Frailty Models in Survival Analysis

Wienke, Andreas

doi:10.1201/9781420073911

Cited by 360 publications

(445 citation statements)

References 233 publications

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“…This ICC parameter was simplified using the method of moments and using the assumption of independence between ∆ i j and ∆ il (where j l) conditional on the shared gamma frailty (Z i ). The shared frailty induces the within-cluster correlation as it is shared among the members of the same cluster (Wienke, 2011).…”

Section: Censoring Indicatorsmentioning

confidence: 99%

On the estimation of intracluster correlation for time-to-event outcomes in cluster randomized trials

2016

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Kalia, Sumeet, "On the estimation of intracluster correlation for time-to-event outcomes in cluster randomized trials" (2015). AbstractCluster randomized trials (CRTs) involve the random assignment of intact social units rather than independent subjects to intervention groups. Time-to-event outcomes often are endpoints in CRTs where the intracluster correlation coefficient (ICC) serves as a descriptive parameter to assess the similarity among outcomes in a cluster. However, estimating the ICC inCRTs with time-to-event outcomes is a challenge due to the presence of censored observations.The ICC is estimated for two CRTs using the censoring indicators and observed outcomes.A simulation study explores the effect of administrative censoring on estimating the ICC.Results show that the ICC estimators derived from censoring indicators and observed outcomes are negatively biased for positively correlated outcomes. Analytic work further supports these results. Censoring indicators may be preferred to estimate the ICC under moderate frequency of administrative censoring while the observed outcomes may be preferred under minimal frequency of administrative censoring.

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Section: Censoring Indicatorsmentioning

confidence: 99%

On the estimation of intracluster correlation for time-to-event outcomes in cluster randomized trials

2016

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“…It is assumed that survival times (in cluster i) are conditionally independent with respect to the frailty. This common frailty is the cause of dependence among lifetimes of within a cluster (Wienke, 2011). The frailties,z i , are i.i.d.…”

Section: Shared Frailty Modelsmentioning

confidence: 99%

“…While some authors use continuous distributions such as Gamma (Clayton, 1978;Vaupel et al, 1979), inverse Gaussian (Hougaard, 1984;Whitmore & Lee, 1991;Hanagal & Sharma, 2015), log-normal (McGilchrist & Aisbett, 1991) and positive stable (Hougaard, 1986), other authors use discrete distributions (Caroni, Crowder, & Kimber, 2010;Ata & Ozel, 2012). However, the Gamma distribution is the most common and widely used in literature for determining the frailty effect, which acts multiplicatively on the baseline hazard (Wienke, 2011). Due to its computational convenience, one parameter Gamma distribution (with mean 1 and variance θ) is used as the frailty distribution.…”

Section: Shared Frailty Modelsmentioning

confidence: 99%

Parameter Estimation of Shared Frailty Models Based on Particle Swarm Optimization

Aşkın¹,

İnan²,

Büyüklü³

2016

IJSP

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Standard survival techniques such as proportional hazards model are suffering from the unobserved heterogeneity. Frailty models provide an alternative way in order to account for heterogeneity caused by unobservable risk factors. Although vast studies have been done on estimation procedures, Evolutionary Algorithms (EAs) haven't received much attention in frailty studies. In this paper, we investigate the estimation performance of maximum likelihood estimation (MLE) via Particle Swarm Optimization (PSO) in modelling multivariate survival data with shared gamma frailty. Simulation studies and real data application are performed in order to assess the performance of MLE via PSO, quasi-Newton and conjugate gradient method.

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“…, then the non-conditional bivariate survival function is given by the formula (Wienke 2010 )). To take into account information about censoring, we shall put δ j = 0, 1, where δ j = 0 indicates right censoring, j = 0, 1.…”

Section: Survival Analysis Under a Frailty Settingmentioning

confidence: 99%

Study of the bivariate survival data using frailty models based on Lévy processes

Begun

Yashin

2018

AStA Adv Stat Anal

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Frailty models allow us to take into account the non-observable inhomogeneity of individual hazard functions. Although models with time-independent frailty have been intensively studied over the last decades and a wide range of applications in survival analysis have been found, the studies based on the models with time-dependent frailty are relatively rare. In this paper, we formulate and prove two propositions related to the identifiability of the bivariate survival models with frailty given by a nonnegative bivariate Lévy process. We discuss parametric and semiparametric procedures for estimating unknown parameters and baseline hazard functions. Numerical experiments with simulated and real data illustrate these procedures. The statements of the propositions can be easily extended to the multivariate case.

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Frailty Models in Survival Analysis

Cited by 360 publications

References 233 publications

On the estimation of intracluster correlation for time-to-event outcomes in cluster randomized trials

On the estimation of intracluster correlation for time-to-event outcomes in cluster randomized trials

Parameter Estimation of Shared Frailty Models Based on Particle Swarm Optimization

Study of the bivariate survival data using frailty models based on Lévy processes

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