Planning and Reporting Effective Web-Based RAND/UCLA Appropriateness Method Panels: Literature Review and Preliminary Recommendations.

BACKGROUND: The RAND/UCLA Appropriateness Method (RAM), a variant of the Delphi Method, was developed to synthesize existing evidence and elicit the clinical judgement of medical experts on the appropriate treatment of specific clinical presentations. Technological advances now allow researchers to conduct expert panels on the internet, offering a cost-effective and convenient alternative to the traditional RAM. For example, the Department of Veterans Affairs recently used a web-based RAM to validate clinical recommendations for de-intensifying routine primary care services. A substantial literature describes and tests various aspects of the traditional RAM in health research; yet we know comparatively less about how researchers implement web-based expert panels. OBJECTIVE: The objectives of this study are twofold: (1) to understand how the web-based RAM process is currently used and reported in health research and (2) to provide preliminary reporting guidance for researchers to improve the transparency and reproducibility of reporting practices. METHODS: The PubMed database was searched to identify studies published between 2009 and 2019 that used a web-based RAM to measure the appropriateness of medical care. Methodological data from each article were abstracted. The following categories were assessed: composition and characteristics of the web-based expert panels, characteristics of panel procedures, results, and panel satisfaction and engagement. RESULTS: Of the 12 studies meeting the eligibility criteria and reviewed, only 42% (5/12) implemented the full RAM process with the remaining studies opting for a partial approach. Among those studies reporting, the median number of participants at first rating was 42. While 92% (11/12) of studies involved clinicians, 50% (6/12) involved multiple stakeholder types. Our review revealed that the studies failed to report on critical aspects of the RAM process. For example, no studies reported response rates with the denominator of previous rounds, 42% (5/12) did not provide panelists with feedback between rating periods, 50% (6/12) either did not have or did not report on the panel discussion period, and 25% (3/12) did not report on quality measures to assess aspects of the panel process (eg, satisfaction with the process). CONCLUSIONS: Conducting web-based RAM panels will continue to be an appealing option for researchers seeking a safe, efficient, and democratic process of expert agreement. Our literature review uncovered inconsistent reporting frameworks and insufficient detail to evaluate study outcomes. We provide preliminary recommendations for reporting that are both timely and important for producing replicable, high-quality findings. The need for reporting standards is especially critical given that more people may prefer to participate in web-based rather than in-person panels due to the ongoing COVID-19 pandemic.

Impact of the COVID-19 Pandemic on Cardiovascular Science: Anticipating Problems and Potential Solutions: A Presidential Advisory From the American Heart Association.

The coronavirus disease 2019 (COVID-19) pandemic has had worldwide repercussions for health care and research. In spring 2020, most non-COVID-19 research was halted, hindering research across the spectrum from laboratory-based experimental science to clinical research. Through the second half of 2020 and the first half of 2021, biomedical research, including cardiovascular science, only gradually restarted, with many restrictions on onsite activities, limited clinical research participation, and the challenges associated with working from home and caregiver responsibilities. Compounding these impediments, much of the global biomedical research infrastructure was redirected toward vaccine testing and deployment. This redirection of supply chains, personnel, and equipment has additionally hampered restoration of normal research activity. Transition to virtual interactions offset some of these limitations but did not adequately replace the need for scientific exchange and collaboration. Here, we outline key steps to reinvigorate biomedical research, including a call for increased support from the National Institutes of Health. We also call on academic institutions, publishers, reviewers, and supervisors to consider the impact of COVID-19 when assessing productivity, recognizing that the pandemic did not affect all equally. We identify trainees and junior investigators, especially those with caregiving roles, as most at risk of being lost from the biomedical workforce and identify steps to reduce the loss of these key investigators. Although the global pandemic highlighted the power of biomedical science to define, treat, and protect against threats to human health, significant investment in the biomedical workforce is required to maintain and promote well-being.

Machine Learning and Artificial Intelligence in Pharmaceutical Research and Development: a Review.

