The University of Wisconsin Institute for Clinical and Translational Research hub supports multiple pilot award programs that engage cross-disciplinary Translational Teams. To support those teams, our Team Science group aims to offer a learning experience that is accessible, active, and actionable. We identified Collaboration Planning as a high-impact intervention to stimulate team-building activities that provide Translational Team members with the skills to lead and participate in high-impact teams.

We adapted the published materials on Collaboration Planning to develop a 90-minute facilitated intervention with questions in 10 areas, presuming no previous knowledge of Science of Team Science (SciTS) or team-science best practices. Attendees received a short follow-up survey and submitted a written collaboration plan with their first quarterly progress report.

Thirty-nine participants from 13 pilot teams from a wide range of disciplines engaged in these sessions. We found that teams struggled to know who to invite, that some of our questions were confusing and too grounded in the language of SciTS, and groups lacked plans for managing their information and communications. We identified several areas for improvement including ensuring that the process is flexible to meet the needs of different teams, continuing to evolve the questions so they resonate with teams, and the need to provide resources for areas where teams needed additional guidance, including information and data management, authorship policies, and conflict management.

With further development and testing, Collaboration Planning has the potential to support Translational Teams in developing strong team dynamics and team functioning.

Evaluating clinical and translational research (CTR) mentored training programs is challenging because no two programs are alike. Careful selection of appropriate metrics is required to make valid comparisons between individuals and between programs. The KL2 program provides mentored-training for early-stage CTR investigators. Clinical and Translational Awards across the country have unique KL2 programs. The evaluation of KL2 programs has begun to incorporate bibliometrics to measure KL2 scholar and program impact.

This study investigated demographic differences in bibliometric performance and post-K award funding of KL2 scholars and compared the bibliometric performance and post-K award federal funding of KL2 scholars and other mentored-K awardees at the same institution. Data for this study included SciVal and iCite bibliometrics and National Institutions of Health RePORTER grant information for mentored-K awardees (K08, K23, and KL2) at Case Western Reserve University between 2005 and 2013.

Results showed no demographics differences within the KL2 program scholars. Bibliometric differences between KL2 and other mentored-K awardee indicated an initial KL2 advantage for the number of publications at 5 years’ post-matriculation (i.e., the start of the K award). Regression analyses indicated the number of initial publications was a significant predictor of federal grant funding at the same time point. Analysis beyond the 5-year post-matriculation point did not result in a sustained, significant KL2 advantage.

Factors that contributed to the grant funding advantage need to be determined. Additionally, differences between translational and clinical bibliometrics must be interpreted with caution, and appropriate metrics for translational science must be established.

The Clinical and Translational Science Awards (CTSA) Consortium, about 60 National Institutes of Health (NIH)-supported CTSA hubs at academic health care institutions nationwide, is charged with improving the clinical and translational research enterprise. Together with the NIH National Center for Advancing Translational Sciences (NCATS), the Consortium implemented Common Metrics and a shared performance improvement framework.

Initial implementation across hubs was assessed using quantitative and qualitative methods over a 19-month period. The primary outcome was implementation of three Common Metrics and the performance improvement framework. Challenges and facilitators were elicited.

Among 59 hubs with data, all began implementing Common Metrics, but about one-third had completed all activities for three metrics within the study period. The vast majority of hubs computed metric results and undertook activities to understand performance. Differences in completion appeared in developing and carrying out performance improvement plans. Seven key factors affected progress: hub size and resources, hub prior experience with performance management, alignment of local context with needs of the Common Metrics implementation, hub authority in the local institutional structure, hub engagement (including CTSA Principal Investigator involvement), stakeholder engagement, and attending training and coaching.

Implementing Common Metrics and performance improvement in a large network of research-focused organizations proved feasible but required substantial time and resources. Considerable heterogeneity across hubs in data systems, existing processes and personnel, organizational structures, and local priorities of home institutions created disparate experiences across hubs. Future metric-based performance management initiatives across heterogeneous local contexts should anticipate and account for these types of differences.

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 z-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 z-score slope = −0.063, 95% CI: (−0.080, −0.046), p < 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), p = 0.004).

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 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.

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 interpretation for clinical translation studies to ensure reproducibility before taking the next translational step. There are both commonality and differences along the clinical translation research phases in terms of research focuses and considerations regarding study design, implementation, and data analysis approaches.

Sound scientific practices, starting with rigorous study design, transparency, and team efforts can greatly enhance CTR. Investigators from multidisciplinary teams should work along the spectrum of CTR phases, and identify optimal practices for study design, data collection, data analysis, and results reporting to allow timely advances in the relevant field of research.

