pinecone/core-2020-05-10-deduplication huggingface dataset:
Bayesian hierarchical modelling of continuous non-negative longitudinal data with a spike at zero : an application to a study of birds visiting gardens in winter

The development of methods for dealing with continuous data with a spike at zero has lagged behind those for overdispersed or zero-inflated count data. We consider longitudinal ecological data corresponding to an annual average of 26 weekly maximum counts of birds, and are hence effectively continuous, bounded below by zero but also with a discrete mass at zero. We develop a Bayesian hierarchical Tweedie regression model that can directly accommodate the excess number of zeros common to this type of data, whilst accounting for both spatial and temporal correlation. Implementation of the model is conducted in a Markov chain Monte Carlo (MCMC) framework, using reversible jump MCMC to explore uncertainty across both parameter and model spaces. This regression modelling framework is very flexible and removes the need to make strong assumptions about mean-variance relationships a priori. It can also directly account for the spike at zero, whilst being easily applicable to other types of data and other model formulations. Whilst a correlative study such as this cannot prove causation, our results suggest that an increase in an avian predator may have led to an overall decrease in the number of one of its prey species visiting garden feeding stations in the United Kingdom. This may reflect a change in behaviour of house sparrows to avoid feeding stations frequented by sparrowhawks, or a reduction in house sparrow population size as a result of sparrowhawk increase.Publisher PDFPeer reviewe
Analysis of interval-censored recurrent event processes subject to resolution

This is the peer reviewed version of the following article: Shen, H. and Cook, R. J. (2015), Analysis of interval-censored recurrent event processes subject to resolution. Biom. J., 57: 725–742. doi: 10.1002/bimj.201400162, which has been published in final form at http://dx.doi.org/10.1002/bimj.201400162.  This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.Interval-censored recurrent event data arise when the event of interest is not readily observed but the cumulative event count can be recorded at periodic assessment times. In some settings, chronic disease processes may resolve, and individuals will cease to be at risk of events at the time of disease resolution. We develop an expectation-maximization algorithm for fitting a dynamic mover-stayer model to interval-censored recurrent event data under a Markov model with a piecewise-constant baseline rate function given a latent process. The model is motivated by settings in which the event times and the resolution time of the disease process are unobserved. The likelihood and algorithm are shown to yield estimators with small empirical bias in simulation studies. Data are analyzed on the cumulative number of damaged joints in patients with psoriatic arthritis where individuals experience disease remission.Natural Sciences and Engineering Research Council of Canada (RGPIN 155849); Canadian Institutes for Health Research (FRN 13887); Canada Research Chair (Tier 1) – CIHR funded (950-226626).  HS: Grant from the Division of High Impact Clinical Trials of the Ontario Institute for Cancer Researc
Zero-inflated regression models for radiation-induced chromosome aberration data : a comparative study.

Within the field of cytogenetic biodosimetry, Poisson regression is the classical approach for modeling the number of chromosome aberrations as a function of radiation dose. However, it is common to find data that exhibit overdispersion. In practice, the assumption of equidispersion may be violated due to unobserved heterogeneity in the cell population, which will render the variance of observed aberration counts larger than their mean, and/or the frequency of zero counts greater than expected for the Poisson distribution. This phenomenon is observable for both full- and partial-body exposure, but more pronounced for the latter. In this work, different methodologies for analyzing cytogenetic chromosomal aberrations datasets are compared, with special focus on zero-inflated Poisson and zero-inflated negative binomial models. A score test for testing for zero inflation in Poisson regression models under the identity link is also developed
Detection of dependence patterns with delay

