The Jackknife estimate of covariance of two Kaplan–Meier integrals with covariables

In this paper the Jackknife estimate of covariance of two Kaplan–Meier integrals with covariates is introduced. Its strong consistency is established under mild conditions. Several applications of the estimator are discussed.     

Expanded renal transplantation: a competing risk model approach

Multi-state models (MSMs) are useful to analyze survival data when, besides the event of main interest, one or more intermediate states of the individual are identified. These models take the several existing states and the possible transitions among them into account. At the same time, covariate effects on each transition intensity may be investigated separately and, therefore, MSMs are more flexible than the standard Cox proportional hazards model. In this work, we use MSMs to investigate the impact of the quality of a transplanted kidney for a group of patients at the Hospital Universitario Central de Asturias. Specifically, we use an illness-death model to study the evolution of patients with kidney disease who received a renal transplant after a dialysis period. The intermediate state is defined as the failure of the received organ, while the terminating state is the death of the patient. In order to increase the potential number of organs available for transplant, the standards of quality for the transplanted kidneys were relaxed (the new criteria are labeled expanded criteria), and these ‘expanded kidneys’ were transplanted in appropriate candidates (older patients, with higher prevalence of diabetes mellitus). Results suggest that the expanded kidneys have a minor effect on survival, while both the kidney mortality and the risk of death increase with the patient's age and the serum creatinine and serum hemoglobin levels.     

Strong consistency of presmoothed Kaplan–Meier integrals when covariables are present

It is known that the Kaplan–Meier estimation may be improved via presmoothing methods. In this article, we introduce an extended presmoothed Kaplan–Meier estimator in the presence of covariates. The main result is the strong consistency of general empirical integrals based on such an estimator. As applications, one can obtain a consis-tent multivariate empirical distribution under censoring, and also can obtain a consistent estimation of regression parameters. We illustrate the new estimation methods through simulations and real data analysis.     

Nonparametric estimation of transition probabilities in a non-Markov illness–death model

In this paper we consider nonparametric estimation of transition probabilities for multi-state models. Specifically, we focus on the illness-death or disability model. The main novelty of the proposed estimators is that they do not rely on the Markov assumption, typically assumed to hold in a multi-state model. We investigate the asymptotic properties of the introduced estimators, such as their consistency and their convergence to a normal law. Simulations demonstrate that the new estimators may outperform Aalen–Johansen estimators (the classical nonparametric tool for estimating the transition probabilities) in non-Markov situation. An illustration through real data analysis is included.     

Quality of Life and Leisure Activities: How do Leisure Activities Contribute to Subjective Well-Being?

The quality of life is determined with objective factors and also with subjective perception of factors which influence human life. Leisure activities play a very important role in subjective well-being because they provide opportunities to meet life values and needs. Through participation in leisure activities people build social relationships, feel positive emotions, acquire additional skills and knowledge, and therefore improve their quality of life. In this report we will explore how leisure activities improve subjective well-being. We will try to distinguish among different types of leisure activities and find those which contribute more to the subjective well-being. Particularly, we will explore which leisure activities contribute to the subjective well-being of women and men of different age. Our study is based on data from a representative sample of Croatian citizens (N = 4,000), who estimated their subjective well-being and participation in various leisure activities. First, we will describe the subjective well-being of various groups of people who differ by gender and age. Afterward, we will identify important leisure activities which determine subjective well-being across age and gender groups. Overall, our results show that engagement in leisure activities contributes to subjective well being, while the pattern of important leisure activities somewhat varies across different age and gender groups.     

Copula-graphic estimation with left-truncated and right-censored data

In Survival Analysis and related fields of research right-censored and left-truncated data often appear. Usually, it is assumed that the right-censoring variable is independent of the lifetime of ultimate interest. However, in particular applications dependent censoring may be present; this is the case, for example, when there exist several competing risks acting on the same individual. In this paper we propose a copula-graphic estimator for such a situation. The estimator is based on a known Archimedean copula function which properly represents the dependence structure between the lifetime and the censoring time. Therefore, the current work extends the copula-graphic estimator in de Uña-Álvarez and Veraverbeke [Generalized copula-graphic estimator. Test. 2013;22:343–360] in the presence of left-truncation. An asymptotic representation of the estimator is derived. The performance of the estimator is investigated in an intensive Monte Carlo simulation study. An application to unemployment duration is included for illustration purposes.     

Comparing Nonparametric Estimators for Length-Biased Data

Length-biased data appear when sampling lifetimes by cross-section. Right-censoring may affect the sampled information due to time limitation in following-up, lost to follow-up cases, etc. In this article, we compare by simulations two alternative nonparametric estimators of the lifetime distribution function when the data are length-biased and right-censored. These estimates, recently introduced in the literature, are based on nonparametric maximum-likelihood and moment-based principles. It is shown that the relative benefits associated to each estimator depend on several factors, such as the shape of the underlying distribution, sample size, or censoring level.