Semiparametric Methods for Semi-competing Risks Problem with Censoring and Truncation

Studies of chronic life-threatening diseases often involve both mortality and morbidity. In observational studies, the data may also be subject to administrative left truncation and right censoring. Since mortality and morbidity may be correlated and mortality may censor morbidity, the Lynden-Bell estimator for left truncated and right censored data may be biased for estimating the marginal survival function of the non-terminal event. We propose a semiparametric estimator for this survival function based on a joint model for the two time-to-event variables, which utilizes the gamma frailty specification in the region of the observable data. Firstly, we develop a novel estimator for the gamma frailty parameter under left truncation. Using this estimator, we then derive a closed form estimator for the marginal distribution of the non-terminal event. The large sample properties of the estimators are established via asymptotic theory. The methodology performs well with moderate sample sizes, both in simulations and in an analysis of data from a diabetes registry.

Regression models for bivariate survival data

Multivariate lifetime 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 lifetime when an individual is followed for the occurrence of two or more types of events, or when distinct individuals have dependent event times. In most studies there are covariates such as treatments, group indicators, individual characteristics, or environmental conditions, whose relationship to lifetime is of interest. This leads to a consideration of regression models.The well known Cox proportional hazards model and its variations, using the marginal hazard functions employed for the analysis of multivariate survival data in literature are not sufficient to explain the complete dependence structure of pair of lifetimes on the covariate vector. Motivated by this, in Chapter 2, we introduced a bivariate proportional hazards model using vector hazard function of Johnson and Kotz (1975), in which the covariates under study have different effect on two components of the vector hazard function. The proposed model is useful in real life situations to study the dependence structure of pair of lifetimes on the covariate vector . The well known partial likelihood approach is used for the estimation of parameter vectors. We then introduced a bivariate proportional hazards model for gap times of recurrent events in Chapter 3. The model incorporates both marginal and joint dependence of the distribution of gap times on the covariate vector . In many fields of application, mean residual life function is considered superior concept than the hazard function. Motivated by this, in Chapter 4, we considered a new semi-parametric model, bivariate proportional mean residual life time model, to assess the relationship between mean residual life and covariates for gap time of recurrent events. The counting process approach is used for the inference procedures of the gap time of recurrent events. In many survival studies, the distribution of lifetime may depend on the distribution of censoring time. In Chapter 5, we introduced a proportional hazards model for duration times and developed inference procedures under dependent (informative) censoring. In Chapter 6, we introduced a bivariate proportional hazards model for competing risks data under right censoring. The asymptotic properties of the estimators of the parameters of different models developed in previous chapters, were studied. The proposed models were applied to various real life situations.

Oaxaca-Blinder type counterfactual decomposition methods for duration outcomes

Los métodos disponibles para realizar análisis de descomposición que se pueden aplicar cuando los datos son completamente observados, no son válidos cuando la variable de interés es censurada. Esto puede explicar la escasez de este tipo de ejercicios considerando variables de duración, las cuales se observan usualmente bajo censura. Este documento propone un método del tipo Oaxaca-Blinder para descomponer diferencias en la media en el contexto de datos censurados. La validez de dicho método radica en la identificación y estimación de la distribución conjunta de la variable de duración y un conjunto de covariables. Adicionalmente, se propone un método más general que permite descomponer otros funcionales de interés como la mediana o el coeficiente de Gini, el cual se basa en la especificación de la función de distribución condicional de la variable de duración dado un conjunto de covariables. Con el fin de evaluar el desempeño de dichos métodos, se realizan experimentos tipo Monte Carlo. Finalmente, los métodos propuestos son aplicados para analizar las brechas de género en diferentes características de la duración del desempleo en España, tales como la duración media, la probabilidad de ser desempleado de largo plazo y el coeficiente de Gini. Los resultados obtenidos permiten concluir que los factores diferentes a las características observables, tales como capital humano o estructura del hogar, juegan un papel primordial para explicar dichas brechas.

