Browse
Submit Data
Databases
API
Help

Dataset Information

28 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Evaluating Clinical Efficacy of Antiviral Therapy for COVID-19: A Surrogate Endpoint Approach.

ABSTRACT:

Introduction

Efficient evaluation with an early surrogate endpoint, taking into account the process of disease evolution, may not only clarify inconsistent or underpowered results but also provide a new insight into the exploration of a new antiviral therapy for treating COVID-19 patients.

Methods

We assessed the dynamics of COVID-19 disease spectrum, commencing from low-risk (no or low oxygen supplement), medium-risk (non-invasive ventilator or high oxygen supplement), and high-risk (extracorporeal membrane oxygenation or invasive ventilator) risk state on enrollment, and then the subsequent progression and regression of risk states until discharge or death. The efficacy of antiviral therapy in altering the dynamics was assessed by using the high-risk state as a surrogate endpoint based on the data retrieved from the two-arm Adaptive Covid-19 Treatment Trial.

Results

Using the high-risk state as a surrogate endpoint, remdesivir treatment led to a decrease in the high-risk COVID-19 state by 34.8% (95% CI 26.7-42.0%) for a 14-day period and 29.3% (95% CI 28.8-29.8%) up to 28 days, which were consistent with a statistically significant reduction of death by 30.5% (95% CI 6.6, 50.9%) up to a 28-day period. The estimates of numbers needed to be treated were 100.9 (95% CI 88.1, 115.7) for using the high-risk COVID-19 state as a surrogate endpoint for a 14-day period and 133.3 (95% CI 112.5, 158.0) were required for averting one death from COVID-19 up to 28 days.

Conclusions

We demonstrate the expedient use of the high-risk COVID-19 disease status as a surrogate endpoint for evaluating the primary outcome of the earliest death.

SUBMITTER: Jen HH

PROVIDER: S-EPMC7972010 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Clinical Endpoints for Evaluating Efficacy in COVID-19 Vaccine Trials.

Project description:Several vaccine candidates to protect against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or coronavirus disease 2019 (COVID-19) have entered or will soon enter large-scale, phase 3, placebo-controlled randomized clinical trials. To facilitate harmonized evaluation and comparison of the efficacy of these vaccines, a general set of clinical endpoints is proposed, along with considerations to guide the selection of the primary endpoints on the basis of clinical and statistical reasoning. The plausibility that vaccine protection against symptomatic COVID-19 could be accompanied by a shift toward more SARS-CoV-2 infections that are asymptomatic is highlighted, as well as the potential implications of such a shift.

| S-EPMC7596738 | biostudies-literature

Early triple antiviral therapy for COVID-19.

Project description: Not available

| S-EPMC7836694 | biostudies-literature

Microdystrophin Expression as a Surrogate Endpoint for Duchenne Muscular Dystrophy Clinical Trials.

Project description:Duchenne muscular dystrophy (DMD) is a serious, rare genetic disease, affecting primarily boys. It is caused by mutations in the DMD gene and is characterized by progressive muscle degeneration that results in loss of function and early death due to respiratory and/or cardiac failure. Although limited treatment options are available, some for only small subsets of the patient population, DMD remains a disease with large unmet medical needs. The adeno-associated virus (AAV) vector is the leading gene delivery system for addressing genetic neuromuscular diseases. Since the gene encoding the full-length dystrophin protein exceeds the packaging capacity of a single AAV vector, gene replacement therapy based on AAV-delivery of shortened, yet, functional microdystrophin genes has emerged as a promising treatment. This article seeks to explain the rationale for use of the accelerated approval pathway to advance AAV microdystrophin gene therapy for DMD. Specifically, we provide support for the use of microdystrophin expression as a surrogate endpoint that could be used in clinical trials to support accelerated approval.

| S-EPMC10210223 | biostudies-literature

Metabolomics to Predict Antiviral Drug Efficacy in COVID-19.

Project description: Not available

| S-EPMC7462337 | biostudies-literature

Interim Guidelines on Antiviral Therapy for COVID-19.

Project description:Since the first case was reported in Wuhan, Hubei Province, China on December 12, 2019, Coronavirus disease 2019 (COVID-19) has spread widely to other countries since January 2020. As of April 16, 2020, 10635 confirmed cases have been reported, with 230 deaths in Korea. COVID-19 patients may be asymptomatic or show various clinical manifestations, including acute symptoms such as fever, fatigue, sore throat; pneumonia presenting as acute respiratory distress syndrome; and multiple organ failure. As COVID-19 has such varied clinical manifestations and case fatality rates, no standard antiviral therapy regimen has been established other than supportive therapy. In the present guideline, we aim to introduce potentially helpful antiviral and other drug therapies based on in vivo and in vitro research and clinical experiences from many countries.

| S-EPMC7335642 | biostudies-literature

Clinical study evaluating the efficacy of ivermectin in COVID-19 treatment: A randomized controlled study.

