Semi- and non-parametric competing risks analysis of right censored data and failure cause missing completely at random

We consider a nonparametric and a semiparametric (in presence of covariates) additive hazards rate competing risks model with censoring and failure cause possibly missing completely at random. Estimators of the unknown parameters are proposed in order to satisfy some optimality criteria. Large ample results are given for all the estimators. Our nonparametric method is applied to a real data set and the behavior of the semiparametric estimators are analyzed through a Monte Carlo study

Semiparametric inference of competing risks data with additive hazards and missing cause of failure under MCAR or MAR assumptions

International audienceIn this paper, we consider a semiparametric model for lifetime data with competing risks and missing causes of death. We assume that an additive hazards model holds for each cause-specific hazard rate function and that a random right censoring occurs. Our goal is to estimate the regression parameters as well as the functional parameters such as the baseline and cause-specific cumulative hazard rate functions / cumulative incidence functions. We first introduce preliminary estimators of the unknown (Euclidean and functional) parameters when cause of death indicators are missing completely at random (MCAR). These estimators are obtained using the observations with known cause of failure. The advantage of considering the MCAR model is that the information given by the observed lifetimes with unknown failure cause can be used to improve the preliminary estimates in order to attain an asymptotic optimality criterion. This is the main purpose of our work. However, since it is often more realistic to consider a missing at random (MAR) mechanism, we also derive estimators of the regression and functional parameters under the MAR model. We study the large sample properties of our estimators through martingales and empirical process techniques. We also provide a simulation study to compare the behavior of our three types of estimators under the different mechanisms of missingness. It is shown that our improved estimators under MCAR assumption are quite robust if only the MAR assumption holds. Finally, three illustrations on real datasets are also given

Empirical likelihood confidence bands for mean functions of recurrent events with competing risks and a terminal event

In this paper, we study recurrent events with competing risks in the presence of a terminal event and a censorship. We focus our attention on the mean functions which give the mean number of events of a specific type that have occured up to a time $t$. Using empirical likelihood ratio techniques, we are able to build confidence bands for these functions. We have a data set of nosocomial infections in an intensive care unit of a french hospital. For each patient, we know if and when he caught an infection, what infection it was (septicemia, urinary tract infection...), if and when he died and when he left the hospital. Our model fits this context and will be used to build confidence bands for one type of nosocomial infection and even a confidence tube for two types

On the parametric maximum likelihood estimator for independent but non-identically distributed observations with application to truncated data

International audienceWe investigate the parametric maximum likelihood estimator for truncated data when the truncation value is different according to the observed individual or item. We extend Lehmann's proof (1983) of the asymptotic properties of the parametric maximum likelihood estimator in the case of independent non-identically distributed observations. Two cases are considered: either the number of distinct probability distribution functions that can be observed in the population from which the sample comes from is finite or this number is infinite. Sufficient conditions for consistency and asymptotic normality are provided for both cases

Bandes de confiance par vraisemblance empirique ( -méthode fonctionnelle et applications aux processus des événements récurrents)

Disposant d un jeu de données sur des infections nosocomiales, nous utilisons des techniques de vraisemblance empirique pour construire des bandes de confiance pour certaines quantité d intérêt. Cette étude nous amène à renforcer les outils déjà existants afin qu ils s adaptent à notre cadre. Nous présentons dans une première partie les outils mathématiques issus de la littérature que nous utilisons dans ce travail de thèse. Nous les appliquons ensuite à diverses situations et donnons de nouvelles démonstrations lorsque cela est nécessaire. Nous conduisons aussi des simulations et obtenons des résultats concrets concernant notre jeu de données. Enfin, nous détaillons les algorithmes utilisés.The starting point of this thesis is a data set of nosocomial infectionsin an intensive care unit of a French hostipal. We focused our attention onbuilding confidence bands for some parameters of interest using empiricallikelihood techniques. In order to do so, we had to adapt and develop somealready existing methods so that they fit our setup.We begin by giving a state of the art of the different theories we use.We then apply them to different setups and demonstrate new results whenneeded. Finally, we conduct simulations and describe our algorithms.BESANCON-Bib. Electronique (250560099) / SudocSudocFranceF

Local Symmetries and Order-Disorder Transitions in Small Macroscopic Wigner Islands

The influence of local order on the disordering scenario of small Wigner islands is discussed. A first disordering step is put in evidence by the time correlation functions and is linked to individual excitations resulting in configuration transitions, which are very sensitive to the local symmetries. This is followed by two other transitions, corresponding to orthoradial and radial diffusion, for which both individual and collective excitations play a significant role. Finally, we show that, contrary to large systems, the focus that is commonly made on collective excitations for such small systems through the Lindemann criterion has to be made carefully in order to clearly identify the relative contributions in the whole disordering process.Comment: 14 pages, 10 figure

Ultrastructural localization of rRNA shows defective nuclear export of preribosomes in mutants of the Nup82p complex

To study the nuclear export of preribosomes, ribosomal RNAs were detected by in situ hybridization using fluorescence and EM, in the yeast Saccharomyces cerevisiae. In wild-type cells, semiquantitative analysis shows that the distributions of pre-40S and pre-60S particles in the nucleolus and the nucleoplasm are distinct, indicating uncoordinated transport of the two subunits within the nucleus. In cells defective for the activity of the GTPase Gsp1p/Ran, ribosomal precursors accumulate in the whole nucleus. This phenotype is reproduced with pre-60S particles in cells defective in pre-rRNA processing, whereas pre-40S particles only accumulate in the nucleolus, suggesting a tight control of the exit of the small subunit from the nucleolus. Examination of nucleoporin mutants reveals that preribosome nuclear export requires the Nup82p–Nup159p–Nsp1p complex. In contrast, mutations in the nucleoporins forming the Nup84p complex yield very mild or no nuclear accumulation of preribosome. Interestingly, domains of Nup159p required for mRNP trafficking are not necessary for preribosome export. Furthermore, the RNA helicase Dbp5p and the protein Gle1p, which interact with Nup159p and are involved in mRNP trafficking, are dispensable for ribosomal transport. Thus, the Nup82p–Nup159p–Nsp1p nucleoporin complex is part of the nuclear export pathways of preribosomes and mRNPs, but with distinct functions in these two processes

Estimation in a Competing Risks Proportional Hazards Model Under Length-biased Sampling With Censoring

International audienceWhat population does the sample represent? The answer to this question is of crucial importance when estimating a survivor function in duration studies. As is well-known, in a stationary population, survival data obtained from a cross-sectional sample taken from the population at time $t_0$ represents not the target density $f(t)$ but its length-biased version proportional to $tf(t)$, for $t>0$. The problem of estimating survivor function from such length-biased samples becomes more complex, and interesting, in presence of competing risks and censoring. This paper lays out a sampling scheme related to a mixed Poisson process and develops nonparametric estimators of the survivor function of the target population assuming that the two independent competing risks have proportional hazards. Two cases are considered: with and without independent consoring before length biased sampling. In each case, the weak convergence of the process generated by the proposed estimator is proved. A well-known study of the duration in power for political leaders is used to illustrate our results. Finally, a simulation study is carried out in order to assess the finite sample behaviour of our estimators