Over the past decade, artificial intelligence (AI) and machine learning (ML) have become the breakthrough technology most anticipated to have a transformative effect on pharmaceutical research and development (R&D). This is partially driven by revolutionary advances in computational technology and the parallel dissipation of previous constraints to the collection/processing of large volumes of data. Meanwhile, the cost of bringing new drugs to market and to patients has become prohibitively expensive. Recognizing these headwinds, AI/ML techniques are appealing to the pharmaceutical industry due to their automated nature, predictive capabilities, and the consequent expected increase in efficiency. ML approaches have been used in drug discovery over the past 15-20 years with increasing sophistication. The most recent aspect of drug development where positive disruption from AI/ML is starting to occur, is in clinical trial design, conduct, and analysis. The COVID-19 pandemic may further accelerate utilization of AI/ML in clinical trials due to an increased reliance on digital technology in clinical trial conduct. As we move towards a world where there is a growing integration of AI/ML into R&D, it is critical to get past the related buzz-words and noise. It is equally important to recognize that the scientific method is not obsolete when making inferences about data. Doing so will help in separating hope from hype and lead to informed decision-making on the optimal use of AI/ML in drug development. This manuscript aims to demystify key concepts, present use-cases and finally offer insights and a balanced view on the optimal use of AI/ML methods in R&D.

Bioequivalence and food effect of a fixed-dose combination of macitentan and tadalafil: Adaptive design in the COVID-19 pandemic.

The COVID-19 pandemic has forced clinical studies to accommodate imposed limitations. In this study, the bioequivalence part could not be conducted as planned. Thus, the aim was to demonstrate bioequivalence, using an adaptive study design, of tadalafil in fixed-dose combination (FDC) tablets of macitentan/tadalafil with single macitentan and tadalafil (Canadian-sourced) tablets and assess the effect of food on FDC tablets in healthy subjects. This Phase 1, single-center, open-label, single-dose, two-part, two-period, randomized, crossover study enrolled 62 subjects. Tadalafil bioequivalence as part of FDC of macitentan/tadalafil (10/40 mg) with single-component tablets of macitentan (10 mg) and tadalafil (40 mg) was determined by pharmacokinetic (PK) assessment under fasted conditions. The effect of food on FDC was evaluated under fed and fasted conditions. Fasted 90% confidence intervals (CIs) for geometric mean ratios (GMRs) were within bioequivalence limits for tadalafil and macitentan. Fed and fasted 90% CIs for area under the curve (AUC) GMR were within bioequivalence limits. However, 90% CIs for maximum plasma concentration (Cmax ) GMR for macitentan and tadalafil were outside bioequivalence limits. One FDC-treated subject experienced a serious adverse event of transient ischemic attack (bioequivalence part). To address pandemic-imposed limitations, an adaptive study design was implemented to demonstrate that the FDC tablet was bioequivalent to the free combination of macitentan and tadalafil (Canadian-sourced). No clinically significant differences in PK were determined between fed and fasted conditions; the FDC formulation could be taken irrespective of meals. The FDC formulation under fasted and fed conditions was well tolerated with no clinically relevant differences in safety profiles between the treatment groups. NCT Number: NCT04235270.

Biologically grounded scientific methods: The challenges ahead for combating epidemics.

The protracted COVID 19 pandemic may indicate failures of scientific methodologies. Hoping to facilitate the evaluation and/or update of methods relevant in Biomedicine, several aspects of scientific processes are here explored. First, the background is reviewed. In particular, eight topics are analyzed: (i) the history of Higher Education models in reference to the pursuit of science and the type of student cognition pursued, (ii) whether explanatory or actionable knowledge is emphasized depending on the well- or ill-defined nature of problems, (iii) the role of complexity and dynamics, (iv) how differences between Biology and other fields influence methodologies, (v) whether theory, hypotheses or data drive scientific research, (vi) whether Biology is reducible to one or a few factors, (vii) the fact that data, to become actionable knowledge, require structuring, and (viii) the need of inter-/trans-disciplinary knowledge integration. To illustrate how these topics interact, a second section describes four temporal stages of scientific methods: conceptualization, operationalization, validation and evaluation. They refer to the transition from abstract (non-measurable) concepts (such as 'health') to the selection of concrete (measurable) operations (such as 'quantification of ́anti-virus specific antibody titers'). Conceptualization is the process that selects concepts worth investigating, which continues as operationalization when data-producing variables viewed to reflect critical features of the concepts are chosen. Because the operations selected are not necessarily valid, informative, and may fail to solve problems, validations and evaluations are critical stages, which require inter/trans-disciplinary knowledge integration. It is suggested that data structuring can substantially improve scientific methodologies applicable in Biology, provided that other aspects here mentioned are also considered. The creation of independent bodies meant to evaluate biologically oriented scientific methods is recommended.

