pinecone/core-2020-05-10-deduplication huggingface dataset:
Gene expression profiling of Ewing sarcoma tumours reveals the prognostic importance of tumour–stromal interactions: a report from the Children's Oncology Group

Relapse of Ewing sarcoma (ES) can occur months or years after initial remission, and salvage therapy for relapsed disease is usually ineffective. Thus, there is great need to develop biomarkers that can predict which patients are at risk for relapse so that therapy and post‐therapy evaluation can be adjusted accordingly. For this study, we performed whole genome expression profiling on two independent cohorts of clinically annotated ES tumours in an effort to identify and validate prognostic gene signatures. ES specimens were obtained from the Children's Oncology Group and whole genome expression profiling performed using Affymetrix Human Exon 1.0 ST arrays. Lists of differentially expressed genes between survivors and non‐survivors were used to identify prognostic gene signatures. An independent cohort of tumours from the Euro‐Ewing cooperative group was similarly analysed as a validation cohort. Unsupervised clustering of gene expression data failed to segregate tumours based on outcome. Supervised analysis of survivors versus non‐survivors revealed a small number of differentially expressed genes and several statistically significant gene signatures. Gene‐specific enrichment analysis demonstrated that integrin and chemokine genes were associated with survival in tumours where stromal contamination was present. Tumours that did not harbour stromal contamination showed no association of any genes or pathways with clinical outcome. Our results reflect the challenges of performing RNA‐based assays on archived bone tumour specimens. In addition, they reveal a key role for tumour stroma in determining ES prognosis. Future biological and clinical investigations should focus on elucidating the contribution of tumour:micro‐environment interactions on ES progression and response to therapy
Likelihood-based inference for correlated diffusions

We address the problem of likelihood based inference for correlated diffusion processes using Markov chain Monte Carlo (MCMC) techniques. Such a task presents two interesting problems. First, the construction of the MCMC scheme should ensure that the correlation coefficients are updated subject to the positive definite constraints of the diffusion matrix. Second, a diffusion may only be observed at a finite set of points and the marginal likelihood for the parameters based on these observations is generally not available. We overcome the first issue by using the Cholesky factorisation on the diffusion matrix. To deal with the likelihood unavailability, we generalise the data augmentation framework of Roberts and Stramer (2001 Biometrika 88(3):603-621) to d-dimensional correlated diffusions including multivariate stochastic volatility models. Our methodology is illustrated through simulation based experiments and with daily EUR /USD, GBP/USD rates together with their implied volatilities.
Statistical inference optimized with respect to the observed sample for
  single or multiple comparisons

The normalized maximum likelihood (NML) is a recent penalized likelihood that
has properties that justify defining the amount of discrimination information
(DI) in the data supporting an alternative hypothesis over a null hypothesis as
the logarithm of an NML ratio, namely, the alternative hypothesis NML divided
by the null hypothesis NML. The resulting DI, like the Bayes factor but unlike
the p-value, measures the strength of evidence for an alternative hypothesis
over a null hypothesis such that the probability of misleading evidence
vanishes asymptotically under weak regularity conditions and such that evidence
can support a simple null hypothesis. Unlike the Bayes factor, the DI does not
require a prior distribution and is minimax optimal in a sense that does not
involve averaging over outcomes that did not occur. Replacing a (possibly
pseudo-) likelihood function with its weighted counterpart extends the scope of
the DI to models for which the unweighted NML is undefined. The likelihood
weights leverage side information, either in data associated with comparisons
other than the comparison at hand or in the parameter value of a simple null
hypothesis. Two case studies, one involving multiple populations and the other
involving multiple biological features, indicate that the DI is robust to the
type of side information used when that information is assigned the weight of a
single observation. Such robustness suggests that very little adjustment for
multiple comparisons is warranted if the sample size is at least moderate.Comment: Typo in equation (7) of v2 corrected in equation (6) of v3; clarity
  improve
Response-dependent two-phase sampling designs

