Walterswhitley9755

This methodology is extensible to other wearable sensors.

Determining the optimal sampling rate allows us to compress biomedical data and reduce storage needs and financial costs. We have used optical heart rate sensors as a case study for the connection between data volumes and resource requirements to develop methodology for determining the optimal sampling rate for clinical relevance that minimizes resource utilization. This methodology is extensible to other wearable sensors.

Access to cutting-edge technologies is essential for investigators to advance translational research. The Indiana Clinical and Translational Sciences Institute (CTSI) spans three major and preeminent universities, four large academic campuses across the state of Indiana, and is mandate to provide best practices to a whole state.

To address the need to facilitate the availability of innovative technologies to its investigators, the Indiana CTSI implemented the Access Technology Program (ATP). The activities of the ATP, or any program of the Indiana CTSI, are challenged to connect technologies and investigators on the multiple Indiana CTSI campuses by the geographical distances between campuses (1-4 hr driving time).

Herein, we describe the initiatives developed by the ATP to increase the availability of state-of-the-art technologies to its investigators on all Indiana CTSI campuses, and the methods developed by the ATP to bridge the distance between campuses, technologies, and investigators for the advancement of clinical translational research.

The methods and practices described in this publication may inform other approaches to enhance translational research, dissemination, and usage of innovative technologies by translational investigators, especially when distance or multi-campus cultural differences are factors to efficient application.

The methods and practices described in this publication may inform other approaches to enhance translational research, dissemination, and usage of innovative technologies by translational investigators, especially when distance or multi-campus cultural differences are factors to efficient application.

Failure to achieve accrual goals is a common problem in health-related research. Electronic health records represent a promising resource, offering the ability to identify a precisely defined cohort of patients who meet inclusion/exclusion criteria. However, challenges associated with the recruitment process remain and institutional policies vary.

We interviewed researchers, institutional review board chairs, and primary care physicians in North Carolina and Tennessee. Questions focused on strategies for initiating contact with potentially eligible patients, as well as recruitment letters asking recipients to opt in versus opt out of further communication.

When we asked about initiating contact with prospective participants, qualitative themes included trust, credibility, and established relationships; research efficiency and validity; privacy and autonomy; the intersection between research and clinical care; and disruption to physician-researcher and physician-patient relationships. All interviewees saeloping balanced approaches that respect patients and facilitate the advancement of science.

Rigor and reproducibility are two important cornerstones of medical and scientific advancement. Clinical and translational research (CTR) contains four phases (T1-T4), involving the translation of basic research to humans, then to clinical settings, practice, and the population, with the ultimate goal of improving public health. Here we provide a framework for rigorous and reproducible CTR.

In this paper we define CTR, provide general and phase-specific recommendations for improving quality and reproducibility of CTR with emphases on study design, data collection and management, analyses and reporting. We present and discuss aspects of rigor and reproducibility following published examples of CTR from the literature, including one example that shows the development path of different treatments that address anaplastic lymphoma kinase-positive (ALK+) non-small cell lung cancer (NSCLC).

It is particularly important to consider robust and unbiased experimental design and methodology for analysis and interprvances in the relevant field of research.For the past 4 years, as part of the National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) grant award number UL1TR001436, the Clinical Translational Science Institute of Southeast Wisconsin (CTSI) has used process engineering approaches to identify and understand barriers that local researchers and other stakeholders face when engaging in clinical and translational science. We describe these approaches and present preliminary results. We identified barriers from published and unpublished work at other CTSA hubs, supplemented by surveys and semi-structured interviews of CTSI faculty. We then used a multifaceted approach to organize, visualize, and analyze the barriers. We have identified 27 barriers to date. We ranked their priority for CTSI to address based on the barrier's impact, the feasibility of intervention, and whether addressing the barrier aligned with CTSI's institutional role. This approach provides a systematic framework to scope and address the "barriers to research problem" at CTSI institutions.The availability of large healthcare datasets offers the opportunity for researchers to navigate the traditional clinical and translational science research stages in a nonlinear manner. In particular, data scientists can harness the power of large healthcare datasets to bridge from preclinical discoveries (T0) directly to assessing population-level health impact (T4). A successful bridge from T0 to T4 does not bypass the other stages entirely; rather, effective team science makes a direct progression from T0 to T4 impactful by incorporating the perspectives of researchers from every stage of the clinical and translational science research spectrum. In this exemplar, we demonstrate how effective team science overcame challenges and, ultimately, ensured success when a diverse team of researchers worked together, using healthcare big data to test population-level substance use disorder (SUD) hypotheses generated from preclinical rodent studies. This project, called Advancing Substance use disorder Knowledge using Big Data (ASK Big Data), highlights the critical roles that data science expertise and effective team science play in quickly translating preclinical research into public health impact.

