Exploring the variability of DNA molecules via principal geodesic analysis on the shape space

Most of the linear statistics deal with data lying in a Euclidean space. However, there are many examples, such as DNA molecule topological structures, in which the initial or the transformed data lie in a non-Euclidean space. To get a measure of variability in these situations, the principal component analysis (PCA) is usually performed on a Euclidean tangent space as it cannot be directly implemented on a non-Euclidean space. Instead, principal geodesic analysis (PGA) is a new tool that provides a measure of variability for nonlinear statistics. In this paper, the performance of this new tool is compared with that of the PCA using a real data set representing a DNA molecular structure. It is shown that due to the nonlinearity of space, the PGA explains more variability of the data than the PCA.     

Statistical profiling methods with hierarchical logistic regression for healthcare providers with binary outcomes

Within the context of California's public report of coronary artery bypass graft (CABG) surgery outcomes, we first thoroughly review popular statistical methods for profiling healthcare providers. Extensive simulation studies are then conducted to compare profiling schemes based on hierarchical logistic regression (LR) modeling under various conditions. Both Bayesian and frequentist's methods are evaluated in classifying hospitals into ‘better’, ‘normal’ or ‘worse’ service providers. The simulation results suggest that no single method would dominate others on all accounts. Traditional schemes based on LR tend to identify too many false outliers, while those based on hierarchical modeling are relatively conservative. The issue of over shrinkage in hierarchical modeling is also investigated using the 2005–2006 California CABG data set. The article provides theoretical and empirical evidence in choosing the right methodology for provider profiling.     

幼女“产婴证奸”问题探析

近来,幼女＂产婴证奸＂的事件屡屡见诸报端。这一方面凸显了我国救助机制的缺失,实现正义的代价过于高昂;另一方面还引发了一些现实问题和法律难题。针对这种状况,建议完善相关立法,构建相应的社会救助机制,加强对这一群体的普法及性教育,培养其自我保护意识,避免＂产婴证奸＂现象的再次发生。     

Characterizing the reconstruction and enumerating the patterns of DNA sequences with repeats

A repeat in a DNA sequence is a substring that appears more than once. In DNA sequencing, the occurrence of repeats may hinder the unique reconstruction. In addition, the number of possible reconstructions depends on the pattern of repeats in a DNA sequence. Arratia et al. studied the patterns of DNA sequences with twofold repeats that result in k-way reconstructions. In this paper, multiple-fold repeats, including twofold repeats, are considered. For each pattern of DNA repeats, the possible reconstructions of the DNA sequence are enumerated by its reduced digraph. Then the reconstructions of DNA sequences with repeats are characterized using the pattern graphs. Finally, for DNA sequences with n repeats, the patterns of DNA repeats resulting in k-way reconstruction are enumerated. Dedicated to Professor Frank K. Hwang on the occasion of his 65th birthday.     

Reducing Hospital Readmissions by Integrating Empirical Prediction with Resource Optimization

Hospital readmissions present an increasingly important challenge for health‐care organizations. Readmissions are expensive and often unnecessary, putting patients at risk and costing $15 billion annually in the United States alone. Currently, 17% of Medicare patients are readmitted to a hospital within 30 days of initial discharge with readmissions typically being more expensive than the original visit to the hospital. Recent legislation penalizes organizations with a high readmission rate. The medical literature conjectures that many readmissions can be avoided or mitigated by post‐discharge monitoring. To develop a good monitoring plan it is critical to anticipate the timing of a potential readmission and to effectively monitor the patient for readmission causing conditions based on that knowledge. This research develops new methods to empirically generate an individualized estimate of the time to readmission density function and then uses this density to optimize a post‐discharge monitoring schedule and staffing plan to support monitoring needs. Our approach integrates classical prediction models with machine learning and transfer learning to develop an empirical density that is personalized to each patient. We then transform an intractable monitoring plan optimization with stochastic discharges and health state evolution based on delay‐time models into a weakly coupled network flow model with tractable subproblems after applying a new pruning method that leverages the problem structure. Using this multi‐methodologic approach on two large inpatient datasets, we show that optimal readmission prediction and monitoring plans can identify and mitigate 40–70% of readmissions before they generate an emergency readmission.