Clinical Endpoints for Evaluating Efficacy in COVID-19 Vaccine Trials.
Ontology highlight
ABSTRACT:
SUBMITTER: Mehrotra DV
PROVIDER: S-EPMC7596738 | biostudies-literature | 2020 Oct
REPOSITORIES: biostudies-literature
Publications
Clinical Endpoints for Evaluating Efficacy in COVID-19 Vaccine Trials.
Mehrotra Devan V DV Janes Holly E HE Fleming Thomas R TR Annunziato Paula W PW Neuzil Kathleen M KM Carpp Lindsay N LN Benkeser David D Brown Elizabeth R ER Carone Marco M Cho Iksung I Donnell Deborah D Fay Michael P MP Fong Youyi Y Han Shu S Hirsch Ian I Huang Ying Y Huang Yunda Y Hyrien Ollivier O Juraska Michal M Luedtke Alex A Nason Martha M Vandebosch An A Zhou Honghong H Cohen Myron S MS Corey Lawrence L Hartzel Jonathan J Follmann Dean D Gilbert Peter B PB
Annals of internal medicine 20201022 2
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 stati ...[more]
Similar Datasets
Project description:BackgroundVaccine clinical trials should strive to recruit a racially, socioeconomically, and ethnically diverse range of participants to ensure appropriate representation that matches population characteristics. Yet, full inclusion in research is often limited.MethodsA single-center retrospective study was conducted of adults enrolled at Brigham and Women's Hospital (Boston, MA) between July 2020 and December 2021. Demographic characteristics, including age, race, ethnicity, ZIP code, and sex assigned at birth, were analyzed from both HIV and COVID-19 vaccine trials during the study period, acknowledging the limitations to representation under these parameters. We compared the educational attainment of vaccine trial participants to residents of the Massachusetts metropolitan area, geocoded participants' addresses to their census block group, and linked them to reported median household income levels from publicly available data for 2020. Frequency and quartile analyses were carried out, and spatial analyses were performed using ArcGIS Online web-based mapping software (Esri).ResultsA total of 1030 participants from four COVID-19 vaccine trials (n = 916 participants) and six HIV vaccine trials (n = 114 participants) were included in the analysis. The median age was 49 years (IQR 33-63) and 28 years (IQR 24-34) for the COVID-19 and HIV vaccine trials, respectively. Participants identifying as White were the majority group represented for both the COVID-19 (n = 598, 65.3%) and HIV vaccine trials (n = 83, 72.8%). Fewer than 25% of participants identified as Hispanic or Latin. Based on ZIP code of residence, the median household income for COVID-19 vaccine clinical trial participants (n = 846) was 102,088 USD (IQR = 81,442-126,094). For HIV vaccine clinical trial participants (n = 109), the median household income was 101,266 USD (IQR 75,052-108,832).ConclusionWe described the characteristics of participants enrolled for HIV and COVID-19 vaccine trials at a single center and found similitude in geographical distribution, median incomes, and proportion of underrepresented individuals between the two types of vaccine candidate trials. Further outreach efforts are needed to ensure the inclusion of individuals from lower educational and socioeconomic brackets. In addition, continued and sustained efforts are necessary to ensure inclusion of individuals from diverse racial and ethnic backgrounds.
| S-EPMC11311253 | biostudies-literature
Project description:BackgroundEndpoint choice for randomized controlled trials of treatments for novel coronavirus-induced disease (COVID-19) is complex. Trials must start rapidly to identify treatments that can be used as part of the outbreak response, in the midst of considerable uncertainty and limited information. COVID-19 presentation is heterogeneous, ranging from mild disease that improves within days to critical disease that can last weeks to over a month and can end in death. While improvement in mortality would provide unquestionable evidence about the clinical significance of a treatment, sample sizes for a study evaluating mortality are large and may be impractical, particularly given a multitude of putative therapies to evaluate. Furthermore, patient states in between "cure" and "death" represent meaningful distinctions. Clinical severity scores have been proposed as an alternative. However, the appropriate summary measure for severity scores has been the subject of debate, particularly given the variable time course of COVID-19. Outcomes measured at fixed time points, such as a comparison of severity scores between treatment and control at day 14, may risk missing the time of clinical benefit. An endpoint such as time to improvement (or recovery) avoids the timing problem. However, some have argued that power losses will result from reducing the ordinal scale to a binary state of "recovered" versus "not recovered."MethodsWe evaluate statistical power for possible trial endpoints for COVID-19 treatment trials using simulation models and data from two recent COVID-19 treatment trials.ResultsPower for fixed time-point methods depends heavily on the time selected for evaluation. Time-to-event approaches have reasonable statistical power, even when compared with a fixed time-point method evaluated at the optimal time.DiscussionTime-to-event analysis methods have advantages in the COVID-19 setting, unless the optimal time for evaluating treatment effect is known in advance. Even when the optimal time is known, a time-to-event approach may increase power for interim analyses.