International audienceThe Unitary Events (UE) method is a popular and efficient method used this last decade to detect dependence patterns of  joint spike activity among simultaneously recorded neurons. The first introduced method is based on binned coincidence count (Grün, 1996) and can be applied on two or more simultaneously recorded neurons. Among the improvements of the methods, a transposition to the continuous framework has recently been proposed in (Muiño and Borgelt, 2014) and fully investigated in (Tuleau-Malot et al., 2014) for two neurons. The goal of the present paper is to extend this study to more than two neurons. The main result is the determination of the limit distribution of the coincidence count. This leads to the construction of an independence test between $L\geq 2$ neurons. Finally we propose a multiple test procedure via a Benjamini and Hochberg approach (Benjamini and Hochberg, 1995). All the theoretical results are illustrated by a simulation study, and compared to the UE method proposed in (Grün et al., 2002). Furthermore our method is applied on real data
Inference for binomial probability based on dependent Bernoulli random variables with applications to meta-analysis and group level studies

We study bias arising as a result of nonlinear transformations of random variables in random or mixed effects models and its effect on inference in group-level studies or in meta-analysis. The findings are illustrated on the example of overdispersed binomial distributions, where we demonstrate considerable biases arising from standard log-odds and arcsine transformations of the estimated probability inline image, both for single-group studies and in combining results from several groups or studies in meta-analysis. Our simulations confirm that these biases are linear in ρ, for small values of ρ, the intracluster correlation coefficient. These biases do not depend on the sample sizes or the number of studies K in a meta-analysis and result in abysmal coverage of the combined effect for large K. We also propose bias-correction for the arcsine transformation. Our simulations demonstrate that this bias-correction works well for small values of the intraclass correlation. The methods are applied to two examples of meta-analyses of prevalence
Meta-analysis of two studies in the presence of heterogeneity with
  applications in rare diseases

Random-effects meta-analyses are used to combine evidence of treatment
effects from multiple studies. Since treatment effects may vary across trials
due to differences in study characteristics, heterogeneity in treatment effects
between studies must be accounted for to achieve valid inference. The standard
model for random-effects meta-analysis assumes approximately normal effect
estimates and a normal random-effects model. However, standard methods based on
this model ignore the uncertainty in estimating the between-trial
heterogeneity. In the special setting of only two studies and in the presence
of heterogeneity we investigate here alternatives such as the
Hartung-Knapp-Sidik-Jonkman method (HKSJ), the modified Knapp-Hartung method
(mKH, a variation of the HKSJ method) and Bayesian random-effects meta-analyses
with priors covering plausible heterogeneity values. The properties of these
methods are assessed by applying them to five examples from various rare
diseases and by a simulation study. Whereas the standard method based on normal
quantiles has poor coverage, the HKSJ and mKH generally lead to very long, and
therefore inconclusive, confidence intervals. The Bayesian intervals on the
whole show satisfying properties and offer a reasonable compromise between
these two extremes.Comment: 12 pages, 4 figure
Prioritizing covariates in the planning of future studies in the
  meta-analytic framework

Science can be seen as a sequential process where each new study augments
evidence to the existing knowledge. To have the best prospects to make an
impact in this process, a new study should be designed optimally taking into
account the previous studies and other prior information. We propose a formal
approach for the covariate prioritization, i.e., the decision about the
covariates to be measured in a new study. The decision criteria can be based on
conditional power, change of the p-value, change in lower confidence limit,
Kullback-Leibler divergence, Bayes factors, Bayesian false discovery rate or
difference between prior and posterior expectation. The criteria can be also
used for decisions on the sample size. As an illustration, we consider
covariate prioritization based on genome-wide association studies for
C-reactive protein levels and make suggestions on the genes to be studied
further.
  keywords: design; evidence-based medicine; meta-analysis; power; scientific
metho
A flexible multivariate random effects proportional odds model with
  application to adverse effects during radiation therapy

