PCORnet's Collaborative Research Groups.

The Patient-Centered Outcomes Research Institute (PCORI) launched a multi-institutional "network of networks" in 2013 - Patient-Centered Clinical Research Network (PCORnet) - that is designed to conduct clinical research that is faster, less expensive, and more responsive to the information needs of patients and clinicians. To enhance cross-network and cross-institutional collaboration and catalyze the use of PCORnet, PCORI has supported formation of 11 Collaborative Research Groups focusing on specific disease types (e.g., cardiovascular health and cancer) or particular patient populations (e.g., pediatrics and health disparities). PCORnet's Collaborative Research Groups are establishing research priorities within these focus areas, establishing relationships with potential funders, and supporting development of specific research projects that will use PCORnet resources. PCORnet remains a complex, multilevel, and heterogeneous network that is still maturing and building a diverse portfolio of observational and interventional people-centered research; engaging with PCORnet can be daunting, particularly for outside investigators. We believe the Collaborative Research Groups are stimulating interest and helping investigators navigate the complexity, but only time will tell if these efforts will bear fruit in terms of funded multicenter PCORnet projects

A Hierarchical Multivariate Two-Part Model for Profiling Providers\u27 Effects on Healthcare Charges

Procedures for analyzing and comparing healthcare providers\u27 effects on health services delivery and outcomes have been referred to as provider profiling. In a typical profiling procedure, patient-level responses are measured for clusters of patients treated by providers that in turn, can be regarded as statistically exchangeable. Thus, a hierarchical model naturally represents the structure of the data. When provider effects on multiple responses are profiled, a multivariate model rather than a series of univariate models, can capture associations among responses at both the provider and patient levels. When responses are in the form of charges for healthcare services and sampled patients include non-users of services, charge variables are a mix of zeros and highly-skewed positive values that present a modeling challenge. For analysis of regressor effects on charges for a single service, a frequently used approach is a two-part model (Duan, Manning, Morris, and Newhouse 1983) that combines logistic or probit regression on any use of the service and linear regression on the log of positive charges given use of the service. Here, we extend the two-part model to the case of charges for multiple services, using a log-linear model and a general multivariate log-normal model, and employ the resultant multivariate two-part model as the within-provider component of a hierarchical model. The log-linear likelihood is reparameterized as proposed by Fitzmaurice and Laird (1993), so that regressor effects on any use of each service are marginal with respect to any use of other services. The general multivariate log-normal likelihood is constructed in such a way that variances of log of positive charges for each service are provider-specific but correlations between log of positive charges for different services are uniform across providers. A data augmentation step is included in the Gibbs sampler used to fit the hierarchical model, in order to accommodate the fact that values of log of positive charges are undefined for unused service. We apply this hierarchical, multivariate, two-part model to analyze the effects of primary care physicians on their patients\u27 annual charges for two services, primary care and specialty care. Along the way, we also demonstrate an approach for incorporating prior information about the effects of patient morbidity on response variables, to improve the accuracy of provider profiles that are based on patient samples of limited size

Studying Effects of Primary Care Physicians and Patients on the Trade-Off Between Charges for Primary Care and Specialty Care Using a Hierarchical Multivariate Two-Part Model

Objective. To examine effects of primary care physicians (PCPs) and patients on the association between charges for primary care and specialty care in a point-of-service (POS) health plan. Data Source. Claims from 1996 for 3,308 adult male POS plan members, each of whom was assigned to one of the 50 family practitioner-PCPs with the largest POS plan member-loads. Study Design. A hierarchical multivariate two-part model was fitted using a Gibbs sampler to estimate PCPs\u27 effects on patients\u27 annual charges for two types of services, primary care and specialty care, the associations among PCPs\u27 effects, and within-patient associations between charges for the two services. Adjusted Clinical Groups (ACGs) were used to adjust for case-mix. Principal Findings. PCPs with higher case-mix adjusted rates of specialist use were less likely to see their patients at least once during the year (estimated correlation: –.40; 95% CI: –.71, –.008) and provided fewer services to patients that they saw (estimated correlation: –.53; 95% CI: –.77, –.21). Ten of 11 PCPs whose case-mix adjusted effects on primary care charges were significantly less than or greater than zero (p \u3c .05) had estimated, case-mix adjusted effects on specialty care charges that were of opposite sign (but not significantly different than zero). After adjustment for ACG and PCP effects, the within-patient, estimated odds ratio for any use of primary care given any use of specialty care was .57 (95% CI: .45, .73). Conclusions. PCPs and patients contributed independently to a trade-off between utilization of primary care and specialty care. The trade-off appeared to partially offset significant differences in the amount of care provided by PCPs. These findings were possible because we employed a hierarchical multivariate model rather than separate univariate models

