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New technologies and disruptions related to Coronavirus disease-2019 have led to expansion of decentralized approaches to clinical trials. Remote tools and methods hold promise for increasing trial efficiency and reducing burdens and barriers by facilitating participation outside of traditional clinical settings and taking studies directly to participants. The Trial Innovation Network, established in 2016 by the National Center for Advancing Clinical and Translational Science to address critical roadblocks in clinical research and accelerate the translational research process, has consulted on over 400 research study proposals to date. Its recommendations for decentralized approaches have included eConsent, participant-informed study design, remote intervention, study task reminders, social media recruitment, and return of results for participants. Some clinical trial elements have worked well when decentralized, while others, including remote recruitment and patient monitoring, need further refinement and assessment to determine their value. Partially decentralized, or “hybrid” trials, offer a first step to optimizing remote methods. Decentralized processes demonstrate potential to improve urban-rural diversity, but their impact on inclusion of racially and ethnically marginalized populations requires further study. To optimize inclusive participation in decentralized clinical trials, efforts must be made to build trust among marginalized communities, and to ensure access to remote technology.
One challenge for multisite clinical trials is ensuring that the conditions of an informative trial are incorporated into all aspects of trial planning and execution. The multicenter model can provide the potential for a more informative environment, but it can also place a trial at risk of becoming uninformative due to lack of rigor, quality control, or effective recruitment, resulting in premature discontinuation and/or non-publication. Key factors that support informativeness are having the right team and resources during study planning and implementation and adequate funding to support performance activities. This communication draws on the experience of the National Center for Advancing Translational Science (NCATS) Trial Innovation Network (TIN) to develop approaches for enhancing the informativeness of clinical trials. We distilled this information into three principles: (1) assemble a diverse team, (2) leverage existing processes and systems, and (3) carefully consider budgets and contracts. The TIN, comprised of NCATS, three Trial Innovation Centers, a Recruitment Innovation Center, and 60+ CTSA Program hubs, provides resources to investigators who are proposing multicenter collaborations. In addition to sharing principles that support the informativeness of clinical trials, we highlight TIN-developed resources relevant for multicenter trial initiation and conduct.
Patients presenting to hospital with suspected coronavirus disease 2019 (COVID-19), based on clinical symptoms, are routinely placed in a cohort together until polymerase chain reaction (PCR) test results are available. This procedure leads to delays in transfers to definitive areas and high nosocomial transmission rates. FebriDx is a finger-prick point-of-care test (PoCT) that detects an antiviral host response and has a high negative predictive value for COVID-19. We sought to determine the clinical impact of using FebriDx for COVID-19 triage in the emergency department (ED).
We undertook a retrospective observational study evaluating the real-world clinical impact of FebriDx as part of an ED COVID-19 triage algorithm.
Emergency department of a university teaching hospital.
Patients presenting with symptoms suggestive of COVID-19, placed in a cohort in a ‘high-risk’ area, were tested using FebriDx. Patients without a detectable antiviral host response were then moved to a lower-risk area.
Between September 22, 2020, and January 7, 2021, 1,321 patients were tested using FebriDx, and 1,104 (84%) did not have a detectable antiviral host response. Among 1,104 patients, 865 (78%) were moved to a lower-risk area within the ED. The median times spent in a high-risk area were 52 minutes (interquartile range [IQR], 34–92) for FebriDx-negative patients and 203 minutes (IQR, 142–255) for FebriDx-positive patients (difference of −134 minutes; 95% CI, −144 to −122; P < .0001). The negative predictive value of FebriDx for the identification of COVID-19 was 96% (661 of 690; 95% CI, 94%–97%).
FebriDx improved the triage of patients with suspected COVID-19 and reduced the time that severe acute respiratory coronavirus virus 2 (SARS-CoV-2) PCR-negative patients spent in a high-risk area alongside SARS-CoV-2–positive patients.
Public health practitioners face challenging, potentially high-consequence, problems that require computational support. Available computational tools may not adequately fit these problems, thus forcing practitioners to rely on qualitative estimates when making critical decisions. Scientists at the Center for Computational Epidemiology and Response Analysis and practitioners from the Texas Department of State Health Services (TXDSHS) have established a participatory development cycle where public health practitioners work closely with academia to foster the development of data-driven solutions for specific public health problems and to translate these solutions to practice. Tools developed through this cycle have been deployed at TXDSHS offices where they have been used to refine and enhance the region’s medical countermeasure distribution and dispensing capabilities. Consequently, TXDSHS practitioners planning for a 49-county region in North Texas have achieved a 29% reduction in the number of points of dispensing required to complete dispensing to the region within time limitations. Further, an entire receiving, staging, and storing site has been removed from regional plans, thus freeing limited resources (eg, personnel, security, and infrastructure) for other uses. In 2018, planners from Southeast Texas began using these tools to plan for a multi-county, full-scale exercise which was scheduled to be conducted in October 2019.
This chapter highlights some important aspects of the design and analysis of clinical trials, and sketches a number of relevant statistical concepts. A controlled clinical trial of a medical intervention should have at least one primary hypothesis that drives its design. Well-designed and well-executed trials include an unambiguous protocol approved by the Institutional Review Boards (IRBs) or Ethics Committees of the participating clinics, laboratories, and data centers. The chapter also describes the basic frequentist statistical testing paradigm used by the typical randomized clinical trial with particular reference to ideas necessary in selecting sample size. Most clinical trials study more than one outcome of interest. Many neurological clinical trials compare therapies with respect to time to occurrence of the primary outcome. In the past, few clinical trials were performed in the Bayesian framework, but Bayesian methods have become more widely used recently.