Radiation therapy in patients with head and neck cancer has a toxic effect on
mucosa, the soft tissue in and around the mouth. Hence mucositis is a serious
common side effect and is a condition characterized by pain and inflammation of
the surface of the mucosa. Although the mucosa recovers during breaks of and
following the radiotherapy course the recovery will depend on the type of
tissue involved and on its location. We present a novel flexible multivariate
random effects proportional odds model which takes account of the longitudinal
course of oral mucositis at different mouth sites and of the radiation dosage
(in terms of cumulative dose). The model is an extension of the {\em
proportional odds model} which is used for ordinal response variables. Our
model includes the ordinal multivariate response of the mucositis score by
location, random intercepts for individuals and includes a non-linear function
of cumulative radiation dose. The model allows to test whether sensitivity
differs by mouth sites after having adjusted for site specific cumulative
radiation dose. The model also allows to check whether and how the (non-linear)
effect of site specific dose differs by site. We fit the model to longitudinal
patient data from a prospective observation and find that after adjusting for
cumulative dose, upper, lower lips and mouth floor are associated with the
lowest mucositis scores and hard and soft palate are associated with the
highest mucositis scores. This implies the possibility that tissues at
different mouth locations differ in their sensitivity to the toxic effect of
radiation. We also find that cumulative dose followed by mouth site are the
strongest predictors of mucositis, and the effects of age and gender are
negligible
A probabilistic network for the diagnosis of acute cardiopulmonary
  diseases

In this paper, the development of a probabilistic network for the diagnosis
of acute cardiopulmonary diseases is presented. This paper is a draft version
of the article published after peer review in 2018
(https://doi.org/10.1002/bimj.201600206). A panel of expert physicians
collaborated to specify the qualitative part, that is a directed acyclic graph
defining a factorization of the joint probability distribution of domain
variables. The quantitative part, that is the set of all conditional
probability distributions defined by each factor, was estimated in the Bayesian
paradigm: we applied a special formal representation, characterized by a low
number of parameters and a parameterization intelligible for physicians,
elicited the joint prior distribution of parameters from medical experts, and
updated it by conditioning on a dataset of hospital patient records using
Markov Chain Monte Carlo simulation. Refinement was cyclically performed until
the probabilistic network provided satisfactory Concordance Index values for a
selection of acute diseases and reasonable inference on six fictitious patient
cases. The probabilistic network can be employed to perform medical diagnosis
on a total of 63 diseases (38 acute and 25 chronic) on the basis of up to 167
patient findings.Comment: The DOI of the article published after peer review was added. A
  technical detail was added in Section 3.2, Formula 8 (as a consequence, the
  ID of all the subsequent formulas result augmented by 1 with respect to the
  previous version). The prior standard deviation of the Gamma distribution in
  Table 4 was fixed (in the previous version, the prior variance was indicated,
  instead
Using predictions from a joint model for longitudinal and survival data to inform the optimal time of intervention in an abdominal aortic aneurysm screening programme.

Joint models of longitudinal and survival data can be used to predict the risk of a future event occurring based on the evolution of an endogenous biomarker measured repeatedly over time. This has led naturally to the use of dynamic predictions that update each time a new longitudinal measurement is provided. In this paper, we show how such predictions can be utilised within a fuller decision modelling framework, in particular to allow planning of future interventions for patients under a 'watchful waiting' care pathway. Through the objective of maximising expected life-years, the predicted risks associated with not intervening (e.g. the occurrence of severe sequelae) are balanced against risks associated with the intervention (e.g. operative risks). Our example involves patients under surveillance in an abdominal aortic aneurysm screening programme where a joint longitudinal and survival model is used to associate longitudinal measurements of aortic diameter with the risk of aneurysm rupture. We illustrate how the decision to intervene, which is currently based on a diameter measurement greater than a certain threshold, could be made more personalised and dynamic through the application of a decision modelling approach.Peer-reviewedPublisher Versio
Cancer phase I trial design using drug combinations when a fraction of
  dose limiting toxicities is attributable to one or more agents

