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Competing risks data are commonly encountered in randomized clinical trials and observational studies. This paper considers the situation where the ending statuses of competing events have different clinical interpretations and/or are of simultaneous interest. In clinical trials, often more than one competing event has meaningful clinical interpretations even though the trial effects of different events could be different or even opposite to each other. In this paper, we develop estimation procedures and inferential properties for the joint use of multiple cumulative incidence functions (CIFs). Additionally, by incorporating longitudinal marker information, we develop estimation and inference procedures for weighted CIFs and related metrics. The proposed methods are applied to a COVID‐19 in‐patient treatment clinical trial, where the outcomes of COVID‐19 hospitalization are either death or discharge from the hospital, two competing events with completely different clinical implications. [ABSTRACT FROM AUTHOR]

Competing risks data are commonly encountered in randomized clinical trials or observational studies. Ignoring competing risks in survival analysis leads to biased risk estimates and improper conclusions. Often, one of the competing events is of primary interest and the rest competing events are handled as nuisances. These approaches can be inadequate when multiple competing events have important clinical interpretations and thus of equal interest. For example, in COVID‐19 in‐patient treatment trials, the outcomes of COVID‐19 related hospitalization are either death or discharge from hospital, which have completely different clinical implications and are of equal interest, especially during the pandemic. In this paper we develop nonparametric estimation and simultaneous inferential methods for multiple cumulative incidence functions (CIFs) and corresponding restricted mean times. Based on Monte Carlo simulations and a data analysis of COVID‐19 in‐patient treatment clinical trial, we demonstrate that the proposed method provides global insights of the treatment effects across multiple endpoints. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has presented unique challenges for rapidly designing, initiating, and delivering therapeutic clinical trials. PRINCIPLE (Platform Randomised Trial of Treatments in the Community for Epidemic and Pandemic Illnesses) is the UK national platform investigating repurposed therapies for COVID-19 treatment of older people in the community at high risk of complications. Standard methods of patient recruitment were failing to meet the required pace and scale of enrolment. This paper describes the development and appraisal of a near real-time, data-driven, ethical approach for enhancing recruitment in community care by contacting people with a recent COVID-19 positive test result from the central NHS Test and Trace service within approximately 24-48 h of their test result.Methods: A multi-disciplinary team was formed to solve the technical, ethical, public perception, logistical and information governance issues required to provide a near-real time (approximately within 24-48 h of receiving a positive test) feed of potential trial participants from test result data to the research team. PRINCIPLE was also given unique access to the Summary Care Record (SCR) to ensure safe prescribing, and to enable the trial team to quickly and safely bring consented patients into the trial. A survey of the public was used to understand public perceptions of the use of test data for this proposed methodology.Results: Prior to establishing the data service, PRINCIPLE registered on average 87 participants per week. This increased by up to 87 additional people registered per week from the test data, contributing to an increase from 1013 recruits to PRINCIPLE at the start of October 2020 to 2802 recruits by 20 December 2020. Whilst procedural caveats were identified by the public consultation, out of 2639 people contacted by PRINCIPLE following a positive test result, no one raised a concern about being approached.Conclusions: This paper describes a novel approach to using near-real time NHS operational data to recruit community-based patients within a few days of presentation with acute illness. This approach increased recruitment and reduced time between positive test and randomisation, allowing more rapid evaluation of treatments and increased safety for participants. End-to-end public and patient involvement in the design of the approach provided evidence to inform information governance decisions.Trial Registration: PRINCIPLE is funded by UK Research and Innovation and the Department of Health and Social Care through the National Institute for Health Research. EudraCT number: 2020-001209-22 . 26/03/2020 ISRCTN registry: ISRCTN86534580 . 20/03/2020 REC number: 20/SC/058 IRAS number: 281958. [ABSTRACT FROM AUTHOR]

The World Health Organization (WHO) announced the Coronavirus disease (COVID-19), which is triggered by SARS-CoV-2 virus, on March 11, 2020. Nations all over the world reacted quickly, enacting various mitigation measures such as travel and movement restrictions, prohibitions on meeting, generalized or partial lockdowns, handwashing, and promotion of good hygiene and health. Corona virus outbreaks have claimed the lives of many people globally since 2019, with an estimated of over 5 million people infected. The world health organization since the announcement of the COVID-19 pandemic released statements about nations working towards finding a vaccine for the virus. Furthermore, with 80% of African population depending on traditional medicine, a spotlight was on the WHO about their views on African traditional medicine, which is a component of African indigenous Knowledge Systems as one of the COVID-19 treatments. The WHO therefore declared that traditional medicine would undergo clinical trials to check its potential as COVID-19 treatment. The paper examines the effects of COVID-19 in Africa, as well as existing clinical trials in African traditional medicine to cure and treat the virus, using existing literature. In addition, the paper considers the future of traditional medicine in the pharmaceutical and other medical plant industries. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic has driven an unprecedented level of global activity in drug discovery and clinical development for effective therapeutics targeting the coronavirus disease. There are currently 744 therapeutics being tested in 2879 clinical trials globally. Almost 90% of these clinical trials are focused on monotherapies. Combination therapies are the mainstay of antiviral therapeutics to increase the potency of the individual compounds and to combat the rapid evolution of resistance, although combination therapies have inherently complex clinical and regulatory development challenges. Increased understanding of the SARS‐CoV‐2 lifecycle and COVID‐19 pathology provides a scientific rationale for evaluating the effectiveness of different combinations. In this paper, we provide an overview of the current clinical trial landscape for combination therapeutics targeting COVID‐19 through weekly scanning of national and international clinical trial registries. Our analysis delves specifically into dual combination therapies in what can be defined as "pivotal clinical trials" (active, randomised, controlled and at least phase II), with a focus on new and repurposed therapeutic candidates that have shown positive signals and/or been granted authorisation for emergency use based on positive efficacy and safety data. [ABSTRACT FROM AUTHOR]