An association between lifespan and variation in insulin-like growth factor I receptor in sheep

Longevity in livestock is a valuable trait. When productive animals live longer, fewer replacement animals need to be raised. However, selection for longevity is not commonly the focus of breeding programs as direct selection for long-lived breeding stock is virtually impossible until late in the reproductive life of the animal. Additionally the underlying genetic factors or genes associated with longevity are either not known, or not well understood. In humans, there is evidence that IGF 1 receptor (IGF1R) is involved in longevity. Polymorphism in the IGF1R gene has been associated with longevity in a number of species. Recently, 3 alleles of ovine IGF1R were identified, but no analysis of the effect of IGF1R variation on sheep longevity has been reported. In this study, associations between ovine IGF1R variation, longevity and fertility were investigated. Polymerase chain reaction-single strand conformational polymorphism (PCR-SSCP) was used to type IGF1R variation in 1,716 New Zealand sheep belonging to 6 breeds and 36 flocks. Ovine IGF1R C was associated with age when adjusting for flock (present 5.5 ± 0.2 yr, absent 5.0 ± 0.1 yr, P = 0.02). A general linear mixed effects model suggested an association (P = 0.06) between age and genotype, when correcting for flock. Pairwise comparison (least significant difference) of specific genotypes revealed the difference to be between AA (5.0± 0.1 yr) and AC (5.6 ± 0.2 yr, P = 0.02). A weak negative Pearson correlation between fertility and longevity traits was observed (r = -0.25, P < 0.01). The finding of an association between variation in IGF1R and lifespan in sheep may be useful in prolonging the lifespan of sheep

The Kepler DB, a Database Management System for Arrays, Sparse Arrays and Binary Data

The Kepler Science Operations Center stores pixel values on approximately six million pixels collected every 30-minutes, as well as data products that are generated as a result of running the Kepler science processing pipeline. The Kepler Database (Kepler DB) management system was created to act as the repository of this information. After one year of ight usage, Kepler DB is managing 3 TiB of data and is expected to grow to over 10 TiB over the course of the mission. Kepler DB is a non-relational, transactional database where data are represented as one dimensional arrays, sparse arrays or binary large objects. We will discuss Kepler DB's APIs, implementation, usage and deployment at the Kepler Science Operations Center

Detection of Potential Transit Signals in Sixteen Quarters of Kepler Mission Data

We present the results of a search for potential transit signals in four years of photometry data acquired by the Kepler Mission. The targets of the search include 111,800 stars which were observed for the entire interval and 85,522 stars which were observed for a subset of the interval. We found that 9,743 targets contained at least one signal consistent with the signature of a transiting or eclipsing object, where the criteria for detection are periodicity of the detected transits, adequate signal-to-noise ratio, and acceptance by a number of tests which reject false positive detections. When targets that had produced a signal were searched repeatedly, an additional 6,542 signals were detected on 3,223 target stars, for a total of 16,285 potential detections. Comparison of the set of detected signals with a set of known and vetted transit events in the Kepler field of view shows that the recovery rate for these signals is 96.9%. The ensemble properties of the detected signals are reviewed.Comment: Accepted by ApJ Supplemen

Mapping Past, Present, and Future Climatic Suitability for Invasive Aedes Aegypti and Aedes Albopictus in the United States: A Process-Based Modeling Approach Using CMIP5 Downscaled Climate Scenarios

The ongoing spread of the mosquitoes, Aedes aegypti and Aedes albopictus, in the continental United States leaves new areas at risk for local transmission of dengue, chikungunya, and Zika viruses. All three viruses have caused major disease outbreaks in the Americas with infected travelers returning regularly to the U.S. The expanding range of these mosquitoes raises questions about whether recent spread has been enabled by climate change or other anthropogenic influences. In this analysis, we used downscaled climate scenarios from the NASA Earth Exchange Global Daily Downscaled Projections (NEX GDDP) dataset to model Ae. aegypti and Ae. albopictus population growth rates across the United States. We used a stage-structured matrix population model to understand past and present climatic suitability for these vectors, and to project future suitability under CMIP5 climate change scenarios. Our results indicate that much of the southern U.S. is suitable for both Ae. aegypti and Ae. albopictus year-round. In addition, a large proportion of the U.S. is seasonally suitable for mosquito population growth, creating the potential for periodic incursions into new areas. Changes in climatic suitability in recent decades for Ae. aegypti and Ae. albopictus have occurred already in many regions of the U.S., and model projections of future climate suggest that climate change will continue to reshape the range of Ae. aegypti and Ae. albopictus in the U.S., and potentially the risk of the viruses they transmit