Calculating partial expected value of perfect information via Monte Carlo sampling algorithms

Partial expected value of perfect information (EVPI) calculations can quantify the value of learning about particular subsets of uncertain parameters in decision models. Published case studies have used different computational approaches. This article examines the computation of partial EVPI estimates via Monte Carlo sampling algorithms. The mathematical definition shows 2 nested expectations, which must be evaluated separately because of the need to compute a maximum between them. A generalized Monte Carlo sampling algorithm uses nested simulation with an outer loop to sample parameters of interest and, conditional upon these, an inner loop to sample remaining uncertain parameters. Alternative computation methods and shortcut algorithms are discussed and mathematical conditions for their use considered. Maxima of Monte Carlo estimates of expectations are biased upward, and the authors show that the use of small samples results in biased EVPI estimates. Three case studies illustrate 1) the bias due to maximization and also the inaccuracy of shortcut algorithms 2) when correlated variables are present and 3) when there is nonlinearity in net benefit functions. If relatively small correlation or nonlinearity is present, then the shortcut algorithm can be substantially inaccurate. Empirical investigation of the numbers of Monte Carlo samples suggests that fewer samples on the outer level and more on the inner level could be efficient and that relatively small numbers of samples can sometimes be used. Several remaining areas for methodological development are set out. A wider application of partial EVPI is recommended both for greater understanding of decision uncertainty and for analyzing research priorities

Restrictive ID policies: implications for health equity

We wish to thank Synod Community Services for their critical work to develop, support, and implement a local government-issued ID in Washtenaw County, MI. We also thank Yousef Rabhi of the Michigan House of Representatives and Janelle Fa'aola of the Washtenaw ID Task Force, Lawrence Kestenbaum of the Washtenaw County Clerk's Office, Sherriff Jerry Clayton of the Washtenaw County Sherriff's Office, and the Washtenaw ID Task Force for their tireless commitment to developing and supporting the successful implementation of the Washtenaw ID. Additionally, we thank Vicenta Vargas and Skye Hillier for their contributions to the Washtenaw ID evaluation. We thank the Curtis Center for Research and Evaluation at the University of Michigan School of Social Work, the National Center for Institutional Diversity at the University of Michigan, and the University of California-Irvine Department of Chicano/Latino Studies and Program in Public Health for their support of the Washtenaw ID community-academic research partnership. Finally, we thank the reviewers for their helpful comments on earlier drafts of this manuscript. (Curtis Center for Research and Evaluation at the University of Michigan School of Social Work; National Center for Institutional Diversity at the University of Michigan; University of California-Irvine Department of Chicano/Latino Studies; Program in Public Health)https://link.springer.com/content/pdf/10.1007/s10903-017-0579-3.pdfPublished versio

Glioblastoma Subclasses Can Be Defined by Activity among Signal Transduction Pathways and Associated Genomic Alterations

Glioblastoma multiforme (GBM) is an umbrella designation that includes a heterogeneous group of primary brain tumors. Several classification strategies of GBM have been reported, some by clinical course and others by resemblance to cell types either in the adult or during development. From a practical and therapeutic standpoint, classifying GBMs by signal transduction pathway activation and by mutation in pathway member genes may be particularly valuable for the development of targeted therapies.We performed targeted proteomic analysis of 27 surgical glioma samples to identify patterns of coordinate activation among glioma-relevant signal transduction pathways, then compared these results with integrated analysis of genomic and expression data of 243 GBM samples from The Cancer Genome Atlas (TCGA). In the pattern of signaling, three subclasses of GBM emerge which appear to be associated with predominance of EGFR activation, PDGFR activation, or loss of the RAS regulator NF1. The EGFR signaling class has prominent Notch pathway activation measured by elevated expression of Notch ligands, cleaved Notch receptor, and downstream target Hes1. The PDGF class showed high levels of PDGFB ligand and phosphorylation of PDGFRbeta and NFKB. NF1-loss was associated with lower overall MAPK and PI3K activation and relative overexpression of the mesenchymal marker YKL40. These three signaling classes appear to correspond with distinct transcriptomal subclasses of primary GBM samples from TCGA for which copy number aberration and mutation of EGFR, PDGFRA, and NF1 are signature events.Proteomic analysis of GBM samples revealed three patterns of expression and activation of proteins in glioma-relevant signaling pathways. These three classes are comprised of roughly equal numbers showing either EGFR activation associated with amplification and mutation of the receptor, PDGF-pathway activation that is primarily ligand-driven, or loss of NF1 expression. The associated signaling activities correlating with these sentinel alterations provide insight into glioma biology and therapeutic strategies

