Summary Motivated by an imaging study, this paper develops a nonparametric testing procedure for testing the null hypothesis that two samples of curves observed at discrete grids and with noise have the same underlying distribution. The objective is to formally compare white matter tract profiles between healthy individuals and multiple sclerosis patients, as assessed by conventional diffusion tensor imaging measures. We propose to decompose the curves using functional principal component analysis of a mixture process, which we refer to as marginal functional principal component analysis. This approach reduces the dimension of the testing problem in a way that enables the use of traditional nonparametric univariate testing procedures. The procedure is computationally efficient and accommodates different sampling designs. Numerical studies are presented to validate the size and power properties of the test in many realistic scenarios. In these cases, the proposed test has been found to be more powerful than its primary competitor. Application to the diffusion tensor imaging data reveals that all the tracts studied are associated with multiple sclerosis and the choice of the diffusion tensor image measurement is important when assessing axonal disruption.

An increasingly important data source for the development of clinical risk prediction models are Electronic health records (EHRs). One of their key advantages is that they contain data on many individuals collected over time. This allows one to incorporate more clinical information into a risk model. However, traditional methods for developing risk models are not well suited to these irregularly collected clinical covariates. In this paper, we compare a range of approaches for using longitudinal predictors in a clinical risk model. Using data from an EHR for patients undergoing hemodialysis, we incorporate five different clinical predictors to a risk model for patient mortality. We consider different approaches for treating the repeated measurements including use of summary statistics, machine learning methods, functional data analysis and joint models. We follow-up our empirical findings with a simulation study. Overall, our results suggest that simple approaches perform just as well, if not better, than more complex analytic approaches. These results have important implication for development of risk prediction models with EHRs.

No abstract

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. The training strategy also incorporates a “train the trainer” approach to enable CBs who have successfully completed training to train new staff or faculty.

The presence of pulmonary hypertension (PH) significantly worsens outcomes in patients with advanced sarcoidosis, but its optimal management is unknown. We aimed to characterize a large sarcoidosis-associated pulmonary hypertension (SAPH) cohort to better understand patient characteristics, clinical outcomes, and management strategies including treatment with PH therapies. Patients at Duke University Medical Center with biopsy-proven sarcoidosis and SAPH confirmed by right heart catheterization (RHC) were identified from 1990–2010. Subjects were followed for up to 11 years and assessed for differences by treatment strategy for their SAPH, including those who were not treated with PH-specific therapies. Our primary outcomes of interest were change in 6-minute walk distance (6MWD) and change in N-terminal pro-brain natriuretic peptide (NT-proBNP) by after therapy. We included 95 patients (76% women, 86% African American) with SAPH. Overall, 70% of patients had stage IV pulmonary sarcoidosis, and 77% had functional class III/IV symptoms. Median NT-proBNP value was elevated (910 pg/mL), and right ventricular dysfunction was moderate/severe in 55% of patients. Median values for mean pulmonary artery pressure (49 mmHg) and pulmonary vascular resistance (8.5 Woods units) were consistent with severe pulmonary hypertension. The mortality rate over median 3-year follow-up was 32%. Those who experienced a clinical event and those who did not had similar overall echocardiographic findings, hemodynamics, 6MWD and NT-proBNP at baseline, and unadjusted analysis showed that only follow-up NT-proBNP was associated with all-cause hospitalization or mortality. A sign test to evaluate the difference between NT-Pro-BNP before and after PH therapy produced evidence that a significant difference existed between the median pre- and post-NT-Pro-BNP (−387.0 (IQR: −1373.0-109), p = 0.0495). Use of PH-specific therapy may be helpful in selected patients with SAPH and pre-capillary pulmonary vascular disease. Prospective trials are needed to characterize responses to PH-specific therapy in this subset of patients with SAPH.

The field of data science has great potential to address critical questions relevant for academic medical centers. Data science initiatives are consequently being established within academic medicine. At the cornerstone of such initiatives are scientists who practice data science. These scientists include biostatisticians, clinical informaticians, database and software developers, computational scientists, and computational biologists. Too often, however, those involved in the practice of data science are viewed by academic leadership as providing a noncomplex service to facilitate research and further the careers of other academic faculty. The authors contend that the success of data science initiatives relies heavily on the understanding that the practice of data science is a critical intellectual contribution to the overall science conducted at an academic medical center. Further, careful thought by academic leadership is needed for allocation of resources devoted to the practice of data science. At the Stanford University School of Medicine, the authors have developed an innovative model for a data science collaboratory based on 4 fundamental elements: an emphasis on collaboration over consultation, a subscription-based funding mechanism that reflects commitment by key partners, an investment in the career development of faculty who practice data science, and a strong educational component for data science members in team science and for clinical and translational investigators in data science. As data science becomes increasingly essential to learning health systems, centers that specialize in the practice of data science are a critical component of the research infrastructure and intellectual environment of academic medical centers.

No abstract

Background Treatment algorithms for large soft tissue defects in lower extremity trauma are not clearly defined. The aim of this study is to determine if there are geographic or demographic differences in the management of open tibia fractures with soft tissue defects requiring either soft tissue reconstruction or amputation in the United States (US). Methods A retrospective analysis was performed on the Nationwide Inpatient Sample (NIS), 2000 to 2011. We evaluated flap and amputation rates in the open tibia fractures with soft tissue defects based on geographic and socioeconomic factors. Results From 2000 to 2011, there were 175,283 open tibia fractures in the US; 7.2% (n = 12,620) had a concomitant soft tissue defect requiring either flap or amputation. The overall flap rate was 73.2% (n = 9,235). When compared with the South at 68%, flap rates were highest in the West at 79% (adjusted odds ratio [AOR] = 2.06; 95% confidence interval [CI] = 1.49, 2.86; p < 0.0001), followed by the Northeast at 77% (AOR = 1.63; 95% CI = 1.22, 2.19; p = 0.001), and the Midwest at 74% (AOR = 1.76; 95% CI = 1.25, 2.47; p = 0.001). Flap rates were lower in the rural hospitals in the West (AOR = 0.24; 95% CI = 0.07, 0.84; p = 0.03) and Northeast (AOR = 0.55; 95% CI = 0.37, 0.82; p = 0.003) when compared with the urban hospitals. Flap rates were highest in the highest income quartile at 77% (AOR = 1.53; 95% CI = 1.05, 2.25; p = 0.03) compared with 72% in the lowest income quartile. Conclusions Reconstruction rates were significantly higher in three major US regions when compared with the South, urban hospitals in the West and Northeast, and the highest income quartile.