| S-EPMC7611901 | biostudies-literature
Project description:Zika virus (ZIKV) infection during pregnancy is associated with microcephaly and other birth defects, collectively termed Congenital Zika Syndrome (CZS). During the epidemic in 2015-16, ZIKV spread through the Americas and quickly joined the list of other known teratogenic pathogens, TORCH. Multiple ZIKV vaccines have been developed for protection of pregnant women and women of childbearing age. However, ZIKV infection incidence has since waned substantially, and adverse birth outcomes are rare outcomes of infection. Studying a vaccine's protective efficacy against CZS in a large phase III clinical trial may be infeasible in such times of low incidence. Should trials be initiated, researchers may resort to alternative clinical endpoints. In this study, we simulate a variety of vaccine clinical trial scenarios to evaluate the feasibility of the CZS endpoint in vaccine studies and compare CZS to other potential outcomes: ZIKV infection detected through weekly, biweekly, or monthly testing and laboratory-confirmed, symptomatic Zika Virus Disease. We compare the sample size required for 80% statistical power to detect vaccine efficacy and trial duration for each scenario. Our results show the feasibility of CZS clinical endpoints depends on the timing of simulated clinical trials in the course of a seasonal epidemic, due to CZS risk varying with trimester of infection. This result highlights additional considerations needed when designing vaccine efficacy trials of protection against teratogenic pathogens.
| S-EPMC6450563 | biostudies-literature
Project description:Questions remain regarding the effect of baseline host and exposure factors on vaccine efficacy (VE) across pathogens and vaccine platforms. We report placebo-controlled data from four Phase 3 COVID-19 trials during the early period of the pandemic. This was a cross-protocol analysis of four randomized, placebo-controlled efficacy trials (Moderna/mRNA1273, AstraZeneca/AZD1222, Janssen/Ad26.COV2.S, and Novavax/NVX-CoV2373) using a harmonized design. Trials were conducted in the United States and international sites in adults ≥ 18 years of age. VE was assessed for symptomatic and severe COVID-19. We analyzed 114,480 participants from both placebo and vaccine arms, enrolled July 2020 to February 2021, with follow up through July 2021. VE against symptomatic COVID-19 showed little heterogeneity across baseline socio-demographic, clinical or exposure characteristics, in either univariate or multivariate analysis, regardless of vaccine platform. Similarly, VE against severe COVID-19 in the single trial (Janssen) with sufficient endpoints for analysis showed little evidence of heterogeneity. COVID-19 VE is not influenced by baseline host or exposure characteristics across efficacy trials of different vaccine platforms and countries when well matched to circulating virus strains. This supports use of these vaccines, regardless of platform type, as effective tools in the near term for reducing symptomatic and severe COVID-19, particularly for older individuals and those with common co-morbidities during major variant shifts. Clinical trial registration numbers: NCT04470427, NCT04516746, NCT04505722, and NCT04611802.
| S-EPMC10288314 | biostudies-literature
Project description:BackgroundThe current endpoints for therapeutic trials of hospitalized COVID-19 patients capture only part of the clinical course of a patient and have limited statistical power and robustness.MethodsWe specify proportional odds models for repeated measures of clinical status, with a common odds ratio of lower severity over time. We also specify the proportional hazards model for time to each level of improvement or deterioration of clinical status, with a common hazard ratio for overall treatment benefit. We apply these methods to Adaptive COVID-19 Treatment Trials.ResultsFor remdesivir versus placebo, the common odds ratio was 1.48 (95% confidence interval (CI) = 1.23-1.79; p < 0.001), and the common hazard ratio was 1.27 (95% CI = 1.09-1.47; p = 0.002). For baricitinib plus remdesivir versus remdesivir alone, the common odds ratio was 1.32 (95% CI = 1.10-1.57; p = 0.002), and the common hazard ratio was 1.30 (95% CI = 1.13-1.49; p < 0.001). For interferon beta-1a plus remdesivir versus remdesivir alone, the common odds ratio was 0.95 (95% CI = 0.79-1.14; p = 0.56), and the common hazard ratio was 0.98 (95% CI = 0.85-1.12; p = 0.74).ConclusionsThe proposed methods comprehensively characterize the treatment effects on the entire clinical course of a hospitalized COVID-19 patient.
| S-EPMC11304635 | biostudies-literature
Project description: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.