Design in biology and rational design in vaccinology: A conceptual analysis.

This review discusses the philosophical foundations of what used to be called "the scientific method" and is nowadays often known as the scientific attitude. It used to be believed that scientific theories and methods aimed at the truth especially in the case of physics, chemistry and astronomy because these sciences were able to develop numerous scientific laws that made it possible to understand and predict many physical phenomena. The situation is different in the case of the biological sciences which deal with highly complex living organisms made up of huge numbers of constituents that undergo continuous dynamic processes; this leads to novel emergent properties in organisms that cannot be predicted because they are not present in the constituents before they have interacted with each other. This is one of the reasons why there are no universal scientific laws in biology. Furthermore, all scientific theories can only achieve a restricted level of predictive success because they remain valid only under the limited range of conditions that were used for establishing the theory' in the first place. Many theories that used to be accepted were subsequently shown to be false, demonstrating that scientific theories always remain tentative and can never be proven beyond and doubt. It is ironical that as scientists have finally accepted that approximate truths are perfectly adequate and that absolute truth is an illusion, a new irrational sociological phenomenon called Post-Truth conveyed by social media, the Internet and fake news has developed in the Western world that is convincing millions of people that truth simply does not exist. Misleading information is circulated with the intention to deceive and science denialism is promoted by denying the remarkable achievements of science and technology during the last centuries. Although the concept of intentional design is widely used to describe the methods that biologists use to make discoveries and inventions, it will be argued that the term is not appropriate for explaining the appearance of life on our planet nor for describing the scientific creativity of scientific investigators. The term rational for describing the development of new vaccines is also unjustified. Because the analysis of the COVID-19 pandemic requires contributions from biomedical and psycho-socioeconomic sciences, one scientific method alone would be insufficient for combatting the pandemic.

Advancing lung organoids for COVID-19 research.

The COVID-19 pandemic has emphasised the need to develop effective treatments to combat emerging viruses. Model systems that poorly represent a virus' cellular environment, however, may impede research and waste resources. Collaborations between cell biologists and virologists have led to the rapid development of representative organoid model systems to study severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We believe that lung organoids, in particular, have advanced our understanding of SARS-CoV-2 pathogenesis, and have laid a foundation to study future pandemic viruses and develop effective treatments.

Meta-analysis with zero-event studies: a comparative study with application to COVID-19 data.

BACKGROUND: Meta-analysis is a statistical method to synthesize evidence from a number of independent studies, including those from clinical studies with binary outcomes. In practice, when there are zero events in one or both groups, it may cause statistical problems in the subsequent analysis. METHODS: In this paper, by considering the relative risk as the effect size, we conduct a comparative study that consists of four continuity correction methods and another state-of-the-art method without the continuity correction, namely the generalized linear mixed models (GLMMs). To further advance the literature, we also introduce a new method of the continuity correction for estimating the relative risk. RESULTS: From the simulation studies, the new method performs well in terms of mean squared error when there are few studies. In contrast, the generalized linear mixed model performs the best when the number of studies is large. In addition, by reanalyzing recent coronavirus disease 2019 (COVID-19) data, it is evident that the double-zero-event studies impact the estimate of the mean effect size. CONCLUSIONS: We recommend the new method to handle the zero-event studies when there are few studies in a meta-analysis, or instead use the GLMM when the number of studies is large. The double-zero-event studies may be informative, and so we suggest not excluding them.

Risk-based centralized data monitoring of clinical trials at the time of COVID-19 pandemic.

OBJECTIVES: COVID-19 pandemic caused several alarming challenges for clinical trials. On-site source data verification (SDV) in the multicenter clinical trial became difficult due to travel ban and social distancing. For multicenter clinical trials, centralized data monitoring is an efficient and cost-effective method of data monitoring. Centralized data monitoring reduces the risk of COVID-19 infections and provides additional capabilities compared to on-site monitoring. The key steps for on-site monitoring include identifying key risk factors and thresholds for the risk factors, developing a monitoring plan, following up the risk factors, and providing a management plan to mitigate the risk. METHODS: For analysis purposes, we simulated data similar to our clinical trial data. We classified the data monitoring process into two groups, such as the Supervised analysis process, to follow each patient remotely by creating a dashboard and an Unsupervised analysis process to identify data discrepancy, data error, or data fraud. We conducted several risk-based statistical analysis techniques to avoid on-site source data verification to reduce time and cost, followed up with each patient remotely to maintain social distancing, and created a centralized data monitoring dashboard to ensure patient safety and maintain the data quality. CONCLUSION: Data monitoring in clinical trials is a mandatory process. A risk-based centralized data review process is cost-effective and helpful to ignore on-site data monitoring at the time of the pandemic. We summarized how different statistical methods could be implemented and explained in SAS to identify various data error or fabrication issues in multicenter clinical trials.