Background: With the Coronavirus becoming a new reality of our world, global efforts continue to seek answers to many questions regarding the spread, variants, vaccinations, and medications. Particularly, with the emergence of several strains (e.g., Delta, Omicron), vaccines will need further development to offer complete protection against the new variants. It is critical to identify antiviral treatments while the development of vaccines continues. In this regard, the repurposing of already FDA-approved drugs remains a major effort. In this paper, we investigate the hypothesis that a combination of FDA-approved drugs may be considered as a candidate for COVID-19 treatment if (1) there exists an evidence in the COVID-19 biomedical literature that suggests such a combination, and (2) there is match in the clinical trials space that validates this drug combination. Methods: We present a computational framework that is designed for detecting drug combinations, using the following components (a) a Text-mining module: to extract drug names from the abstract section of the biomedical publications and the intervention/treatment sections of clinical trial records. (b) a network model constructed from the drug names and their associations, (c) a clique similarity algorithm to identify candidate drug treatments. Result and Conclusions: Our framework has identified treatments in the form of two, three, or four drug combinations (e.g., hydroxychloroquine, doxycycline, and azithromycin). The identifications of the various treatment candidates provided sufficient evidence that supports the trustworthiness of our hypothesis. [ABSTRACT FROM AUTHOR]

As the world faced the devastation of the COVID‐19 pandemic in late 2019 and early 2020, numerous clinical trials were initiated in many locations in an effort to establish the efficacy (or lack thereof) of potential treatments. As the pandemic has been shifting locations rapidly, individual studies have been at risk of failing to meet recruitment targets because of declining numbers of eligible patients with COVID‐19 encountered at participating sites. It has become clear that it might take several more COVID‐19 surges at the same location to achieve full enrollment and to find answers about what treatments are effective for this disease. This paper proposes an innovative approach for pooling patient‐level data from multiple ongoing randomized clinical trials (RCTs) that have not been configured as a network of sites. We present the statistical analysis plan of a prospective individual patient data (IPD) meta‐analysis (MA) from ongoing RCTs of convalescent plasma (CP). We employ an adaptive Bayesian approach for continuously monitoring the accumulating pooled data via posterior probabilities for safety, efficacy, and harm. Although we focus on RCTs for CP and address specific challenges related to CP treatment for COVID‐19, the proposed framework is generally applicable to pooling data from RCTs for other therapies and disease settings in order to find answers in weeks or months, rather than years. [ABSTRACT FROM AUTHOR]

Ebselen is the leader of selenorganic compounds, and starting from its identification as mimetic of the key antioxidant enzyme glutathione peroxidase, several papers have appeared in literature claiming its biological activities. It was the subject of several clinical trials and it is currently in clinical evaluation for the treatment of COVID-19 patients. Given our interest in the synthesis and pharmacological evaluation of selenorganic derivatives with this review, we aimed to collect all the papers focused on the biological evaluation of ebselen and its close analogues, covering the timeline between 2016 and most of 2021. Our analysis evidences that, even if it lacks specificity when tested in vitro, being able to bind to every reactive cysteine, it proved to be always well tolerated in vivo, exerting no sign of toxicity whatever the administered doses. Besides, looking at the literature, we realized that no review article dealing with the synthetic approaches for the construction of the benzo[d][1,2]-selenazol-3(2H)-one scaffold is available; thus, a section of the present review article is completely devoted to this specific topic. [ABSTRACT FROM AUTHOR]

The first cases of coronavirus disease 2019 (COVID-19) were reported in December 2019 and the outbreak of SARS-CoV-2 was declared a pandemic in March 2020 by the World Health Organization. This sparked a plethora of investigations into diagnostics and vaccination for SARS-CoV-2, as well as treatments for COVID-19. Since COVID-19 is a severe disease associated with a high mortality, clinical trials in this disease should be monitored by a data monitoring committee (DMC), also known as data safety monitoring board (DSMB). DMCs in this indication face a number of challenges including fast recruitment requiring an unusually high frequency of safety reviews, more frequent use of complex designs and virtually no prior experience with the disease. In this paper, we provide a perspective on the work of DMCs for clinical trials of treatments for COVID-19. More specifically, we discuss organizational aspects of setting up and running DMCs for COVID-19 trials, in particular for trials with more complex designs such as platform trials or adaptive designs. Furthermore, statistical aspects of monitoring clinical trials of treatments for COVID-19 are considered. Some recommendations are made regarding the presentation of the data, stopping rules for safety monitoring and the use of external data. The proposed stopping boundaries are assessed in a simulation study motivated by clinical trials in COVID-19. [ABSTRACT FROM AUTHOR]