Drug combination trials are increasingly common nowadays in clinical
research. However, very few methods have been developed to consider toxicity
attributions in the dose escalation process. We are motivated by a trial in
which the clinician is able to identify certain toxicities that can be
attributed to one of the agents. We present a Bayesian adaptive design in which
toxicity attributions are modeled via Copula regression and the maximum
tolerated dose (MTD) curve is estimated as a function of model parameters. The
dose escalation algorithm uses cohorts of two patients, following the continual
reassessment method (CRM) scheme, where at each stage of the trial, we search
for the dose of one agent given the current dose of the other agent. The
performance of the design is studied by evaluating its operating
characteristics when the underlying model is either correctly specified or
misspecified. We show that this method can be extended to accommodate discrete
dose combinations
A shortcut for Hommel's procedure in linearithmic time

Hommel's and Hochberg's procedures for familywise error control are both
derived as shortcuts in a closed testing procedure with the Simes local test.
Hommel's shortcut is exact but takes quadratic time in the number of
hypotheses. Hochberg's shortcut takes only linearithmic time, but is
conservative. In this paper we present an exact shortcut in linearithmic time,
combining the strengths of both procedures. The novel shortcut also applies to
a robust variant of Hommel's procedure that does not require the assumption of
the Simes inequality.Comment: arXiv admin note: text overlap with arXiv:1611.0673
On the properties of GEE estimators in the presence of invariant covariates

In this paper it is shown that the use of non-singular block invariant matrices of covariates leads to `generalized estimating equations' estimators (GEE estimators; Liang, K.-Y.&Zeger, S. (1986). Biometrika, 73(1), 13-22) which are identical regardless of the `working' correlation matrix used. Moreover, they are efficient (McCullagh, P. (1983). The Annals of Statistics, 11(1), 59-67). If on the other hand only time invariant covariates are used the efficiency gain in choosing the `correct' vs. an `incorrect' correlation structure is shown to be negligible. The results of a simple simulation study suggest that although different GEE estimators are no more identical and are no more as efficient as an ML estimator, the differences are still negligible if both time and block invariant covariates are present.
Biased Estimation of Adjusted Odds Ratios From Incomplete Covariate Data Due to Violation of the Missing at Random Assumption

We investigate the possible bias due to an erroneous missing at random assumption if adjusted odds ratios are estimated from incomplete covariate data using the maximum likelihood principle. A relation between complete case estimates and maximum likelihood estimates allows us to identify situations where the bias vanishes. Numerical computations demonstrate that the bias is most serious if the degree of the violation of the missing at random assumption depends on the value of the outcome variable or of the observed covariate. Implications for the analysis of prospective and retrospective studies are given.
Shrinkage pre-test estimator of the intercept parameter for a regression model with mulativariate student-t errors

[Abstract]: In presence of an   uncertain prior information about the slope parameter, the estimation of the intercept of a simple regression
model with a multivariate Student-t error distribution is investigated.  The  unrestricted,  restricted and preliminary test
maximum likelihood estimators are defined. The expressions for the bias and the mean square error of the three estimators are provided and the  relative efficiencies are analysed. 
 A   maximin criterion is  established, and graphs and tables are constructed for different
number of degrees of freedom (D.F.)
as well as sample sizes. A criterion to select optimal significance levelis also discussed
Peroxyoxalate chemiluminescence enhanced by oligophenylenevinylene fluorophores in the presence of various surfactants

Article first published online: 15 JAN 2014The effect of several surfactants on peroxyoxalate chemiluminescence (PO-CL) using oligophenylenevinylene fluorophores was investigated. Among several oligophenylenevinylenes consisting of stilbene units, linearly conjugated ones, such as distyrylbenzene and distyrylstilbene, effectively enhanced PO-CL efficiency. Various effects of anionic, cationic, amphoteric and non-ionic surfactants on the CL efficiency of PO-CL were determined using three oxalates and the distyrylbenzene fluorophore. Anionic and non-ionic surfactants effectively enhanced CL efficiency, in contrast to the negative effect of cationic and amphoteric surfactants. Non-ionic surfactants were also effective in CL reactions of oxalates bearing dodecyl ester groups by the hydrophobic interaction between their alkyl chains. Considering these results, the surfactants not only increase the concentrations of water-insoluble interacting species in the hydrophobic micelle cores, but also control rapid degradation of the oxalates by alkaline hydrolysis. Copyright © 2014 John Wiley & Sons, Ltd
Effect of solvents on photophysical properties and quenching of 2-{[3-(1H-benzimidazole-2-yl) phenyl] carbonoimidoyl}phenol

