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A system composed from a set of independent and identical parallel units is considered and its resistance (survival) against an increasing load is modelled by a counting process model, in the framework of statistical survival analysis. The objective is to estimate the (nonparametrized) hazard function of the distribution of loads breaking the units of the system (i. e. their breaking strengths), to derive the large sample properties of the estimator, and to propose a goodness-of-fit test. We also...
Deviations from assigned treatment occur often in clinical trials. In such a setting, the traditional intent-to-treat analysis does not measure biological efficacy but rather programmatic effectiveness. For all-or-nothing compliance situation, Loeys and Goetghebeur (2003) recently proposed a Structural Proportional Hazards method. It allows for casual estimation in the complier subpopulation provided the exclusion restriction holds: randomization per se has no effect unless exposure has changed....
We propose in this work an original estimator of the conditional intensity of a marker-dependent counting process, that is, a counting process with covariates. We use model selection methods and provide a nonasymptotic bound for the risk of our estimator on a compact set. We show that our estimator reaches automatically a convergence rate over a functional class with a given (unknown) anisotropic regularity. Then, we prove a lower bound which establishes that this rate is optimal. Lastly, we provide...
In a case-cohort design, covariate histories are measured only on cases and a subcohort that is randomly selected from the entire cohort. This design has been widely used in large epidemiologic studies, especially when the exposures of interest are expensive to assemble for all the subjects. In this paper, we propose statistical procedures for analyzing case-cohort sampled current status data under the additive hazards model. Asymptotical properties of the proposed estimator are described and we...
A major concern with some contagious diseases has recently led to an enormous effort to monitor population health status by several different means.
This work presents a modeling approach to overcome this poor data characteristic, allowing its use for the estimation of the true population disease picture. We use a state space model, where we run two processes in parallel - a process describing the non observable states of the population concerning the presence/absence of disease,...
Aitkin y Clayton (1980) proponen el análisis de modelos de duración mediante modelos lineales generalizados. En este trabajo extendemos esta metodología permitiendo que el efecto de alguna de las variables explicativas pueda no ser especificado. Así, el modelo propuesto es un modelo lineal generalizado semiparamétrico, con una componente paramétrica donde se especifica la forma funcional concreta del efecto de las variables explicativas sobre la duración, y una componente no paramétrica donde recogemos...
Multivariate failure time data arise in various forms including recurrent event data when individuals are followed to observe the sequence of occurrences of a certain type of event; correlated failure time when an individual is followed for the occurrence of two or more types of events for which the individual is simultaneously at risk, or when distinct individuals have depending event times; or more complicated multistate processes where individuals may move among a number of discrete states over...
Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from error assumptions and the presence of outliers and influential observations with the fitted models. Assuming censored data, we considered a classical analysis and Bayesian analysis assuming no informative priors for the parameters of the model with a cure fraction. A Bayesian approach was considered by using Markov Chain Monte Carlo Methods with Metropolis-Hasting algorithms steps to...
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