The AIDS Malignancy Consortium (AMC) conducts clinical trials of therapeutic and prevention strategies for cancer in people living with HIV. With its recent expansion to Sub-Saharan Africa and Latin America, there was a need to increase the competence of clinical investigators (CIs) to implement clinical trials in these regions.

AMC CIs were invited to complete a survey to assess role-relevance and self-perceived competence based on the Joint Task Force for Clinical Trials Competency domains.

A total of 40 AMC CIs were invited to complete the questionnaire and 35 responded to the survey. The data management and informatics and engaging with communities' domains were lowest in the average proportion of CIs rating themselves high (scores of 3-4) for self-perceived competency (46.6% and 44.2%) and role-relevance (61.6% and 67.5%), whereas, the ethical and participant safety considerations domain resulted in the highest score for competency (86.6%) and role-relevance (93.3%). In the scientific concepts and research design domain, a high proportion rated for competency in evaluating study designs and scientific literature (71.4% and 74.3%) but a low proportion for competency for designing trials and specimen collection protocols (51.4% and 54.3%).

Given the complexity of AMC clinical research, these results provide evidence of the need to develop training for clinical research professionals across domains where self-perceived competence is low. This assessment will be used to tailor and prioritize the AMC Training Program in clinical trial development and management for AMC CIs.

Given the complexity of AMC clinical research, these results provide evidence of the need to develop training for clinical research professionals across domains where self-perceived competence is low. This assessment will be used to tailor and prioritize the AMC Training Program in clinical trial development and management for AMC CIs.

The association between surgery with general anesthesia (exposure) and cognition (outcome) among older adults has been studied with mixed conclusions. We revisited a recent analysis to provide missing data education and discuss implications of biostatistical methodology for informative dropout following dementia diagnosis.

We used data from the Mayo Clinic Study of Aging, a longitudinal study of prevalence, incidence, and risk factors for mild cognitive impairment (MCI) and dementia. We fit linear mixed effects models (LMMs) to assess the association between anesthesia exposure and subsequent trajectories of cognitive

-scores assuming data missing at random, hypothesizing that exposure is associated with greater decline in cognitive function. Additionally, we used shared parameter models for informative dropout assuming data missing not at random.

A total of 1948 non-demented participants were included. Median age was 79 years, 49% were female, and 16% had MCI at enrollment. Among median follow-up of 4 study visits over 6.6 years, 172 subjects developed dementia, 270 died, and 594 participants underwent anesthesia. In LMMs, exposure to anesthesia was associated with decline in cognitive function over time (change in annual cognitive

-score slope = -0.063, 95% CI (-0.080, -0.046),

< 0.001). Accounting for informative dropout using shared parameter models, exposure was associated with greater cognitive decline (change in annual slope = -0.081, 95% CI (-0.137, -0.026),

= 0.004).

We revisited prior work by our group with a focus on informative dropout. ASN-002 Although the conclusions are similar, we demonstrated the potential impact of novel biostatistics methodology in longitudinal clinical research.

We revisited prior work by our group with a focus on informative dropout. Although the conclusions are similar, we demonstrated the potential impact of novel biostatistics methodology in longitudinal clinical research.The emphasis on team science in clinical and translational research increases the importance of collaborative biostatisticians (CBs) in healthcare. Adequate training and development of CBs ensure appropriate conduct of robust and meaningful research and, therefore, should be considered as a high-priority focus for biostatistics groups. Comprehensive training enhances clinical and translational research by facilitating more productive and efficient collaborations. While many graduate programs in Biostatistics and Epidemiology include training in research collaboration, it is often limited in scope and duration. Therefore, additional training is often required once a CB is hired into a full-time position. This article presents a comprehensive CB training strategy that can be adapted to any collaborative biostatistics group. This strategy follows a roadmap of the biostatistics collaboration process, which is also presented. A TIE approach (Teach the necessary skills, monitor the Implementation of these skills, and Evaluate the proficiency of these skills) was developed to support the adoption of key principles.