Joint model for bivariate zero-inflated recurrent event data with terminal events

Bivariate recurrent event data are observed when subjects are at risk of experiencing two different type of recurrent events. In this paper, our interest is to suggest statistical model when there is a substantial portion of subjects not experiencing recurrent events but having a terminal event. In a context of recurrent event data, zero events can be related with either the risk free group or a terminal event. For simultaneously reflecting both a zero inflation and a terminal event in a context of bivariate recurrent event data, a joint model is implemented with bivariate frailty effects. Simulation studies are performed to evaluate the suggested models. Infection data from AML (acute myeloid leukemia) patients are analyzed as an application.     

Older age and a higher EHRA score allow higher levels of frailty syndrome to be predicted in patients with atrial fibrillation

Background: There is no research that evaluates the relationship between the severity of the symptoms of atrial fibrillation (AF), the presence of frailty syndrome and acceptance of the illness.

Methods: The study included 132 patients aged 72.7?±?6.73 with diagnosed AF. The severity of the symptoms of AF was determined according to European Heart Rhythm Association (EHRA) guidelines, frailty syndrome was assessed using the Tilburg frailty indicator (TFI) and the acceptance of the illness was assessed using the acceptance of illness scale (AIS). A standard statistical comparison and multiple regression analysis using the stepwise method were performed.

Results: In patients with AF, frailty was 5.31?±?2.69 (TFI). Frailty syndrome was diagnosed in 59.8% of the AF patients who had a score of 7.17?±?1.72. A higher level of EHRA score was connected with a smaller degree of the acceptance of the illness p?=?0.0000. The multiple regression model indicated that age (p?=?0.0009) and the severity of the symptoms (p?=?0.0001) are important predictors of frailty syndrome.

Conclusions: There is a relationship between the presence of frailty syndrome and the intensity of the symptoms and the acceptance of AF. Age and the EHRA score permitted higher levels of frailty syndrome to be predicted.     

Joint modeling of multivariate censored longitudinal and event time data with application to the Genetic Markers of Inflammation Study

The Genetic Markers of Inflammation Study (GenIMS) was conceived to investigate the role of severe sepsis, which is typically defined as system-wide multi-organ failure, on survival. One major hypothesis for this systemic collapse, and reduction in survival, is a cascade of pro-inflammatory and anti-inflammatory cytokines. In this paper, we devised a novel joint modeling strategy to evaluate the joint effect of longitudinal anti-inflammatory marker IL-6 and pro-inflammatory marker IL-10 on 90-day survival. We found that, on average, patients with high initial values of both IL-6 and IL-10, that tend to increase over time, are associated with a reduction in survival expectancy and that accounting for their assumed correlation was justified.     

Confidence intervals for the difference between two median survival times for clustered survival data

Clustered survival data arise often in clinical trial design, where the correlated subunits from the same cluster are randomized to different treatment groups. Under such design, we consider the problem of constructing confidence interval for the difference of two median survival time given the covariates. We use Cox gamma frailty model to account for the within-cluster correlation. Based on the conditional confidence intervals, we can identify the possible range of covariates over which the two groups would provide different median survival times. The associated coverage probability and the expected length of the proposed interval are investigated via a simulation study. The implementation of the confidence intervals is illustrated using a real data set.     

Understanding Mortality Rate Deceleration and Heterogeneity

ABSTRACT

Generic relationships between heterogeneity in population frailty and flattening of aggregate population hazard functions at extreme ages are drawn from classical mathematical results on the limiting behavior of Laplace transforms. In particular, it shows that the population hazard function converges to a constant precisely when the distribution of unobserved heterogeneity in initial mortalities behaves asymptotically as a polynomial near zero.     