MR imaging features of benign retroperitoneal extra-adrenal paragangliomas

The goal of this study was to retrospectively review the magnetic resonance imaging (MRI) features of retroperitoneal extra-adrenal paragangliomas and to evaluate the diagnostic capabilities of MRI. Twenty-four patients with confirmed benign retroperitoneal extra-adrenal paragangliomas who underwent preoperative MRI and surgical resection were enrolled. The patients’ clinical characteristics and MRI features were reviewed by two radiologists. There were no significant differences in the qualitative and quantitative MRI features were determined by the reviewers. High signal intensity in T2- weighted imaging (T2WI) and diffusion-weighted imaging (DWI) was observed in all tumors. In contrast T1-weighted imaging (T1WI) in the arterial phase, 83.33% of the tumors were clearly enhanced. In 87.5% of cases, a persistent enhancement pattern was observed in the venous and delayed phases, and 12.5% of tumors showed a “washout” pattern. The tumor capsule, intratumoral septum and degenerations were visualized in the tumors and may be helpful in the qualitative diagnosis of extraadrenal paragangliomas in MRI. MRI was useful in locating the position, determining the tumor ranges and visualizing the relationship between the tumors and adjacent structures. The presence of typical clinical symptoms and positivity of biochemical tests are also important factors in making an accurate preoperative diagnosis

A note on the pricing of multivariate contingent claims under a transformed-gamma distribution

We develop a framework for pricing multivariate European-style contingent claims in a discrete-time economy based on a multivariate transformed-gamma distribution. In our model, each transformed-gamma distributed underlying asset depends on two terms: a idiosyncratic term and a systematic term, where the latter is the same for all underlying assets and has a direct impact on their correlation structure. Given our distributional assumptions and the existence of a representative agent with a standard utility function, we apply equilibrium arguments and provide sufficient conditions for obtaining preference-free contingent claim pricing equations. We illustrate the applicability of our framework by providing examples of preference-free contingent claim pricing models. Multivariate pricing models are of particular interest when payoffs depend on two or more underlying assets, such as crack and crush spread options, options to exchange one asset for another, and options with a stochastic strike price in general

Prospective, multicentre study of external ventricular drainage-related infections in the UK and Ireland.

OBJECTIVES: External ventricular drain (EVD) insertion is a common neurosurgical procedure. EVD-related infection (ERI) is a major complication that can lead to morbidity and mortality. In this study, we aimed to establish a national ERI rate in the UK and Ireland and determine key factors influencing the infection risk. METHODS: A prospective multicentre cohort study of EVD insertions in 21 neurosurgical units was performed over 6 months. The primary outcome measure was 30-day ERI. A Cox regression model was used for multivariate analysis to calculate HR. RESULTS: A total of 495 EVD catheters were inserted into 452 patients with EVDs remaining in situ for 4700 days (median 8 days; IQR 4-13). Of the catheters inserted, 188 (38%) were antibiotic-impregnated, 161 (32.5%) were plain and 146 (29.5%) were silver-bearing. A total of 46 ERIs occurred giving an infection risk of 9.3%. Cox regression analysis demonstrated that factors independently associated with increased infection risk included duration of EVD placement for ≥8 days (HR=2.47 (1.12-5.45); p=0.03), regular sampling (daily sampling (HR=4.73 (1.28-17.42), p=0.02) and alternate day sampling (HR=5.28 (2.25-12.38); p<0.01). There was no association between catheter type or tunnelling distance and ERI. CONCLUSIONS: In the UK and Ireland, the ERI rate was 9.3% during the study period. The study demonstrated that EVDs left in situ for ≥8 days and those sampled more frequently were associated with a higher risk of infection. Importantly, the study showed no significant difference in ERI risk between different catheter types