This is the peer reviewed version of the following article: McIsaac, M. A. and Cook, R. J. (2014), Response-dependent two-phase sampling designs for biomarker studies. Can J Statistics, 42: 268–284. doi: 10.1002/cjs.11207, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/cjs.11207/references.  This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.Two-phase sampling designs are developed and investigated for use in the context of a rheumatology
study where interest lies in the association between a biomarker with an expensive assay and
disease progression. We derive optimal phase-II stratum-specific sampling probabilities for analyses
from parametric maximum likelihood (ML), mean score (MS), inverse probability weighted
(IPW), and augmented IPW (AIPW) estimating equations. The easy-to-implement optimally efficient
design for the MS estimator is found to be asymptotically optimal for the IPW and AIPW
estimators we consider, and is shown to result in efficiency gains over balanced and simple random
sampling even when analyses are likelihood-based. We further demonstrate the robustness
of this optimal design and show that it results in very efficient estimation even when the model or
parameters used in its derivation are misspecified.Natural Sciences and Engineering Research Council of Canada (RGPIN 155849); Canadian Institutes for Health Research (FRN 13887
Minimum profile Hellinger distance estimation for a semiparametric mixture model

In this paper, we propose a new effective estimator for a class of semiparametric mixture models where one component has known distribution with possibly unknown parameters while the other component density and the mixing proportion are unknown. Such semiparametric mixture models have been often used in multiple hypothesis testing and the sequential clustering algorithm. The proposed estimator is based on the minimum profile Hellinger distance (MPHD), and its theoretical properties are investigated. In addition, we use simulation studies to illustrate the finite sample performance of the MPHD estimator and compare it with some other existing approaches. The empirical studies demonstrate that the new method outperforms existing estimators when data are generated under contamination and works comparably to existing estimators when data are not contaminated. Applications to two real data sets are also provided to illustrate the effectiveness of the new methodology
A Mixture of Generalized Hyperbolic Distributions

We introduce a mixture of generalized hyperbolic distributions as an
alternative to the ubiquitous mixture of Gaussian distributions as well as
their near relatives of which the mixture of multivariate t and skew-t
distributions are predominant. The mathematical development of our mixture of
generalized hyperbolic distributions model relies on its relationship with the
generalized inverse Gaussian distribution. The latter is reviewed before our
mixture models are presented along with details of the aforesaid reliance.
Parameter estimation is outlined within the expectation-maximization framework
before the clustering performance of our mixture models is illustrated via
applications on simulated and real data. In particular, the ability of our
models to recover parameters for data from underlying Gaussian and skew-t
distributions is demonstrated. Finally, the role of Generalized hyperbolic
mixtures within the wider model-based clustering, classification, and density
estimation literature is discussed
Analysis of Incomplete Compound Variables

This is the peer reviewed version of the following article: Zhao, J., Cook, R. J. and Wu, C. (2015), Multiple imputation for the analysis of incomplete compound variables. Can J Statistics, 43: 240–264. doi: 10.1002/cjs.11249, which has been published in final form at http://dx.doi.org/10.1002/cjs.11249.  This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving'In many settings interest lies in modelling a compound variable defined as a function of two or more component variables. When one or more of the components are missing, the compound variable is not observed and a strategy for handling incomplete data is required. Analyses based on individuals with complete data are inefficient and yield potentially inconsistent estimators.We develop a multiple imputation strategy in this setting with an auxiliary model for imputing the compound variable directly, and one based on a multivariate imputation model for the component variables. Asymptotic properties of the imputation-based estimators are presented for the case in which the imputation model is correctly specified, and a shrinkage estimator is proposed to reduce the bias arising from misspecification of the imputation model. Finite sample properties of the various estimators are examined through simulations. An application to data from the Cana- dian Youth Smoking Survey involving a study of body mass index illustrates the approach.Natural Sciences and Engineering Research Council of Canada (RJC RGPIN 155849, CW RGPIN 05613); Canadian Institutes for Health Research (RJC FRN 13887
Using bootstrap for statistical inference on random graphs

