RNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of ...previously unknown exons are being reported. Initial reports of differences in exon usage, and splicing between samples as well as quantitative differences among samples are beginning to surface. Biological variation has been reported to be larger than technical variation. In addition, technical variation has been reported to be in line with expectations due to random sampling. However, strategies for dealing with technical variation will differ depending on the magnitude. The size of technical variance, and the role of sampling are examined in this manuscript.
In this study three independent Solexa/Illumina experiments containing technical replicates are analyzed. When coverage is low, large disagreements between technical replicates are apparent. Exon detection between technical replicates is highly variable when the coverage is less than 5 reads per nucleotide and estimates of gene expression are more likely to disagree when coverage is low. Although large disagreements in the estimates of expression are observed at all levels of coverage.
Technical variability is too high to ignore. Technical variability results in inconsistent detection of exons at low levels of coverage. Further, the estimate of the relative abundance of a transcript can substantially disagree, even when coverage levels are high. This may be due to the low sampling fraction and if so, it will persist as an issue needing to be addressed in experimental design even as the next wave of technology produces larger numbers of reads. We provide practical recommendations for dealing with the technical variability, without dramatic cost increases.
OBJECTIVE:This study profiles an innovative approach to capture patient satisfaction data from emergency department (ED) patients by implementing an electronic survey method. This study compares ...responders to nonresponders.
BACKGROUND:Our hypothesis is that the cohort of survey respondents will be similar to nonresponders in terms of the key characteristics of age, gender, race, ethnicity, ED disposition, and payor status.
METHODS:This study is a cross-sectional design using secondary data from the database and provides an opportunity for univariate analysis of the key characteristics for each group. The data elements will be abstracted from the database and compared with the same key characteristics from a similar sample from the database on nonresponders to the ED satisfaction survey.
FINDINGS:Age showed a statistically significant difference between responders and nonresponders. Comparison by disposition status showed no substantial difference between responders and nonresponders. Gender distribution showed a greater number of female than male responders. Race distribution showed a greater number and response by white and Asian patients as compared with African Americans. A review of ethnicity showed fewer Hispanics responded. An evaluation by payor classification showed greater number and response rate by those with a commercial or Workers Comp payor source. The response rate by Medicare recipients was stronger than expected; however, the response rate by Medicaid recipients and self-pay could be a concern for underrepresentation by lower socioeconomic groups. Finally, the evaluation of the method of notification showed that notification by both e-mail and text substantially improved response rates.
CONCLUSION:The evaluation of key characteristics showed no difference related to disposition, but differences related to age, gender, race, ethnicity, and payor classification. These results point to a potential concern for underrepresentation by lower socioeconomic groups. The results showed that notification by both e-mail and text substantially improved response rates.
Globalization and the expansion of transport networks has transformed migration into a major policy issue because of its effects on a range of phenomena, including resource flows in economics, ...urbanization, as well as the epidemiology of infectious diseases. Quantifying and modeling human migration can contribute towards a better understanding of the nature of migration and help develop evidence-based interventions for disease control policy, economic development, and resource allocation. In this study we paired census microdata from 10 countries in sub-Saharan Africa with additional spatial datasets to develop models for the internal migration flows in each country, including key drivers that reflect the changing social, demographic, economic, and environmental landscapes. We assessed how well these gravity-type spatial interaction models can both explain and predict migration. Results show that the models can explain up to 87 percent of internal migration, can predict future within-country migration with correlations of up to 0.91, and can also predict migration in other countries with correlations of up to 0.72. Findings show that such models are useful tools for understanding migration as well as predicting flows in regions where data are sparse, and can contribute towards strategic economic development, planning, and disease control targeting.
Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than ...between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA) to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r = 0.97) between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes.
In environmental studies, relationships among variables that are misaligned in space are routinely assessed. Because the data are misaligned, kriging is often used to predict the covariate at the ...locations where the response is observed. Using kriging predictions to estimate regression parameters in linear regression models introduces a Berkson error, which induces a covariance structure that is challenging to estimate. In addition, if the parameters associated with kriging (e.g. trend surface parameters and spatial covariance parameters) are estimated, then an additional uncertainty is introduced. We characterize the total measurement error as part of a broader class of Berkson error models and develop an estimated generalized least squares estimator using estimated covariance parameters. In working with the induced model, we fully account for the error structure and estimate the covariance parameters using likelihood-based methods. We provide insight into when it is important to fully account for the covariance structure induced from the different error sources. We assess the performance of the estimators using simulation and illustrate the methodology using publicly available data from the US Environmental Protection Agency.
Hospital-based Emergency Departments are struggling to provide timely care to a steadily increasing number of unscheduled ED visits. Dwindling compensation and rising ED closures dictate that meeting ...this challenge demands greater operational efficiency.
Using techniques from operations research theory, as well as a novel event-driven algorithm for processing priority queues, we developed a flexible simulation platform for hospital-based EDs. We tuned the parameters of the system to mimic U.S. nationally average and average academic hospital-based ED performance metrics and are able to assess a variety of patient flow outcomes including patient door-to-event times, propensity to leave without being seen, ED occupancy level, and dynamic staffing and resource use.