The effect of solvents of varying polarity on the absorption and fluorescence emission of the Schiff base, 2-{[3-(1H-benzimidazole-2-yl) phenyl]carbonoimidoyl}phenol, was studied using Lippert-Mataga bulk polarity function, Reichardt's microscopic solvent polarity parameter and Kamlet's multiple linear regression approach. The spectral properties follow Reichardt's microscopic solvent polarity parameter better than Lippert-Mataga bulk polarity parameter, indicating the presence of both general solute-solvent interactions and specific interactions. Catalan's multiple linear regression approach indicates the major role of solvent polarizability/dipolarity influence compared with solvent acidity or basicity. The solvatochromic effect was utilized to calculate the dipole moments of ground and excited states of the Schiff base using different methods. Bathochromic shift in the emission spectrum and the increase in dipole moment in the excited state signifies the intramolecular charge transfer character in the emitting singlet state. Fluorescence quenching by aniline was also studied in 1,4-dioxane and n-butanol, and the results were analyzed using sphere of action static quenching and finite sink approximation models
Deposition of CdS, CdS/ZnSe and CdS/ZnSe/ZnS shells around CdSeTe alloyed core quantum dots : effects on optical properties

In this work, we have synthesized water-soluble L-cysteine-capped alloyed CdSeTe
core QDs and investigated the structural and optical properties of deposition of each
of CdS, CdS/ZnSe and CdS/ZnSe/ZnS shell layers. Photophysical results showed
that the overcoating of a CdS shell around the alloyed CdSeTe core (QY = 8.4%)
resulted in an effective confinement of the radiative exciton with an improved QY
value of 93.5%. Subsequent deposition of a ZnSe shell around the CdSeTe/CdS
surface decreased the QY to 24.7% but an increase in the QY value of up to 49.5%
was observed when a ZnS shell was overcoated around the CdSeTe/CdS/ZnSe
surface. QDs with shell layers showed improved stability relative to the core. Data
obtained from time-resolved fluorescence measurements provided useful insight
into the variations in the photophysical properties of the QDs upon the formation of
each shell layer. Our study suggests that the formation of the CdSeTe/CdS
core/shell QDs meets the requirements of quality QDs in terms of high PL QY and
stability, hence further deposition of additional shells are not necessary in improving
the optical properties of the core/shell QDs.University of Pretoria. This work is based on the research supported in part by the National Research Foundation of South Africa, Grant Number: 92584.http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1522-72432017-05-31hb2016Chemistr
Luminescent thermometer based on Eu3+/Tb3+-organic-functionalized mesoporous silica

In this work we investigate a mesoporous silica (MS) decorated with dipyridyl-pyridazine (dppz) ligands and further grafted with a mixture of Eu3+/Tb3+ ions (28.45%:71.55%), which was investigated as a potential thermometer in the 10-360K temperature range. The MS material was prepared employing a hetero Diels-Alder reaction: 3,6-di(2-pyridyl)-1,2,4,5-tetrazine was reacted with the double bonds of vinyl-silica (vSilica) followed by an oxidation procedure. We explore using the dppz-vSilica material to obtain visible emitting luminescent materials and for obtaining a luminescent thermometer when grafted with Eu3+/Tb3+ ions. For the dppz-vSilica@Eu,Tb material absolute sensitivityS(a) of 0.011K(-1) (210K) and relative sensitivityS(r) of 1.32 %K-1 (260K) were calculated showing good sensing capability of the material. Upon temperature change from 10K to 360K the emission color of the material changed gradually from yellow to red