In this paper, we propose new nonparametric approach to network inference
that may be viewed as a fusion of block sampling procedures for temporally and
spatially dependent processes with the classical network methodology. We
develop estimation and uncertainty quantification procedures for network mean
degree using a "patchwork" sample and nonparametric bootstrap, under the
assumption of unknown degree distribution. We investigate asymptotic properties
of the proposed patchwork bootstrap procedure and present cross-validation
methodology for selecting an optimal patch size. We validate the new patchwork
bootstrap on simulated networks with short and long tailed mean degree
distributions, and revisit the Erdos collaboration data to illustrate the
proposed methodology.Comment: The paper has been withdrawn by the authors: a general revision of
  methodology is neede
Upper bounds on the minimum coverage probability of model averaged tail
  area confidence intervals in regression

Frequentist model averaging has been proposed as a method for incorporating
"model uncertainty" into confidence interval construction. Such proposals have
been of particular interest in the environmental and ecological statistics
communities. A promising method of this type is the model averaged tail area
(MATA) confidence interval put forward by Turek and Fletcher, 2012. The
performance of this interval depends greatly on the data-based model weights on
which it is based. A computationally convenient formula for the coverage
probability of this interval is provided by Kabaila, Welsh and Abeysekera,
2016, in the simple scenario of two nested linear regression models. We
consider the more complicated scenario that there are many (32,768 in the
example considered) linear regression models obtained as follows. For each of a
specified set of components of the regression parameter vector, we either set
the component to zero or let it vary freely. We provide an easily-computed
upper bound on the minimum coverage probability of the MATA confidence
interval. This upper bound provides evidence against the use of a model weight
based on the Bayesian Information Criterion (BIC)
Interval estimation via tail functions

In this paper we describe a new methodology for constructing confidence intervals. The idea is to specify the tail cutoff areas in terms of a function of the target parameter rather than as constants. This function, called the tail function, can be engineered so as to provide shorter confidence intervals when prior information is available. It can also be used to improve the coverage properties of approximate confidence intervals. We illustrate the methodology by applying it to inference on the normal mean and binomial proportion, and develop measures of the resulting improvements. Guidelines for choosing the optimal tail function in any situation are provided, and the relationship with Bayesian inference is discussed
Clinical diagnosis of posttraumatic stress disorder after myocardial infarction

BACKGROUND: Clinician-rated large-scale studies estimating the prevalence of posttraumatic stress disorder (PTSD) related to myocardial infarction (MI) and identifying predictors of clinical PTSD are currently lacking. HYPOTHESES: We hypothesized that PTSD is prevalent in post-MI patients and that the subjective experience of the MI determines PTSD status. METHODS: We approached 951 post-MI patients with a questionnaire screening for PTSD symptoms related to their MI. Those responding and meeting a cutoff of PTSD symptom levels were invited to participate in a structured clinical interview to diagnose PTSD following Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria. Fear of dying, feelings of helplessness, and severity of pain perceived during the MI were also assessed by visual analog scales. RESULTS: The screening questionnaire was completed by 394 patients, whereby 77 met the cutoff for the interview (8 patients declined the interview). Forty of 394 patients (10.2%) had clinical PTSD (subsyndromal and syndromal forms combined). Younger age (OR 0.95, 95% CI 0.91-0.99), greater fear of dying (OR 2.77, 95% CI 1.28-5.97), and more intense feelings of helplessness (OR 2.97, 95% CI 1.42-6.21) were independent predictors of PTSD status. Perceived pain intensity during MI, sex, type of index MI, left ventricular ejection fraction, number of coronary occlusions, and highest level of total creatinine kinase were not significant predictors. CONCLUSIONS: Clinical PTSD is prevalent in post-MI patients. Demographic and particularly psychological variables related to the subjective experience of the event were stronger predictors of PTSD status than were objective measures of MI severity
Isolated cleft in the posterior mitral valve leaflet: a congenital form of mitral regurgitation

