Laplacian P-splines for Bayesian inference in the mixture cure model

The mixture cure model for analyzing survival data is characterized by the assumption that the population under study is divided into a group of subjects who will experience the event of interest over some finite time horizon and another group of cured subjects who will never experience the event irrespective of the duration of follow-up. When using the Bayesian paradigm for inference in survival models with a cure fraction, it is common practice to rely on Markov chain Monte Carlo (MCMC) methods to sample from posterior distributions. Although computationally feasible, the iterative nature of MCMC often implies long sampling times to explore the target space with chains that may suffer from slow convergence and poor mixing. Furthermore, extra efforts have to be invested in diagnostic checks to monitor the reliability of the generated posterior samples. An alternative strategy for fast and flexible sampling-free Bayesian inference in the mixture cure model is suggested in this paper by combining Laplace approximations and penalized B-splines. A logistic regression model is assumed for the cure proportion and a Cox proportional hazards model with a P-spline approximated baseline hazard is used to specify the conditional survival function of susceptible subjects. Laplace approximations to the conditional latent vector are based on analytical formulas for the gradient and Hessian of the log-likelihood, resulting in a substantial speed-up in approximating posterior distributions. Results show that LPSMC is an appealing alternative to classic MCMC for approximate Bayesian inference in standard mixture cure models.Comment: 34 pages, 6 figures, 5 table

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

EpiLPS: A fast and flexible Bayesian tool for estimation of the time-varying reproduction number.

In infectious disease epidemiology, the instantaneous reproduction number [Formula: see text] is a time-varying parameter defined as the average number of secondary infections generated by an infected individual at time t. It is therefore a crucial epidemiological statistic that assists public health decision makers in the management of an epidemic. We present a new Bayesian tool (EpiLPS) for robust estimation of the time-varying reproduction number. The proposed methodology smooths the epidemic curve and allows to obtain (approximate) point estimates and credible intervals of [Formula: see text] by employing the renewal equation, using Bayesian P-splines coupled with Laplace approximations of the conditional posterior of the spline vector. Two alternative approaches for inference are presented: (1) an approach based on a maximum a posteriori argument for the model hyperparameters, delivering estimates of [Formula: see text] in only a few seconds; and (2) an approach based on a Markov chain Monte Carlo (MCMC) scheme with underlying Langevin dynamics for efficient sampling of the posterior target distribution. Case counts per unit of time are assumed to follow a negative binomial distribution to account for potential overdispersion in the data that would not be captured by a classic Poisson model. Furthermore, after smoothing the epidemic curve, a "plug-in'' estimate of the reproduction number can be obtained from the renewal equation yielding a closed form expression of [Formula: see text] as a function of the spline parameters. The approach is extremely fast and free of arbitrary smoothing assumptions. EpiLPS is applied on data of SARS-CoV-1 in Hong-Kong (2003), influenza A H1N1 (2009) in the USA and on the SARS-CoV-2 pandemic (2020-2021) for Belgium, Portugal, Denmark and France

A dynamic spatio-temporal model to investigate the effect of cattle movements on the spread of Bluetongue BTV-8 in Belgium

When Bluetongue Virus Serotype 8 (BTV-8) was first detected in Northern Europe in 2006, several guidelines were immediately put into place with the goal to protect farms and stop the spreading of the disease. This however did not prevent further rapid spread of BTV-8 across Northern Europe. Using information on the 2006 Bluetongue outbreak in cattle farms in Belgium, a spatio-temporal transmission model was formulated. The model quantifies the local transmission of the disease between farms within a municipality, the short-distance transmission between farms across neighbouring municipalities and the transmission as a result of cattle movement. Different municipality-level covariates such as farm density, land composition variables, temperature and precipitation, were assessed as possibly influencing each component of the transmission process. Results showed a significant influence of the different covariates in each model component, particularly the significant effect of temperature and precipitation values in the number of infected farms. The model which allowed us to predict the dynamic spreading of BTV for different movement restriction scenarios, also affirmed the significant impact of cattle movement in the 2006 BTV outbreak pattern. Simulation results further showed the importance of considering the size of restriction zones in the formulation of guidelines for animal infectious diseases

Bizneset e vogla familjare garant i zhvillimit ekonomik e social

Integrimi global ekonomik si proces gjithnjë e më shumë po ka ndikim edhe në politikat nacionale të zhvillimit, ndaj si i tillë është jashtëzakonisht vështir që të kontrollohet me aplikimin e metodave dhe instrumenteve tradicionale të zhvillimit ekonomik. Kjo mbase vështirëson që në vitet në vijim, të planifikohen qëllime të caktuara ekonomike në nivel shteti. Të gjitha ndërmarrjet në botë, të mëdha apo të vogla, familjare dhe jo familjare, publike apo jo publike, të zhvilluara dhe të pa zhvilluara, po përballen me një sërë pyetjesh të cilat kanë të bëjnë me ardhmërinë dhe perspektivën e tyre, respektivisht vije në pikëpyetje ekzistenca e tyre në situata të paparapara turbulente për rrethinën. Çfarë do të jenë nevojat e tregut, çfarë synon konkurrenca, a po lëkunden qëndrimet e blerësve, kah pretendon përparimi i teknikës dhe teknologjisë, çfarë me zhvillimin e prodhimeve të reja, a ka mundësi të sigurimit të resurseve të nevojshme, kah udhëzon globalizimi i biznesit,cilat nevoja duhet përdorur për arritjen e efikasitetit, efektiviteti dhe inovacionet, etj… të gjitha këto pyetje u adresohen çdo ditë ndërmarrjeve – pa marrë parasysh nëse ato janë në pronësi të familjes apo nuk janë të tilla. Ajo çka ndërmarrjet familjare i bënë të veçanta dhe që i dallon nga ndërmarrjet e tjera që nuk janë në pronësi apo kontroll të familjes, paraqet diversitetin e interesit familjar dhe interesit të ndërmarrjes në sistemin e unik të biznesit familjar. Pronarët e kompanive familjare në të gjithë botën ndajnë filozofinë dhe vlerat e njëjta, ata mendojnë në perspektivë, kapitali i tyre është durimi dhe zelli, interesohen për bashkësinë ku veprojnë dhe punojnë ashtu që kompaninë në gjendjen më të mirë të mundshme t’ua lënë trashëgimtarëve të tyre. Modeli i biznesit të kompanive familjare është tejet inovativ dhe largpamës, kërkon në realizim më të mirë, krijon më shumë vlera, ruan vendet e punës, përkundër rënieve dhe recesioneve ciklike. Zhvillimi i ndërmarrjeve të vogla dhe të mesme familjare, paraqet rrugën më të shpejtë dhe më të lirë të zhvillimit të ekonomive kombëtare. NVM janë shtyllat kurrizore të zhvillimit ekonomik të shteteve në tranzicion dhe përkrah rolit të madh që kanë në punësim ato janë mjaft domethënëse për demokratizimin e shoqërisë