The causes of ED crowding are variable and require site-specific solutions. For example, in a nationally average ED environment, provider availability is a surprising, but persistent bottleneck in patient flow. As a result, resources expended in reducing boarding times may not have the expected impact on patient throughput. On the other hand, reallocating resources into alternate care pathways can dramatically expedite care for lower acuity patients without delaying care for higher acuity patients. In an average academic ED environment, bed availability is the primary bottleneck in patient flow. Consequently, adjustments to provider scheduling have a limited effect on the timeliness of care delivery, while shorter boarding times significantly reduce crowding. An online version of the simulation platform is available at http://spark.rstudio.com/klopiano/EDsimulation/.
In building this robust simulation framework, we have created a novel decision-support tool that ED and hospital managers can use to quantify the impact of proposed changes to patient flow prior to implementation.
Spatially referenced datasets arising from multiple sources are routinely combined to assess relationships among various outcomes and covariates. The geographical units associated with the data, such ...as the geographical coordinates or areal‐level administrative units, are often spatially misaligned, that is, observed at different locations or aggregated over different geographical units. As a result, the covariate is often predicted at the locations where the response is observed. The method used to align disparate datasets must be accounted for when subsequently modeling the aligned data. Here we consider the case where kriging is used to align datasets in point‐to‐point and point‐to‐areal misalignment problems when the response variable is non‐normally distributed. If the relationship is modeled using generalized linear models, the additional uncertainty induced from using the kriging mean as a covariate introduces a Berkson error structure. In this article, we develop a pseudo‐penalized quasi‐likelihood algorithm to account for the additional uncertainty when estimating regression parameters and associated measures of uncertainty. The method is applied to a point‐to‐point example assessing the relationship between low‐birth weights and PM₂.₅ levels after the onset of the largest wildfire in Florida history, the Bugaboo scrub fire. A point‐to‐areal misalignment problem is presented where the relationship between asthma events in Florida's counties and PM₂.₅ levels after the onset of the fire is assessed. Finally, the method is evaluated using a simulation study. Our results indicate the method performs well in terms of coverage for 95% confidence intervals and naive methods that ignore the additional uncertainty tend to underestimate the variability associated with parameter estimates. The underestimation is most profound in Poisson regression models.
When a response variable Y is measured on one set of points and a spatially varying predictor variable X is measured on a different set of points, X and Y have different supports and thus are ...spatially misaligned. To draw inference about the association between X and Y , X is commonly predicted at the points for which Y is observed, and Y is regressed on the predicted X. If X is predicted using kriging or some other smoothing approach, use of the predicted instead of the true (unobserved) X values in the regression results in unbiased estimates of the regression parameters. However, the naive standard errors of these parameters tend to be too small. In this article, two simulation studies are used to compare methods for providing appropriate standard errors in this spatial setting. Three of the methods are extended to the change-of-support case where X is observed at points, but Y is observed for areal units, and these approaches are also compared via simulation.
The United States Department of Agriculture’s National Agricultural Statistics Service (NASS) conducts the June Agricultural Survey (JAS) annually. Substantial misclassification occurs during the ...prescreening process and from field-estimating farm status for nonresponse and inaccessible records, resulting in a biased estimate of the number of US farms from the JAS. Here, the Annual Land Utilization Survey (ALUS) is proposed as a follow-on survey to the JAS to adjust the estimates of the number of US farms and other important variables. A three-phase survey design-based estimator is developed for the JAS-ALUS with nonresponse adjustment for the second phase (ALUS). A design-unbiased estimator of the variance is provided in explicit form.
The proliferation of electronic health records, driven by advances in technology and legislative measures, is stimulating interest in the analysis of passively collected administrative and clinical ...data. Observational data present exciting challenges and opportunities to researchers interested in comparing the effectiveness of different treatment regimes and, as personalized medicine requires, estimating how effectiveness varies among subgroups. In this study, we provide new motivation for the local control approach to the analysis of large observational datasets in which patients are first clustered in pretreatment covariate space and treatment comparisons are made within subgroups of similar patients. The motivation for such an analysis is that the resulting local treatment effect estimates make inherently fair comparisons even when treatment cohorts suffer variation in balance (treatment choice fraction) across pretreatment covariate space. We use an example of Simpson's paradox to show that estimates of the overall average treatment effect, which marginalize over covariate space, can be misleading. Thus, we provide an alternative definition that uses a single, shared marginal distribution to define overall treatment comparisons that are inherently fair given the observed covariates. However, we also argue that overall treatment comparisons should no longer be the focus of comparative effectiveness research; the possibility that treatment effectiveness does vary across patient subpopulations must not be left unexplored. In the spirit of the now ubiquitous concept of personalized medicine, estimating heterogeneous treatment effects in clinically relevant subgroups will allow for, within the limits of the available data, fair treatment comparisons that are more relevant to individual patients.
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