Background:\ud
Isolated congenital cleft of the posterior leaflet of the mitral valve is a rare cause of mitral regurgitation (MR). This study describes the clinical, echocardiographic, and intraoperative findings as well as treatment options.\ud
\ud
Methods:\ud
Adults with an isolated cleft of the posterior mitral valve leaflet diagnosed by transthoracic echocardiography were evaluated with respect to clinical, echocardiographic, preoperative and intraoperative findings, and different surgical strategies.\ud
\ud
Results:\ud
The prevalence of isolated cleft of the posterior mitral valve leaflet in all patients examined was 0.11% (n = 22 out of 19 320 evaluated echocardiograms); male gender was predominant (73%). Dyspnea on exertion was present in almost all patients with at least moderate regurgitation. The predominant localization of the cleft was within segment P2 (59%), followed by a cleft between P1/P2 (18%). An isolated cleft in segment P3 or segment P1 occurred twice in each segment (n = 2; 9%) and between P2/P3 once (n = 1; 5%). Regurgitation was severe in 50% (n = 11), moderate in 9% (n = 2), mild in 27% (n = 6), and only trivial in 14% (n = 3) of the patients. Surgical treatment involved reconstruction with ring annuloplasty in 45% (n = 10) and replacement in 4.5% (n = 1). A total of 11 patients (50%) with mostly mild or trivial mitral regurgitation were treated medically only.\ud
\ud
Conclusion:\ud
Two-dimensional high-resolution cross-sectional echocardiography allows the distinct diagnosis of a clefted posterior leaflet, whereas clinical presentation, electrocardiogram, chest x-ray, and angiography are failing to identify the correct etiology of MR in patients with isolated posterior leaflet cleft mitral valve (IPLCMV). Patients with moderate to severe MR were treated surgically with excellent outcome. Copyright © 2009 Wiley Periodicals, Inc
Comparison between zofenopril and ramipril in combination with acetylsalicylic acid in patients with left ventricular systolic dysfunction after acute myocardial infarction: results of a randomized, double-blind, parallel-group, multicenter, European study (SMILE-4)

BACKGROUND:\ud
\ud
Angiotensin-converting enzyme inhibitors (ACEIs) are largely employed for treating patients with left ventricular dysfunction (LVD), but their efficacy may be negatively affected by concomitant administration of acetylsalicylic acid (ASA), with some difference among the different compounds.\ud
HYPOTHESIS:\ud
\ud
The interaction between ASA and the two ACEIs zofenopril and ramipril may result in a different impact on survival of cardiac patients, due to differences in the pharmacological properties of the two ACEIs.\ud
METHODS:\ud
\ud
This phase IIIb, randomized, double-blind, parallel-group, multicenter, European study compared the safety and efficacy of zofenopril (60 mg/day) and ramipril (10 mg/day) plus ASA (100 mg/day), in 771 patients with LVD (clinical signs of heart failure or a left ventricular ejection fraction <45%) following acute myocardial infarction (AMI). The primary study end point was 1-year combined occurrence of death or hospitalization for cardiovascular causes.\ud
RESULTS:\ud
\ud
In the intention-to-treat population, the primary outcome was significantly reduced by zofenopril (n = 365) vs ramipril (n = 351) (odds ratio [OR]: 0.70, and 95% confidence interval [CI]: 0.51-0.96; P = 0.028) as a result of a decrease in cardiovascular hospitalization (OR: 0.64,95% CI: 0.46-0.88; P = 0.006). Mortality rate was not significantly different between the 2 treatments (OR: 1.51, 95% CI: 0.70-3.27; P = 0.293). Blood pressure values did not significantly change during the 1-year follow-up. N-terminal pro-brain natriuretic peptide levels were progressively reduced during the study, with no statistically significant between-treatment differences. Proportion of patients with deterioration of renal function during the study was similar between the 2 groups. Drug safety profile was comparable between treatments.\ud
CONCLUSIONS:\ud
\ud
In patients with LVD following AMI, the efficacy of zofenopril associated with ASA was superior to that of ramipril plus ASA, indicating some important clinical implications for the future use of ACEIs in patients with LVD or overt heart failure
Reliable Exclusion of Acute Coronary Syndrome Among Hospitalized Patients With Elevated Troponin

