the defender asymptotically. The defender’s strategy is a sequential probability ratio test and thus is computationally efficient for practical implementation.

Conditional power in vaccine trials with seasonal variations

ABSTRACT Conditional power (CP) is widely used in clinical trial monitoring to quantify the evidence for futility stopping or sample size adaptation during the trial. When planning an interim analysis in vaccine trials for seasonal infectious diseases, CPs calculated under the hypothesized or currently estimated effect sizes may not truly reflect future data due to seasonal variations in disease incidence and/or vaccine efficacy (VE). Relying on these estimates alone could lead to erroneous decisions. Therefore, we carried out simulation studies to investigate the use of seven different choices for the drift parameter in computing CP or predictive power (PP) in end-of-season interim analysis. Our simulations showed that, when used to inform futility stopping, CP under the hypothesized effect and a weighted PP under a normal prior distribution appear to outperform others in terms of the overall type II error rate. All CPs and PPs considered in this study resulted in comparable powers and expected sample sizes when used to inform sample size adaptation. The performance of either CP or PP largely depends on the extent to which the chosen drift parameter or the prior distribution of the drift parameter matches the remainder of the trial. Weighted CP/PP tends to be less sensitive to settings where observed data and emerging data in future seasons differ substantially as they incorporate both current estimate and future variations. Therefore, weighted strategies deserve further exploration and perhaps increased usage in guiding trial operations because they are more robust to inaccuracies in prediction. In summary, for vaccine trials with seasonal variations, a decision on trial operations should be guided by a careful consideration of plausible CPs and PPs calculated under reasonable assumptions leveraging the data, prior hypotheses, and new evidence on clinical relevance.

A multicenter, open-label, randomized, proof-of-concept phase II clinical trial to assess the efficacy and safety of icatibant in patients infected with SARS-CoV-2 (COVID-19) and admitted to hospital units without invasive mechanical ventilation: study protocol (ICAT-COVID)

BackgroundCOVID-19 has quickly become a global pandemic with a substantial number of deaths and is a considerable burden for healthcare systems worldwide. Although most cases are paucisymptomatic and limited to the viral infection-related symptoms, some patients evolve to a second phase, with an impaired inflammatory response (cytokine storm) that may lead to acute respiratory distress syndrome and death. This is thought to be caused by increased bradykinin synthesis.MethodsICAT-COVID is a multicenter, randomized, open-label, proof-of-concept phase II clinical trial assessing the clinical efficacy and safety of adding icatibant to the standard of care in patients hospitalized with COVID-19 without invasive mechanical ventilation. Patients hospitalized with a confirmed COVID-19 pneumonia diagnosis (RT-PCR or antigen test ≤ 10 days prior to randomization, and radiographic evidence of pulmonary infiltrates), rated “4” or “5” on the WHO’s clinical status scale, are eligible. Patients will be randomized on a 1:1 ratio to either standard of care-plus-icatibant (experimental group) or to standard of care alone (control group). The experimental group will receive 30 mg of icatibant subcutaneously 3 times a day for 3 days (for a total of 9 doses). The expected sample size is 120 patients (60 per group) from 2 sites in Spain. Primary outcomes are the efficacy and safety of Icatibant. The main efficacy outcome is the number of patients reaching grades “2” or “1” on the WHO scale within 10 days of starting treatment. Secondary outcomes include “long-term efficacy”: number of patients discharged who do not present COVID-19-related relapse or comorbidity up until 28 days after discharge, and mortality.DiscussionIcatibant, a bradykinin type 2 receptor antagonist with proven effectiveness and safety against hereditary angioedema attacks, may be beneficial for COVID-19 patients by inhibiting bradykinin’s action on endothelial cells and by inhibiting the SARS-CoV-2 M protease. Our working hypothesis is that treatment with standard of care-plus-icatibant is effective and safe to treat patients infected with SARS-CoV-2 admitted to hospital for pneumonia without invasive mechanical ventilation.Trial registrationEudraCT 2020-002166-13. ClinicalTrials.gov NCT04978051