Statistical methods for comparing test positivity rates between countries: Which method should be used and why?

The test positivity (TP) rate has emerged as an important metric for gauging the illness burden due to COVID-19. Given the importance of COVID-19 TP rates for understanding COVID-related morbidity, researchers and clinicians have become increasingly interested in comparing TP rates across countries. The statistical methods for performing such comparisons fall into two general categories: frequentist tests and Bayesian methods. Using data from Our World in Data (ourworldindata.org), we performed comparisons for two prototypical yet disparate pairs of countries: Bolivia versus the United States (large vs. small-to-moderate TP rates), and South Korea vs. Uruguay (two very small TP rates of similar magnitude). Three different statistical procedures were used: two frequentist tests (an asymptotic z-test and the 'N-1' chi-square test), and a Bayesian method for comparing two proportions (TP rates are proportions). Results indicated that for the case of large vs. small-to-moderate TP rates (Bolivia versus the United States), the frequentist and Bayesian approaches both indicated that the two rates were substantially different. When the TP rates were very small and of similar magnitude (values of 0.009 and 0.007 for South Korea and Uruguay, respectively), the frequentist tests indicated a highly significant contrast, despite the apparent trivial amount by which the two rates differ. The Bayesian method, in comparison, suggested that the TP rates were practically equivalent-a finding that seems more consistent with the observed data. When TP rates are highly similar in magnitude, frequentist tests can lead to erroneous interpretations. A Bayesian approach, on the other hand, can help ensure more accurate inferences and thereby avoid potential decision errors that could lead to costly public health and policy-related consequences.

Clinical Trials [and Tribulations]: The Immediate Effects of COVID-19 on IBD Clinical Research Activity in the UK.

There have been immediate and profound impacts of SARS-CoV-2 and COVID-19 on health care services worldwide, with major consequences for non COVID-19 related health care. Alongside efforts to reconfigure services and enable continued delivery of safe clinical care for patients with IBD, consideration must also be given to management of IBD research activity. In many centres there has been an effective shutdown of IBD clinical trial activity as research sites have switched focus to either COVID-19 related research or clinical care only. As a result, the early termination of trial programmes, and loss of potentially effective therapeutic options for IBD, has become a real and worrying prospect. Moreover, in many countries research activity has become embedded into clinical care-with clinical trials often providing access to new therapies or strategies-which would otherwise not have been available in standard clinical pathways. This pandemic has significant implications for the design, conduct, analysis, and reporting of clinical trials in IBD. In this Viewpoint, we share our experiences from a clinical and academic perspective in the UK, highlighting the early challenges encountered, and consider implications for patients and staff at research sites, sponsors, research ethics committees, funders, and regulators. We also offer potential solutions both for now and for when we enter a recovery phase from the pandemic.

Social Determinants of Health and Implementation Research: Lessons from the COVID-19 Pandemic.

During the past three decades, the world has experienced many clinical and public health challenges that require implementation of practices and policies informed by an understanding of social determinants of health and health inequities, but perhaps none as global and pervasive as the current COVID-19 pandemic. In the context of this special themed issue on Social Determinants of Health and Implementation Research: Three Decades of Progress and a Need for Convergence, we highlight the application of social determinants of health and implementation research on various aspects of the COVID-19 pandemic.

The COVID-19 research landscape: Measuring topics and collaborations using scientific literature.