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 said it was acceptable for researchers to contact patients through their physicians; most said it was acceptable for researchers to contact patients directly. Over half chose contact through physicians as more appropriate. Regarding recruitment letters, qualitative themes included the quality of the participant pool; privacy and control; research efficiency and representativeness; and patients’ opportunity to make their own decisions. All interviewees said asking recipients to opt in to further communication was acceptable; nearly all said opt out was acceptable. Similar proportions chose each approach as more appropriate.

Comparing these results to our previous research with patients reveals potential differences in stakeholder perspectives. We offer suggestions for developing balanced approaches that respect patients and facilitate the advancement of science.

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.

Personalized medicine has exposed wearable sensors as new sources of biomedical data which are expected to accrue annual data storage costs of approximately $7.2 trillion by 2020 (>2000 exabytes). To improve the usability of wearable devices in healthcare, it is necessary to determine the minimum amount of data needed for accurate health assessment.

Here, we present a generalizable optimization framework for determining the minimum necessary sampling rate for wearable sensors and apply our method to determine optimal optical blood volume pulse sampling rate. We implement t-tests, Bland–Altman analysis, and regression-based visualizations to identify optimal sampling rates of wrist-worn optical sensors.

We determine the optimal sampling rate of wrist-worn optical sensors for heart rate and heart rate variability monitoring to be 21–64 Hz, depending on the metric.

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.

Shared decision-making (SDM) is a critical component of delivering patient-centered care. Members of vulnerable populations may play a passive role in clinical decision-making; therefore, understanding their prior decision-making experiences is a key step to engaging them in SDM.

To understand the previous healthcare experiences and current expectations of vulnerable populations on clinical decision-making regarding therapeutic options.

Clients of a local food bank were recruited to participate in focus groups. Participants were asked to share prior health decision experiences, explain difficulties they faced when making a therapeutic decision, describe features of previous satisfactory decision-making processes, share factors under consideration when choosing between treatment options, and suggest tools that would help them to communicate with healthcare providers. We used the inductive content analysis to interpret data gathered from the focus groups.

Twenty-six food bank clients participated in four focus groups. All participants lived in areas of socioeconomic disadvantage. Four themes emerged: prior negative clinical decision-making experience with providers, patients preparing to engage in SDM, challenges encountered during the decision-making process, and patients’ expectations of decision aids. Participants also reported they were unable to discuss therapeutic options at the time of decision-making. They also expressed financial concerns and the need for sufficiently detailed information to evaluate risks.

Our findings suggest the necessity of developing decision aids that would improve the engagement of vulnerable populations in the SDM process, including consideration of affordability, use of patient-friendly language, and incorporation of drug–drug and drug–food interactions information.

Air pollution is linked to mortality and morbidity. Since humans spend nearly all their time indoors, improving indoor air quality (IAQ) is a compelling approach to mitigate air pollutant exposure. To assess interventions, relying on clinical outcomes may require prolonged follow-up, which hinders feasibility. Thus, identifying biomarkers that respond to changes in IAQ may be useful to assess the effectiveness of interventions.

We conducted a narrative review by searching several databases to identify studies published over the last decade that measured the response of blood, urine, and/or salivary biomarkers to variations (natural and intervention-induced) of changes in indoor air pollutant exposure.

Numerous studies reported on associations between IAQ exposures and biomarkers with heterogeneity across study designs and methods. This review summarizes the responses of 113 biomarkers described in 30 articles. The biomarkers which most frequently responded to variations in indoor air pollutant exposures were high sensitivity C-reactive protein (hsCRP), von Willebrand Factor (vWF), 8-hydroxy-2′-deoxyguanosine (8-OHdG), and 1-hydroxypyrene (1-OHP).

This review will guide the selection of biomarkers for translational studies evaluating the impact of indoor air pollutants on human health.

Missing data are inevitable in medical research and appropriate handling of missing data is critical for statistical estimation and making inferences. Imputation is often employed in order to maximize the amount of data available for statistical analysis and is preferred over the typically biased output of complete case analysis. This article examines several types of regression imputation of missing covariates in the prediction of time-to-event outcomes subject to right censoring.

We evaluated the performance of five regression methods in the imputation of missing covariates for the proportional hazards model via summary statistics, including proportional bias and proportional mean squared error. The primary objective was to determine which among the parametric generalized linear models (GLMs) and least absolute shrinkage and selection operator (LASSO), and nonparametric multivariate adaptive regression splines (MARS), support vector machine (SVM), and random forest (RF), provides the “best” imputation model for baseline missing covariates in predicting a survival outcome.

LASSO on an average observed the smallest bias, mean square error, mean square prediction error, and median absolute deviation (MAD) of the final analysis model’s parameters among all five methods considered. SVM performed the second best while GLM and MARS exhibited the lowest relative performances.

LASSO and SVM outperform GLM, MARS, and RF in the context of regression imputation for prediction of a time-to-event outcome.