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Abstract Background Exome sequencing (ES) has probable utility for shortening the diagnostic odyssey of children with suspected genetic disorders. This report describes the design and methods of a study evaluating the potential of ES as a routine clinical tool for pediatric patients who have suspected genetic conditions and who are in the early stages of the diagnostic odyssey. Methods The North Carolina Clinical Genomic Evaluation by Next-generation Exome Sequencing (NCGENES) 2 study is an interdisciplinary, multi-site Phase III randomized controlled trial of two interventions: educational pre-visit preparation (PVP) and offer of first-line ES. In this full-factorial design, parent-child dyads are randomly assigned to one of four study arms (PVP + usual care, ES + usual care, PVP + ES + usual care, or usual care alone) in equal proportions. Participants are recruited from Pediatric Genetics or Neurology outpatient clinics in three North Carolina healthcare facilities. Eligible pediatric participants are

Summary: Integrating data across heterogeneous research environments is a key challenge in multi-site, collaborative research projects. While it is important to allow for natural variation in data collection protocols across research sites, it is also important to achieve interoperability between datasets in order to reap the full benefits of collaborative work. However, there are few standards to guide the data coordination process from project conception to completion. In this paper, we describe the experiences of the Clinical Sequence Evidence-Generating Research (CSER) consortium Data Coordinating Center (DCC), which coordinated harmonized survey and genomic sequencing data from seven clinical research sites from 2020 to 2022. Using input from multiple consortium working groups and from CSER leadership, we first identify 14 lessons learned from CSER in the categories of communication, harmonization, informatics, compliance, and analytics. We then distill these lessons learned into 11 recommendations for future research consortia in the areas of planning, communication, informatics, and analytics. We recommend that planning and budgeting for data coordination activities occur as early as possible during consortium conceptualization and development to minimize downstream complications. We also find that clear, reciprocal, and continuous communication between consortium stakeholders and the DCC is equally important to maintaining a secure and centralized informatics ecosystem for pooling data. Finally, we discuss the importance of actively interrogating current approaches to data governance, particularly for research studies that straddle the research-clinical divide.

Objective To examine the prevalence and predictors of patient-reported barriers to care among survivors of head and neck squamous cell carcinoma and the association with health-related quality of life (HRQOL) outcomes. Study Design Retrospective cohort study. Setting Outpatient oncology clinic at an academic tertiary care center. Methods Data were obtained from the UNC Health Registry/Cancer Survivorship Cohort. Barriers to care included self-reported delays in care and inability to obtain needed care due to cost. HRQOL was measured with validated questionnaires: general (PROMIS) and cancer specific (FACT-GP). Results The sample included 202 patients with head and neck squamous cell carcinoma with a mean age of 59.6 years (SD, 10.0). Eighty-two percent were male and 87% were White. Sixty-two patients (31%) reported at least 1 barrier to care. Significant predictors of a barrier to care in unadjusted analysis included age ≤60 years ( P = .007), female sex ( P = .020), being unmarried ( P = .016), being uninsured ( P = .047), and Medicaid insurance ( P = .022). Patients reporting barriers to care had significantly worse physical and mental HRQOL on the PROMIS questionnaires ( P < .001 and P = .002, respectively) and lower cancer-specific HRQOL on the FACT-GP questionnaire ( P < .001), which persisted across physical, social, emotional, and functional domains. There was no difference in 5-year OS (75.3% vs 84.1%, P = .177) or 5-year CSS (81.6% vs 85.4%, P = .542) in patients with and without barriers to care. Conclusion Delay- and affordability-related barriers are common among survivors of head and neck cancer and appear to be associated with significantly worse HRQOL outcomes. Certain sociodemographic groups appear to be more at risk of patient-reported barriers to care.

Abstract Background Appropriate depression care is a cancer‐care priority. However, many cancer survivors live with undiagnosed and untreated depression. Prostate cancer survivors may be particularly vulnerable, but little is known about their access to depression care. The goal of this study was to describe patterns and predictors of clinical diagnosis and treatment of depression in prostate cancer survivors. Methods Generalized estimating equations were used to evaluate indicators of self‐reported clinical diagnosis and treatment depression as a function of individual‐level characteristics within a longitudinal dataset. The data were from a population‐based cohort of North Carolinian prostate cancer survivors who were enrolled from 2004 to 2007 on the North Carolina‐Louisiana Prostate Cancer Project (N = 1,031), and prospectively followed annually from 2008 to 2011 on the Health Care Access and Prostate Cancer Treatment in North Carolina (N = 805). Results The average rate of self‐reported clinical diagnosis of depression was 44% (95% CI: 39%‐49%), which declined from 60% to 40% between prostate cancer diagnosis and 5‐7 years later. Factors associated with lower odds of self‐reported clinical diagnosis of depression include African‐American race, employment, age at enrollment, low education, infrequent primary care visits, and living with a prostate cancer diagnosis for more than 2 years. The average rate of self‐reported depression treatment was 62% (95% CI: 55%‐69%). Factors associated with lower odds of self‐reported depression treatment included employment and living with a prostate cancer diagnosis for 2 or more years. Conclusion Prostate cancer survivors experience barriers when in need of depression care.

Constraints arise naturally in many scientific experiments/studies such as in, epidemiology, biology, toxicology, etc. and often researchers ignore such information when analyzing their data and use standard methods such as the analysis of variance (ANOVA). Such methods may not only result in a loss of power and efficiency in costs of experimentation but also may result poor interpretation of the data. In this paper we discuss constrained statistical inference in the context of linear mixed effects models that arise naturally in many applications, such as in repeated measurements designs, familial studies and others. We introduce a novel methodology that is broadly applicable for a variety of constraints on the parameters. Since in many applications sample sizes are small and/or the data are not necessarily normally distributed and furthermore error variances need not be homoscedastic (i.e. heterogeneity in the data) we use an empirical best linear unbiased predictor (EBLUP) type residual based bootstrap methodology for deriving critical values of the proposed test. Our simulation studies suggest that the proposed procedure maintains the desired nominal Type I error while competing well with other tests in terms of power. We illustrate the proposed methodology by re-analyzing a clinical trial data on blood mercury level. The methodology introduced in this paper can be easily extended to other settings such as nonlinear and generalized regression models.