Bayesian Analysis for the Generalized Lognormal Distribution Applied to Failure Time Analysis

There are several versions of the lognormal distribution in the statistical literature, one is based in the exponential transformation of generalized normal distribution (GN). This paper presents the Bayesian analysis for the generalized lognormal distribution (logGN) considering independent non-informative Jeffreys distributions for the parameters as well as the procedure for implementing the Gibbs sampler to obtain the posterior distributions of parameters. The results are used to analyze failure time models with right-censored and uncensored data. The proposed method is illustrated using actual failure time data of computers.

Estimation with Interval Censored Data in Longitudinal Studies

In biostatistical applications interest often focuses on the estimation of the distribution of a time-until-event variable T. If one observes whether or not T exceeds an observed monitoring time at a random number of monitoring times, then the data structure is called interval censored data. We extend this data structure by allowing the presence of a possibly time-dependent covariate process that is observed until end of follow up. If one only assumes that the censoring mechanism satisfies coarsening at random, then, by the curve of dimensionality, typically no regular estimators will exist. To fight the curse of dimensionality we follow the approach of Robins and Rotnitzky (1992) by modeling parameters of the censoring mechanism. We model the right-censoring mechanism by modeling the hazard of the follow up time, conditional on T and the covariate process. For the monitoring mechanism we avoid modeling the joint distribution of the monitoring times by only modeling a univariate hazard of the pooled monitoring times, conditional on the follow up time, T, and the covariates process, which can be estimated by treating the pooled sample of monitoring times as i.i.d. In particular, it is assumed that the monitoring times and the right-censoring times only depend on T through the observed covariate process. We introduce inverse probability of censoring weighted (IPCW) estimator of the distribution of T and of smooth functionals thereof which are guaranteed to be consistent and asymptotically normal if we have available correctly specified semiparametric models for the two hazards of the censoring process. Furthermore, given such correctly specified models for these hazards of the censoring process, we propose a one-step estimator which will improve on the IPCW estimator if we correctly specify a lower-dimensional working model for the conditional distribution of T, given the covariate process, that remains consistent and asymptotically normal if this latter working model is misspecified. It is shown that the one-step estimator is efficient if each subject is at most monitored once and the working model contains the truth. In general, it is shown that the one-step estimator optimally uses the surrogate information if the working model contains the truth. It is not optimal in using the interval information provided by the current status indicators at the monitoring times, but simulations in Peterson, van der Laan (1997) show that the efficiency loss is small.

Nonparametric Estimation of the Bivariate Recurrence Time Distribution

This paper considers statistical models in which two different types of events, such as the diagnosis of a disease and the remission of the disease, occur alternately over time and are observed subject to right censoring. We propose nonparametric estimators for the joint distribution of bivariate recurrence times and the marginal distribution of the first recurrence time. In general, the marginal distribution of the second recurrence time cannot be estimated due to an identifiability problem, but a conditional distribution of the second recurrence time can be estimated non-parametrically. In literature, statistical methods have been developed to estimate the joint distribution of bivariate recurrence times based on data of the first pair of censored bivariate recurrence times. These methods are efficient in the current model because recurrence times of higher orders are not used. Asymptotic properties of the estimators are established. Numerical studies demonstrate the estimator performs well with practical sample sizes. We apply the proposed method to a Denmark psychiatric case register data set for illustration of the methods and theory.

Analyzing Bivariate Survival Data with Interval Sampling and Application to Cancer Epidemiology

In medical follow-up studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as the outcomes to identify the progression of a disease. In cancer studies interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme where the first failure event (cancer onset) is identified within a calendar time interval, the time of the initiating event (birth) can be retrospectively confirmed, and the occurrence of the second event (death) is observed sub ject to right censoring. To analyze this type of bivariate failure time data, it is important to recognize the presence of bias arising due to interval sampling. In this paper, nonparametric and semiparametric methods are developed to analyze the bivariate survival data with interval sampling under stationary and semi-stationary conditions. Numerical studies demonstrate the proposed estimating approaches perform well with practical sample sizes in different simulated models. We apply the proposed methods to SEER ovarian cancer registry data for illustration of the methods and theory.