OBJECTIVES: The Coronavirus Disease 2019 (COVID-19) caused heavy burdens and brought tremendous challenges to global public health. This study aimed to investigate collaboration relationships, research topics, and research trends on COVID-19 using scientific literature. METHOD: COVID-19-related articles published from January 1 to July 1, 2020 were retrieved from PubMed database. A total of 27,370 articles were included. Excel 2010, Medical Text Indexer (MTI), VOSviewer, and D3.js were used to summarize bibliometric features. RESULTS: The number of the COVID-19 research publications has been continuously increasing after its break. United States was the most productive and active country for COVID-19 research, with the largest number of publications and collaboration relationships. Huazhong University of Science and Technology from China was the most productive institute on the number of publications, and University of Toronto from Canada ranked as Top 1 institute for global research collaboration. Four key research topics were identified, of which the topic of epidemiology and public health interventions has gathered highest attentions. Topic of virus infection and immunity has been more focused during the early stage of COVID-19 outbreak compared with later stage. The topic popularity of clinical symptoms and diagnosis has been steady. CONCLUSIONS: Our topic analysis results revealed that the study of drug treatment was insufficient. To achieve critical breakthroughs of this research area, more interdisciplinary, multi-institutional, and global research collaborations are needed.

Pride and Prejudice during the COVID-19 Pandemic: The Misfortune of Inappropriate Clinical Trial Design.

Coronavirus Disease 2019 (COVID-19) is a rapidly evolving global pandemic for which more than a thousand clinical trials have been registered to secure therapeutic effectiveness, expeditiously. Most of these are single-center non-randomized studies rather than multi-center, randomized controlled trials. Single-arm trials have several limitations and may be conducted when spontaneous improvement is not anticipated, small placebo effect exists, and randomization to a placebo is not ethical. In an emergency where saving lives takes precedence, it is ethical to conduct trials with any scientifically proven design, however, safety must not be compromised. A phase II or III trial can be conducted directly in a pandemic with appropriate checkpoints and stopping rules. COVID-19 has two management paradigms- antivirals, or treatment of its complications. Simultaneous assessment of two different treatments can be done using 2 × 2 factorial schema. World Health Organization's SOLIDARITY trial is a classic example of the global research protocol which can evaluate the preferred treatment to combat COVID-19 pandemic. Short of that, a trial design must incorporate the practicality of the intervention used, and an appropriate primary endpoint which should ideally be a clinical outcome. Collaboration between institutions is needed more than ever to successfully execute and accrue in randomized trials.

Moving fast but going slow: coordination challenges for trials of COVID-19 post-exposure prophylaxis.

An unprecedented volume of research has been generated in response to the COVID-19 pandemic. However, there are risks of inefficient duplication and of important work being impeded if efforts are not synchronized. Excessive reliance on observational studies, which can be more rapidly conducted but are inevitably subject to measured and unmeasured confounders, can foil efforts to conduct rigorous randomized trials. These challenges are illustrated by recent global efforts to conduct clinical trials of post-exposure prophylaxis (PEP) as a strategy for preventing COVID-19. Innovative strategies are needed to help overcome these issues, including increasing communication between the Data Safety and Monitoring Committees (DSMCs) of similar trials. It is important to reinforce the primacy of high-quality trials in generating unbiased answers to pressing prevention and treatment questions about COVID-19.

COVID-19 hits a trial: Arguments against hastily deviating from the plan.

The COVID-19 pandemic has substantially impacted the conduct of clinical trials. While initially preparing for a period of time, where it would likely be impossible to supervise trials in the usual way and precautionary measures had to be implemented to care for medication supply and general safety of study participants it is now important to consider, how the impact of the pandemic on trial outcome can be assessed, which measures are needed to decide, how to proceed with the trial and what is needed to compensate to irregularity introduced by the pandemic situation. Obviously not all trials will suffer to the same degree: some trials may be close to finalizing recruitment, others may not yet have started. Similarly not all clinical trials investigate vulnerable patient populations, but some will and may in addition have recruited to an extent that beneficial effects achieved in the initial phase of the trial may be outweighed by an increase e.g. in mortality that impacts both treatment groups. The situation is further complicated by the fact that the pandemic reached different countries in the world and even cities in one country at different points in time with different severity. Our example is a randomized and double-blind clinical trial comparing digitoxin and placebo in patients with advanced chronic heart failure. This trial has recruited roughly 1/3 of the overall 2200 patients when the disease outbreak reached Germany. We discuss how simulations and theoretical considerations can be used to address questions about the need to increase the overall sample-size to be recruited to compensate for a potential shrinkage of the treatment effect caused by the COVID-19 pandemic and what role the degree of consistency could play when comparing pre-, during- and post- COVID-19 periods of trial conduct regarding the question, whether the treatment effect can be considered consistent and with this generalizable. This is dependent on the size of the treatment effect and the impact of the pandemic. We argue, that in case of doubt, it may be wise to proceed with the original study plan.