Sins of Omission

Little is known about the relative incidence of serious errors of omission versus errors of commission. Objective : To identify the most common substantive medical errors identified by medical record review. Design : Retrospective cohort study. Setting : Twelve Veterans Affairs health care systems in 2 regions. Participants : Stratified random sample of 621 patients receiving care over a 2-year period. Main Outcome Measure : Classification of reported quality problems. Methods : Trained physicians reviewed the full inpatient and outpatient record and described quality problems, which were then classified as errors of omission versus commission. Results : Eighty-two percent of patients had at least 1 error reported over a 13-month period. The average number of errors reported per case was 4.7 (95% confidence intervals [CI]: 4.4, 5.0). Overall, 95.7% (95% CI: 94.9%, 96.4%) of errors were identified as being problems with underuse. Inadequate care for people with chronic illnesses was particularly common. Among errors of omission, obtaining insufficient information from histories and physicals (25.3%), inadequacies in diagnostic testing (33.9%), and patients not receiving needed medications (20.7%) were all common. Out of the 2,917 errors identified, only 27 were rated as being highly serious, and 26 (96%) of these were errors of omission. Conclusions : While preventing iatrogenic injury resulting from medical errors is a critically important part of quality improvement, we found that the overwhelming majority of substantive medical errors identifiable from the medical record were related to people getting too little medical care, especially for those with chronic medical conditions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74567/1/j.1525-1497.2005.0152.x.pd

Is Opium a Real Risk Factor for Esophageal Cancer or Just a Methodological Artifact? Hospital and Neighborhood Controls in Case-Control Studies

Background: Control selection is a major challenge in epidemiologic case-control studies. The aim of our study was to evaluate using hospital versus neighborhood control groups in studying risk factors of esophageal squamous cell carcinoma (ESCC). Methodology/Principal Findings: We compared the results of two different case-control studies of ESCC conducted in the same region by a single research group. Case definition and enrollment were the same in the two studies, but control selection differed. In the first study, we selected two age- and sex-matched controls from inpatient subjects in hospitals, while for the second we selected two age- and sex-matched controls from each subject's neighborhood of residence. We used the test of heterogeneity to compare the results of the two studies. We found no significant differences in exposure data for tobacco-related variables such as cigarette smoking, chewing Nass (a tobacco product) and hookah (water pipe) usage, but the frequency of opium usage was significantly different between hospital and neighborhood controls. Consequently, the inference drawn for the association between ESCC and tobacco use did not differ between the studies, but it did for opium use. In the study using neighborhood controls, opium use was associated with a significantly increased risk of ESCC (adjusted OR 1.77, 95% CI 1.17–2.68), while in the study using hospital controls, this was not the case (OR 1.09, 95% CI 0.63–1.87). Comparing the prevalence of opium consumption in the two control groups and a cohort enrolled from the same geographic area suggested that the neighborhood controls were more representative of the study base population for this exposure. Conclusions/Significance: Hospital and neighborhood controls did not lead us to the same conclusion for a major hypothesized risk factor for ESCC in this population. Our results show that control group selection is critical in drawing appropriate conclusions in observational studies

A new classification method using array Comparative Genome Hybridization data, based on the concept of Limited Jumping Emerging Patterns

<p>Abstract</p> <p>Background</p> <p>Classification using aCGH data is an important and insufficiently investigated problem in bioinformatics. In this paper we propose a new classification method of DNA copy number data based on the concept of limited Jumping Emerging Patterns. We present the comparison of our limJEPClassifier to SVM which is considered the most successful classifier in the case of high-throughput data.</p> <p>Results</p> <p>Our results revealed that the classification performance using limJEPClassifier is significantly higher than other methods. Furthermore, we show that application of the limited JEP's can significantly improve classification, when strongly unbalanced data are given.</p> <p>Conclusion</p> <p>Nowadays, aCGH has become a very important tool, used in research of cancer or genomic disorders. Therefore, improving classification of aCGH data can have a great impact on many medical issues such as the process of diagnosis and finding disease-related genes. The performed experiment shows that the application of Jumping Emerging Patterns can be effective in the classification of high-dimensional data, including these from aCGH experiments.</p