Browsing by Subject "Prioritization"

Now showing 1 - 3 of 3
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    An ethics framework for consolidating and prioritizing COVID-19 clinical trials
    (Sage, 2021) Meyer, Michelle N.; Gelinas, Luke; Bierer, Barbara E.; Chandros Hull, Sara; Joffe, Steven; Magnus, David; Mohapatra, Seema; Sharp, Richard R.; Spector-Bagdady, Kayte; Sugarman, Jeremy; Wilfond, Benjamin S.; Fernandez Lynch, Holly; Robert H. McKinney School of Law
    Given the dearth of established safe and effective interventions to respond to COVID-19, there is an urgent ethical imperative to conduct meaningful clinical research. The good news is that interventions to be tested are not in short supply. Unfortunately, the human and material resources needed to conduct these trials are finite. It is essential that trials be robust and meet enrollment targets and that lower-quality studies not be permitted to displace higher-quality studies, delaying answers to critical questions. Yet, with few exceptions, existing research review bodies and processes are not designed to ensure these conditions are satisfied. To meet this challenge, we offer guidance for research institutions about how to ethically consolidate and prioritize COVID-19 clinical trials, while recognizing that consolidation and prioritization should also take place upstream (among manufacturers and funders) and at a higher level (e.g., nationally). In our proposed three-stage process, trials must first meet threshold criteria. Those that do are evaluated in a second stage to determine whether the institution has sufficient capacity to support all proposed trials. If it does not, the third stage entails evaluating studies against two additional sets of comparative prioritization criteria: those specific to the study and those that aim to advance diversification of an institution’s research portfolio. To implement these criteria fairly, we propose that research institutions form COVID-19 research prioritization committees. We briefly discuss some important attributes of these committees, drawing on the authors’ experiences at our respective institutions. Although we focus on clinical trials of COVID-19 therapeutics, our guidance should prove useful for other kinds of COVID-19 research, as well as non-pandemic research, which can raise similar challenges due to the scarcity of research resources.
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    Best Practices for Biostatistical Consultation and Collaboration in Academic Health Centers
    (Informa UK Limited, 2016) Perkins, Susan M.; Bacchetti, Peter; Davey, Cynthia S.; Lindsell, Christopher J.; Mazumdar, Madhu; Oster, Robert A.; Rocke, David M.; Rudser, Kyle D.; Kim, Mimi; Biostatistics, School of Public Health
    Given the increasing level and scope of biostatistics expertise needed at academic health centers today, we developed best practices guidelines for biostatistics units to be more effective in providing biostatistical support to their institutions, and in fostering an environment in which unit members can thrive professionally. Our recommendations focus on the key areas of: 1) funding sources and mechanisms; 2) providing and prioritizing access to biostatistical resources; and 3) interacting with investigators. We recommend that the leadership of biostatistics units negotiate for sufficient long-term infrastructure support to ensure stability and continuity of funding for personnel, align project budgets closely with actual level of biostatistical effort, devise and consistently apply strategies for prioritizing and tracking effort on studies, and clearly stipulate with investigators prior to project initiation policies regarding funding, lead time, and authorship.
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    WEVar: a novel statistical learning framework for predicting noncoding regulatory variants
    (Oxford University Press, 2021) Wang, Ye; Jiang, Yuchao; Yao, Bing; Huang, Kun; Liu, Yunlong; Wang, Yue; Qin, Xiao; Saykin, Andrew J.; Chen, Li; Biostatistics and Health Data Science, School of Medicine
    Understanding the functional consequence of noncoding variants is of great interest. Though genome-wide association studies or quantitative trait locus analyses have identified variants associated with traits or molecular phenotypes, most of them are located in the noncoding regions, making the identification of causal variants a particular challenge. Existing computational approaches developed for prioritizing noncoding variants produce inconsistent and even conflicting results. To address these challenges, we propose a novel statistical learning framework, which directly integrates the precomputed functional scores from representative scoring methods. It will maximize the usage of integrated methods by automatically learning the relative contribution of each method and produce an ensemble score as the final prediction. The framework consists of two modes. The first 'context-free' mode is trained using curated causal regulatory variants from a wide range of context and is applicable to predict regulatory variants of unknown and diverse context. The second 'context-dependent' mode further improves the prediction when the training and testing variants are from the same context. By evaluating the framework via both simulation and empirical studies, we demonstrate that it outperforms integrated scoring methods and the ensemble score successfully prioritizes experimentally validated regulatory variants in multiple risk loci.