This chapter focuses specifically on the activities and questions that are involved in the generation of data to support the registration and approval of a drug candidate. The data generated in early stage studies provide confidence for deciding whether to advance a drug into more complicated and expensive trials in specific patient populations. During middle stage development it is critical to begin to characterize the dose-response relationship for efficacy and safety endpoints in the selected population. Late stage confirmatory clinical trials often utilize a broader study population than was studied during early development. Besides the general scientific and medical literature, there are several important sources of information that can help with the strategy for clinical development programs and the design of specific trials and their questions. The FDA provides access to guidance documents that outline regulatory requirements related to the development of drugs and devices.
The goal of a controlled clinical trial is to compare the effects of interventions on outcomes of interest. This chapter considers the methods to limit bias and random error at each stage of a clinical trial-design, conduct, analysis and interpretation of results. Many aspects of study design relate to control of bias. The one of greatest importance is the method of assignment to treatment. Study assessments that incorporate some element of subjectivity can also be centralized. Many trials rely on a central adjudication group to make outcome assessments for all subjects in a study. In most studies, the treatments are compared with regard to multiple outcomes. From sample size considerations to central pathology review, from eligibility reviews to interim monitoring plans, all methodological considerations relate in one way or other to minimizing the potential for bias and reducing random error.
This chapter discusses the broad categories of clinical investigations used in post-market drug safety assessment. It presents the three main methods of clinical post-marketing safety assessment: case reports and case series; observational epidemiological studies; and clinical trials. Active surveillance systems are also being explored to identify and examine drug safety issues. Drug safety active surveillance systems, which take advantage of large repositories of automated healthcare data, are now being developed and tested by multiple organizations. The two most common observational epidemiological study designs are the case-control design and the cohort design. The majority of clinical trials are performed primarily to assess the efficacy of a product. The design of a post-marketing clinical trial testing a safety hypothesis is often an active-controlled trial that uses a non-inferiority study design. Relative to observational epidemiological studies, clinical trials designed to answer drug safety questions are usually more costly and more time-consuming.
This chapter provides an overview of outcome measures in neurology clinical trials, including developing a conceptual endpoint model, role and use of biomarkers, and considerations on how to select, use and interpret them in the context of early-stage clinical trial design. Early stage clinical trials (phase 1-2) often employ biomarker targets for proof of concept or therapeutic validation. Therapeutic development programs can be viewed as in the learn zone and confirm zone, with confirmation occurring in the phase 3 trial designed to test clinical efficacy against a standard or placebo. Structural imaging with MRI or computed tomography (CT) has been used as both an entry criteria into clinical trials and as an outcome measure. MRI has frequently been used as a measure of treatment response of multiple sclerosis (MS) patients. Researchers should define the role each endpoint is intended to play in the clinical trial.
Scientific discovery and clinical investigation are critical for developing and evaluating new treatments and can have substantial public health benefits. A detailed analysis of clinical trials funded by the National Institute of Neurological Disorders and Stroke found that the public return on investment in clinical trials has been substantial. In addition to the inherent risks involved in clinical trials, the challenges of translating scientific advances into new therapeutic advances are increasing. Many of the challenges of drug development are particularly acute for treatments of neurological conditions. The scope of clinical trials for neurological conditions is rapidly expanding to address orphan indications, biologics, medical devices, surgeries, and comparative effectiveness studies. In addition to drugs, clinical trials frequently evaluate devices for neurological conditions. High quality data on surgical interventions, such as temporal lobe resections for epilepsy are critical to understanding their relative risks and benefits in the target populations.
Clinical trials in Parkinson's disease (PD) have focused in two major areas: treatments designed to alleviate signs and symptoms in the short run, and treatments designed to modify the long-term progression of the illness. In clinical trials of short-term improvement with early PD patients the most common primary outcome measure is the Unified Parkinson's Disease Rating Scale (UPDRS). Motoric dysfunction, loss of ambulatory capability, cognitive impairment, mood disruption, and autonomic dysfunction all eventually contribute to potentially severe disability in individuals with advanced PD. Trials are just emerging that focus on the development of overall disability in PD, rather than measuring impairments in any particular domain such as motor function or cognitive impairment. Multiple trial designs have been proposed and used in studies to assess disease modification in PD. Several particular safety concerns have emerged in the context of PD clinical trials.
Translating laboratory discoveries into successful therapeutics can be difficult. Clinical Trials in Neurology aims to improve the efficiency of clinical trials and the development of interventions in order to enhance the development of new treatments for neurologic diseases. It introduces the reader to the key concepts underpinning trials in the neurosciences. This volume tackles the challenges of developing therapies for neurologic disorders from measurement of agents in the nervous system to the progression of clinical signs and symptoms through illustrating specific study designs and their applications to different therapeutic areas. Clinical Trials in Neurology covers key issues in Phase I, II and III clinical trials, as well as post-marketing safety surveillance. Topics addressed include regulatory and implementation issues, outcome measures and common problems in drug development. Written by a multidisciplinary team, this comprehensive guide is essential reading for neurologists, psychiatrists, neurosurgeons, neuroscientists, statisticians and clinical researchers in the pharmaceutical industry.
Selection designs and futility designs offer investigators a way to screen potential therapies in early phase clinical research with fewer patients than would be required for a traditional phase 3 trial for each candidate. There are some avoidable-pitfalls when planning a futility study. The first is that if the sample size is too small, a rather awkward situation can arise. The last pitfall relates to the use of historical control data in the single-arm design. Selection procedures offer an attractive approach to the problem of screening potentially good treatments. There are many different procedures for general ranking and selection goals such as selection from among more than two treatments, selection of best subsets of treatments, and ranking treatments in order of efficacy. Although selection procedures efficiently achieve their goal of selecting best treatments, the desire to 'test something' with an accompanying statement of statistical significance seems irresistible.