Estimation in Semiparametric Marginal Shared Gamma Frailty Models

The semiparametric marginal shared frailty models in survival analysis have the non–parametric hazard functions multiplied by a random frailty in each cluster, and the survival times conditional on frailties are assumed to be independent. In addition, the marginal hazard functions have the same form as in the usual Cox proportional hazard models. In this paper, an approach based on maximum likelihood and expectation–maximization is applied to semiparametric marginal shared gamma frailty models, where the frailties are assumed to be gamma distributed with mean 1 and variance θ. The estimates of the fixed–effect parameters and their standard errors obtained using this approach are compared in terms of both bias and efficiency with those obtained using the extended marginal approach. Similarly, the standard errors of our frailty variance estimates are found to compare favourably with those obtained using other methods. The asymptotic distribution of the frailty variance estimates is shown to be a 50–50 mixture of a point mass at zero and a truncated normal random variable on the positive axis for θ₀ = 0. Simulations demonstrate that, for θ₀ < 0, it is approximately an x −(100 − x )%, 0 ≤ x ≤ 50, mixture between a point mass at zero and a truncated normal random variable on the positive axis for small samples and small values of θ₀; otherwise, it is approximately normal.     

Consistency of the NPML Estimator in the Right-Censored Transformation Model

Abstract.  This paper studies the representation and large-sample consistency for non-parametric maximum likelihood estimators (NPMLEs) of an unknown baseline continuous cumulative-hazard-type function and parameter of group survival difference, based on right-censored two-sample survival data with marginal survival function assumed to follow a transformation model, a slight generalization of the class of frailty survival regression models. The paper's main theoretical results are existence and unique a.s. limit, characterized variationally, for large data samples of the NPMLE of baseline nuisance function in an appropriately defined neighbourhood of the true function when the group difference parameter is fixed, leading to consistency of the NPMLE when the difference parameter is fixed at a consistent estimator of its true value. The joint NPMLE is also shown to be consistent. An algorithm for computing it numerically, based directly on likelihood equations in place of the expectation-maximization (EM) algorithm, is illustrated with real data.     

Tuning Parameter Selection in Penalized Frailty Models

The penalized likelihood approach of Fan and Li (2001 Fan, J., Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Association 96:1348–1360.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar], 2002 Fan, J., Li, R. (2002). Variable selection for Cox’s proportional hazards model and frailty model. The Annals of Statistics 30:74–99.[Crossref], [Web of Science ®] , [Google Scholar]) differs from the traditional variable selection procedures in that it deletes the non-significant variables by estimating their coefficients as zero. Nevertheless, the desirable performance of this shrinkage methodology relies heavily on an appropriate selection of the tuning parameter which is involved in the penalty functions. In this work, new estimates of the norm of the error are firstly proposed through the use of Kantorovich inequalities and, subsequently, applied to the frailty models framework. These estimates are used in order to derive a tuning parameter selection procedure for penalized frailty models and clustered data. In contrast with the standard methods, the proposed approach does not depend on resampling and therefore results in a considerable gain in computational time. Moreover, it produces improved results. Simulation studies are presented to support theoretical findings and two real medical data sets are analyzed.     

Frailty selection in bivariate survival models: A cautionary note

One problem that researchers face in analyzing the survival times of groups of related individuals is selecting how the distribution of frailty—an unobserved (or not adequately observed) random factor— should be specified. Several distributions have received attention—for instance, the gamma distribution and a nonparametric N‐point, discrete probability distribution. Researchers have selected these distributions more for mathematical convenience than for their ability to represent biological, social, or economic reality, and the implications of choosing one functional representation of frailty over alternative choices have not been studied extensively. In particular, researchers have paid little attention to the type of association that exists among survival times of individuals in a group or between those of a pair under specific frailty distributions. This research paper explores the association among survival times under gamma, inverse Gaussian, nonparametric N‐point, and Poisson distributions. It shows that the pattern and strength of this association depends on how the distribution of frailty is specified.     

A dynamic multistate model of robustness and frailty

Dynamic multistate models can show realistic population dynamics over time, model complex cycles, and encompass the life history of a cohort. This paper uses a recently developed approach to obtain the analytic solution of a time‐dependent multidimensional differential equation. The illustrative robust/frail model presented shows that the assumption of fixed individual frailty can be abandoned in a two living state model that allows transitions between health statuses and nonproportional hazards of death.