Mapping maternal mortality rate via spatial zero-inflated models for count data : a case study of facility-based maternal deaths from Mozambique

Maternal mortality remains very high in Mozambique, with estimates from 2015 showing a maternal mortality ratio of 489 deaths per 100,000 live births, even though the rates tend to decrease since 1990. Pregnancy related hemorrhage, gestational hypertension and diseases such as malaria and HIV/AIDS are amongst the leading causes of maternal death in Mozambique, and a significant number of these deaths occur within health facilities. Often, the analysis of data on maternal mortality involves the use of counts of maternal deaths as outcome variable. Previously we showed that a class of hierarchical zero-inflated models were very successful in dealing with overdispersion and clustered counts when analyzing data on maternal deaths and related risk factors within health facilities in Mozambique. This paper aims at providing additional insights over previous analyses and presents an extension of such models to account for spatial variation in a disease mapping framework of facility-based maternal mortality in Mozambique

Comparison of commonly used methods in random effects meta-analysis:Application to preclinical data in drug discovery research

Background Meta-analysis of preclinical data is used to evaluate the consistency of findings and to inform the design and conduct of future studies. Unlike clinical meta-analysis, preclinical data often involve many heterogeneous studies reporting outcomes from a small number of animals. Here, we review the methodological challenges in preclinical meta-analysis in estimating and explaining heterogeneity in treatment effects.Methods Assuming aggregate-level data, we focus on two topics: (1) estimation of heterogeneity using commonly used methods in preclinical meta-analysis: method of moments (DerSimonian and Laird; DL), maximum likelihood (restricted maximum likelihood; REML) and Bayesian approach; (2) comparison of univariate versus multivariable meta-regression for adjusting estimated treatment effects for heterogeneity. Using data from a systematic review on the efficacy of interleukin-1 receptor antagonist in animals with stroke, we compare these methods, and explore the impact of multiple covariates on the treatment effects.Results We observed that the three methods for estimating heterogeneity yielded similar estimates for the overall effect, but different estimates for between-study variability. The proportion of heterogeneity explained by a covariate is estimated larger using REML and the Bayesian method as compared with DL. Multivariable meta-regression explains more heterogeneity than univariate meta-regression.Conclusions Our findings highlight the importance of careful selection of the estimation method and the use of multivariable meta-regression to explain heterogeneity. There was no difference between REML and the Bayesian method and both methods are recommended over DL. Multiple meta-regression is worthwhile to explain heterogeneity by more than one variable, reducing more variability than any univariate models and increasing the explained proportion of heterogeneity

Cross nearest-spike interval based method to measure synchrony dynamics

[Abstract] A new synchrony index for neural activity is de ned in this paper. The method is able to measure synchrony dynamics in low ring rate scenarios. It is based on the computation of the time intervals between nearest spikes of two given spike trains. Generalized additive models are proposed for the synchrony pro les obtained by this method. Two hypothesis tests are proposed to assess for di erences in the level of synchronization in a real data example. Bootstrap methods are used to calibrate the distribution of the tests. Also, the expected synchrony due to chance is computed analytically and by simulation to assess for actual synchronization.Ministerio de Economía e Innovación; MTM2008-00166Ministerio de Economía e Innovación; MTM2011-22392Ministerio de Economía e Innovación; BES-2009-017772Galicia. Consellería de Economía e Industria; INCITE09 137 272 P

Inferring age-specific differences in susceptibility to and infectiousness upon SARS-CoV-2 infection based on Belgian social contact data

Several important aspects related to SARS-CoV-2 transmission are not well known due to a lack of appropriate data. However, mathematical and computational tools can be used to extract part of this information from the available data, like some hidden age-related characteristics. In this paper, we present a method to investigate age-specific differences in transmission parameters related to susceptibility to and infectiousness upon contracting SARS-CoV-2 infection. More specifically, we use panel-based social contact data from diary-based surveys conducted in Belgium combined with the next generation principle to infer the relative incidence and we compare this to real-life incidence data. Comparing these two allows for the estimation of age-specific transmission parameters. Our analysis implies the susceptibility in children to be around half of the susceptibility in adults, and even lower for very young children (preschooler). However, the probability of adults and the elderly to contract the infection is decreasing throughout the vaccination campaign, thereby modifying the picture over time.Comment: Revised version, 17 pages, supplementary material 15 page