Project description:Researchers around the world are working at record speed to find the best ways to treat and prevent coronavirus disease 2019 (COVID-19). This study aimed to evaluate the efficacy of ivermectin for the treatment of hospitalized mild to moderate COVID-19 infected patients. This was a randomized open-label controlled study that included 164 patients with COVID-19. Patients were randomized into two groups where Group 1 (Ivermectin group) included patients who received ivermectin 12 mg once daily for 3 days with standard care and Group 2 (control group) included patients who received standard protocol of treatment alone for 14 days. The main outcomes were mortality, the length of hospital stay, and the need for mechanical ventilation. All patients were followed up for 1 month. Overall, 82 individuals were randomized to receive ivermectin plus standard of care and 82 to receive standard of care alone. Patients in the ivermectin group had a shorter length of hospital stay (8.82 ± 4.94 days) than the control group (10.97 ± 5.28 days), but this was not statistically significant (p = 0.085). Three patients (3.7%) in each group required mechanical ventilation (p = 1.00). The death rate was three patients in the ivermectin group (3.7%) versus four patients (4.9%) in the control group without any significant difference between the two groups (p = 1.00). Although there was no statistically significant difference in any endpoints by ivermectin doses (12 mg/day for 3 days); there was an observed trend to reducing hospital stay in the ivermectin-treated group.

| S-EPMC8242425 | biostudies-literature

Surrogate endpoint analysis: an exercise in extrapolation.

Project description:Surrogate endpoints offer the hope of smaller or shorter cancer trials. It is, however, important to realize they come at the cost of an unverifiable extrapolation that could lead to misleading conclusions. With cancer prevention, the focus is on hypothesis testing in small surrogate endpoint trials before deciding whether to proceed to a large prevention trial. However, it is not generally appreciated that a small surrogate endpoint trial is highly sensitive to a deviation from the key Prentice criterion needed for the hypothesis-testing extrapolation. With cancer treatment, the focus is on estimation using historical trials with both surrogate and true endpoints to predict treatment effect based on the surrogate endpoint in a new trial. Successively leaving out one historical trial and computing the predicted treatment effect in the left-out trial yields a standard error multiplier that summarizes the increased uncertainty in estimation extrapolation. If this increased uncertainty is acceptable, three additional extrapolation issues (biological mechanism, treatment following observation of the surrogate endpoint, and side effects following observation of the surrogate endpoint) need to be considered. In summary, when using surrogate endpoint analyses, an appreciation of the problems of extrapolation is crucial.

| S-EPMC3611854 | biostudies-literature

A Bayesian approach to improved estimation of causal effect predictiveness for a principal surrogate endpoint.

Project description:The literature on potential outcomes has shown that traditional methods for characterizing surrogate endpoints in clinical trials based only on observed quantities can fail to capture causal relationships between treatments, surrogates, and outcomes. Building on the potential-outcomes formulation of a principal surrogate, we introduce a Bayesian method to estimate the causal effect predictiveness (CEP) surface and quantify a candidate surrogate's utility for reliably predicting clinical outcomes. In considering the full joint distribution of all potentially observable quantities, our Bayesian approach has the following features. First, our approach illuminates implicit assumptions embedded in previously-used estimation strategies that have been shown to result in poor performance. Second, our approach provides tools for making explicit and scientifically-interpretable assumptions regarding associations about which observed data are not informative. Through simulations based on an HIV vaccine trial, we found that the Bayesian approach can produce estimates of the CEP surface with improved performance compared to previous methods. Third, our approach can extend principal-surrogate estimation beyond the previously considered setting of a vaccine trial where the candidate surrogate is constant in one arm of the study. We illustrate this extension through an application to an AIDS therapy trial where the candidate surrogate varies in both treatment arms.

| S-EPMC3860118 | biostudies-literature

Implementing the Kidney Health Initiative Surrogate Efficacy Endpoint in Patients With IgA Nephropathy (the PROTECT Trial).

Project description: Not available

| S-EPMC6933476 | biostudies-literature

Bivariate network meta-analysis for surrogate endpoint evaluation.

Project description:Surrogate endpoints are very important in regulatory decision making in healthcare, in particular if they can be measured early compared to the long-term final clinical outcome and act as good predictors of clinical benefit. Bivariate meta-analysis methods can be used to evaluate surrogate endpoints and to predict the treatment effect on the final outcome from the treatment effect measured on a surrogate endpoint. However, candidate surrogate endpoints are often imperfect, and the level of association between the treatment effects on the surrogate and final outcomes may vary between treatments. This imposes a limitation on methods which do not differentiate between the treatments. We develop bivariate network meta-analysis (bvNMA) methods, which combine data on treatment effects on the surrogate and final outcomes, from trials investigating multiple treatment contrasts. The bvNMA methods estimate the effects on both outcomes for all treatment contrasts individually in a single analysis. At the same time, they allow us to model the trial-level surrogacy patterns within each treatment contrast and treatment-level surrogacy, thus enabling predictions of the treatment effect on the final outcome either for a new study in a new population or for a new treatment. Modelling assumptions about the between-studies heterogeneity and the network consistency, and their impact on predictions, are investigated using an illustrative example in advanced colorectal cancer and in a simulation study. When the strength of the surrogate relationships varies across treatment contrasts, bvNMA has the advantage of identifying treatment comparisons for which surrogacy holds, thus leading to better predictions.

| S-EPMC6618064 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data