Background  Elevated cardiac troponin I ( cTnI ) occurs in acute coronary syndrome ( ACS ) as well as various scenarios not associated with  ACS .    Hypothesis  Simple clinical criteria can reliably exclude ACS among hospitalized patients with elevated cTnI.    Methods  Records for patients hospitalized from January to April 2011 with elevated  cTnI  (>0.29 ng/ dL ) and an available echocardiogram were retrospectively reviewed. Patients with  ST ‐segment elevation myocardial infarction were excluded. Based on available clinical data, patients were classified as having  ACS  or elevation of  cTnI  unrelated to  ACS  (non‐ ACS ). Median follow‐up was 365 days.    Results  Of 265 records meeting inclusion criteria, 82 (31%) had  ACS  and 183 (69%) had non‐ ACS . In multivariable analysis, odds ratios for non‐ ACS  were 7.6 (95% confidence interval [ CI ]: 3.8‐15.3) for peak  cTnI  <2 ng/ dL , 6.3 (95%  CI : 3.1‐13.0) for absent wall‐motion abnormality, and 4.4 (95%  CI : 2.2‐8.6) for no prior coronary artery disease history. The area under the receiver operating curve for a model using these 3 variables was 0.86, with a 98% negative predictive value for excluding  ACS . Patients who met these 3 criteria had no  ACS ‐related deaths over 1‐year follow‐up.    Conclusions  Hospitalized patients with peak Tn level <2 ng/ dL , no prior history of coronary artery disease, and no new echocardiographic wall‐motion abnormality appear to have a very low likelihood of  ACS . Prospective validation of these results is needed to determine whether additional diagnostic testing could be safely avoided in hospitalized patients meeting these simple clinical criteria
Hospitalization Costs for Acute Myocardial Infarction Patients Treated With Percutaneous Coronary Intervention in the United States Are Substantially Higher Than Medicare Payments

BackgroundAcute coronary syndromes account for half of all deaths secondary to cardiovascular disease and represent a significant economic burden in the United States. Therefore, assessing hospitalization costs relative to Medicare reimbursement for these patients is important in understanding the impact of these patients on hospitals. We hypothesized that hospitalization costs for acute myocardial infarction patients treated with percutaneous coronary intervention (PCI) were higher than their associated Medicare payments.MethodsUsing the Nationwide Inpatient Sample, we evaluated hospitalization costs for patients treated with PCI from 2001 through 2009 by multiplying hospital charges by the group average cost‐to‐charge ratio for each patient's hospitalization. Primary end points examined were total hospital costs and trends over time, which were correlated with clinical outcomes and insurance payments. Costs were inflation adjusted with 2009 as the reference year.ResultsMedian hospitalization costs of PCI increased from $15 889 (interquartile range [IQR] = $12 057–$21 204) in 2001 to $19 349 (IQR = $14 660–$26 282) in 2009. From 2004 to 2009, inflation‐adjusted costs for PCI decreased at a rate of 0.3% per year. In 2009, a total of 265,531 patients received PCI for acute myocardial infarction. Of these, 143 654 were <65 years old, and 121 876 were ≥65 years old. Average 2009 Medicare payments ranged from $9303 to $17 500 depending on the Medicare Severity‐Diagnosis Related Groups (MS‐DRG) billed, leaving hospitals at a loss of anywhere from $4493 to $7940 per patient when comparing costs and reimbursements across all included MS‐DRG codes.ConclusionsHospitalization costs for patients treated with PCI have been stabilizing over the last few years; however, there still remains a significant disparity between Medicare reimbursements and hospitalization costs, which has potential implications on patient outcomes, quality of care, and hospital sustainability