| S-EPMC7833551 | biostudies-literature
Project description:Several candidate vaccines to prevent COVID-19 disease have entered large-scale phase 3 placebo-controlled randomized clinical trials and some have demonstrated substantial short-term efficacy. Efficacious vaccines should, at some point, be offered to placebo participants, which will occur before long-term efficacy and safety are known. Following vaccination of the placebo group, we show that placebo-controlled vaccine efficacy can be derived by assuming the benefit of vaccination over time has the same profile for the original vaccine recipients and the placebo crossovers. This reconstruction allows estimation of both vaccine durability and potential vaccine-associated enhanced disease. Post-crossover estimates of vaccine efficacy can provide insights about durability, identify waning efficacy, and identify late enhancement of disease, but are less reliable estimates than those obtained by a standard trial where the placebo cohort is maintained. As vaccine efficacy estimates for post-crossover periods depend on prior vaccine efficacy estimates, longer pre-crossover periods with higher case counts provide better estimates of late vaccine efficacy. Further, open-label crossover may lead to riskier behavior in the immediate crossover period for the unblinded vaccine arm, confounding vaccine efficacy estimates for all post-crossover periods. We advocate blinded crossover and continued follow-up of trial participants to best assess vaccine durability and potential delayed enhancement of disease. This approach allows placebo recipients timely access to the vaccine when it would no longer be proper to maintain participants on placebo, yet still allows important insights about immunological and clinical effectiveness over time.
| S-EPMC7745130 | biostudies-literature
Project description:To speed the development of vaccines against SARS-CoV-2, the United States Federal Government has funded multiple phase 3 trials of candidate vaccines. A single 11-member data and safety monitoring board (DSMB) monitors all government-funded trials to ensure coordinated oversight, promote harmonized designs, and allow shared insights related to safety across trials. DSMB reviews encompass 3 domains: (1) the conduct of trials, including overall and subgroup accrual and data quality and completeness; (2) safety, including individual events of concern and comparisons by randomized group; and (3) interim analyses of efficacy when event-driven milestones are met. Challenges have included the scale and pace of the trials, the frequency of safety events related to the combined enrollment of over 100 000 participants, many of whom are older adults or have comorbid conditions that place them at independent risk of serious health events, and the politicized environment in which the trials have taken place.
| S-EPMC8240876 | biostudies-literature
Project description:BackgroundCystic fibrosis (CF) pulmonary exacerbations can be serious respiratory events and reduction in exacerbation rate or risk are important efficacy endpoints for CF therapeutic trials. Variability in exacerbation diagnoses and treatment have led drug developers to employ "objective" exacerbation definitions combining antimicrobial treatment (AT) and the presence of ≥4 of 12 respiratory criteria (first published by Fuchs et al. [NEJM 1994;331(10):637-42]). Assumptions underlying this approach have yet to be formally evaluated.MethodsRespiratory events (RE) observed during a 48-week trial of ataluren (NCT02139306), a read-through agent for premature nonsense codons, were compared across six exacerbation definitions: any AT, intravenous AT (IVAT), ≥4 Fuchs criteria present, AT plus ≥4 Fuchs criteria, IVAT plus ≥4 Fuchs criteria, and investigator assessment. Fuchs definitions were evaluated by assessing missingness of individual criteria and associations between criteria presence and clinician exacerbation assessment.ResultsAmong 751 RE, more than one third had ≥4 Fuchs criteria present but were not assessed as exacerbations by investigators. Data for ≥1 and for 4 Fuchs criteria, respectively, were missing for ~ 90% and >30% of RE. Only 6/12 Fuchs criteria were present more often when investigators assessed RE as exacerbations than when they did not.Conclusions"Objective" definitions have shortcomings inconsistent with their purpose of optimizing exacerbation capture in clinical trials : 1) they capture events clinicians do not consider exacerbations, 2) are prone to data missingness which can bias the likelihood of meeting the definition, and 3) employ criteria that are not associated with investigator assessment of exacerbation.
| S-EPMC7362840 | biostudies-literature
Project description:BackgroundPhase III trials have estimated coronavirus disease 2019 (COVID-19) vaccine efficacy (VE) against symptomatic and asymptomatic infection. We explore the direction and magnitude of potential biases in these estimates and their implications for vaccine protection against infection and against disease in breakthrough infections.MethodsWe developed a mathematical model that accounts for natural and vaccine-induced immunity, changes in serostatus, and imperfect sensitivity and specificity of tests for infection and antibodies. We estimated expected biases in VE against symptomatic, asymptomatic, and any severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and against disease following infection for a range of vaccine characteristics and measurement approaches, and the likely overall biases for published trial results that included asymptomatic infections.ResultsVE against asymptomatic infection measured by polymerase chain reaction (PCR) or serology is expected to be low or negative for vaccines that prevent disease but not infection. VE against any infection is overestimated when asymptomatic infections are less likely to be detected than symptomatic infections and the vaccine protects against symptom development. A competing bias toward underestimation arises for estimates based on tests with imperfect specificity, especially when testing is performed frequently. Our model indicates considerable uncertainty in Oxford-AstraZeneca ChAdOx1 and Janssen Ad26.COV2.S VE against any infection, with slightly higher than published, bias-adjusted values of 59.0% (95% uncertainty interval [UI] 38.4-77.1) and 70.9% (95% UI 49.8-80.7), respectively.ConclusionsMultiple biases are likely to influence COVID-19 VE estimates, potentially explaining the observed difference between ChAdOx1 and Ad26.COV2.S vaccines. These biases should be considered when interpreting both efficacy and effectiveness study results.
| S-EPMC8586723 | biostudies-literature