Nonparametric versus Parametric Statistical Approaches for Genetic Anticipation: The Pancreatic Cancer Case

2000 Mathematics Subject Classi cation: 62N01, 62N05, 62P10, 92D10, 92D30.

The right to the screen: from media education to educommunication in Brazil

Nowadays in Brazil, some social organizations, governments and mass media are discussing the need to establish an oversight committee to guarantee the quality of television programmes, as well as the need to set a system to determine what kind of pro, gram is appropriate for every television time slot. Across Brazil, a representative body of children and young people have come to the conclusion that the right to receive quality television programmes is not enough. The children of the new generations think they have the right to access new technologies and the production of their own messages, in accordance with their own creativity, interests and lifestyle projects within society.

The right to screens: from media education to educommunication in Brazil

Social organizations, local governments and the media in Brazil are confronting them, selves over the need for an oversight board to guarantee quality television programming, and establishing a system to determine which television programs are appropriate for which television time slots. Across Brazil, a representative body of children and young adults have determined that the right to receive quality programming is not currently being met. Children of the new generation see themselves as having a right to access new technologies which enable them to produce their own messages according to their own creativity, interests, and social participation. This new generation wants to go beyond education in order to watch and conquer their ""right to screens"".

Longitudinal double-spin asymmetry for inclusive jet production in (p)over-right-arrow + (p)over-right-arrow collisions at root s=200 GeV

We report a new STAR measurement of the longitudinal double-spin asymmetry A(LL) for inclusive jet production at midrapidity in polarized p+p collisions at a center-of-mass energy of root s = 200 GeV. The data, which cover jet transverse momenta 5 < p(T) < 30 GeV/c, are substantially more precise than previous measurements. They provide significant new constraints on the gluon spin contribution to the nucleon spin through the comparison to predictions derived from one global fit to polarized deep-inelastic scattering measurements. They provide significant new constraints on the gluon spin contribution to the nucleon spin through the comparison to predictions derived from one global fit to polarized deep-inelastic scattering measurements.

`As intermitencias da morte`: debate concerning the right to lusophone literature in the youngers and adults` literacy process.

The article discusses the right to lusophone literature - Saramago, as example - in the process of teacher` s formation and inside of adults` literacy through formation research process at Sao Paulo city.

The contribution of classical (beta(1/2)-) and atypical beta-adrenoceptors to the stimulation of human white adipocyte lipolysis and right atrial appendage contraction by novel beta(3)-adrenoceptor agonists of differing selectivities

The role of beta(3)- and other putative atypical beta-adrenaceptors in human white adipocytes and right atrial appendage has been investigated using CGP 12177 and novel phenylethanolamine and aryloxypropanolamine beta(3)-adrenoceptor (beta(3)AR) agonists with varying intrinsic activities and selectivities for human cloned PAR subtypes. The ability to demonstrate beta(1/2)AR antagonist-insensitive (beta(3) or other atypical beta AR-mediated) responses to CGP 12177 was critically dependent on the albumin batch used to prepare and incubate the adipocytes. Four aryloxypropanolamine selective beta(3)AR agonists (SB-226552, SB-229432, SB-236923, SB-246982) consistently elicited beta(1/2)AR antagonist-insensitive lipolysis. However, a phenylethanolamine (SB-220646) that was a selective full beta(3)AR agonist elicited full lipolytic and inotropic responses that were sensitive to beta(1/2)AR antagonism, despite it having very low efficacies at cloned beta(1)- and beta(2)ARs. A component of the response to another phenylethanolamine selective beta(3)AR agonist (SB-215691) was insensitive to beta(1/2)AR antagonism in some experiments. Because novel aryloxypropanolamine had a beta(1/2)AR antagonist-insensitive inotropic effect, these results establish more firmly that beta(3)ARs mediate lipolysis in human white adipocytes, and suggest that putative 'beta(4)ARs' mediate inotropic responses to CGP 12177. The results also illustrate the difficulty of predicting from studies on cloned beta ARs which beta ARs will mediate responses to agonists in tissues that have a high number of beta(1)- and beta(2)ARs or a low number of beta(3)ARs.