A semiparametric random effects model for multivariate competing risks data

Scheike, Thomas H.; Sun, Yanqing; Zhang, Mei‐Jie; Jensen, Tina Kold

doi:10.1093/biomet/asp082

Cited by 32 publications

(35 citation statements)

References 34 publications

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“…The predict() function of timereg computes the predicted cumulative incidence probability and an estimate of its variance at each fixed time point, and constructs (1 − α )100% simultaneous confidence bands over a given time interval. One further advantage is that the software can deal with cluster structure, see Scheike et al (2010).…”

Section: Introductionmentioning

confidence: 99%

Analyzing Competing Risk Data Using theRtimeregPackage

Scheike¹,

Zhang²

2011

J. Stat. Soft.

134

112

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In this paper we describe flexible competing risks regression models using the comp.risk() function available in the timereg package for R based on . Regression models are specified for the transition probabilities, that is the cumulative incidence in the competing risks setting. The model contains the Fine and Gray (1999) model as a special case. This can be used to do goodness-of-fit test for the subdistribution hazards' proportionality assumption (Scheike and Zhang 2008). The program can also construct confidence bands for predicted cumulative incidence curves.We apply the methods to data on follicular cell lymphoma from Pintilie (2007), where the competing risks are disease relapse and death without relapse. There is important non-proportionality present in the data, and it is demonstrated how one can analyze these data using the flexible regression models.

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Section: Introductionmentioning

confidence: 99%

Analyzing Competing Risk Data Using theRtimeregPackage

Scheike¹,

Zhang²

2011

J. Stat. Soft.

134

112

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“…A commonly used approach for producing dependent failure times relies on a frailty model. Numerous examples of generating dependent competing risks data following the shared gamma frailty model can be found in the literature [3–5,7,8]. Normal or positive stable frailty distributions have also been used in generating dependent competing risks data [9–11].…”

Section: Methods For Generating Dependent Competing Risks Datamentioning

confidence: 99%

“…An alternative approach where the marginal cumulative incidence functions follow a semiparametric additive model with frailties accounting for correlations between competing risks failure times within clusters was considered and implemented by Scheike et al [5]. Explicitly defining cumulative incidence functions and introducing random effects in a more complex manner provide a means to define very flexible models where early, late, and constant over time dependence between paired individuals can be imposed [21].…”

Section: Methods For Generating Dependent Competing Risks Datamentioning

confidence: 99%

“…Integrating ξ kl ( s, t ) over the entire period of time where events were observed produces one summary measure, ξ kl , quantifying the strength of the relationship between the individuals in the context of competing risks data. It should be noted that the cross-odds ratio proposed by Scheike et al [5] can be used as an alternative measure for quantifying the dependence between cause-specific failure times.…”

Section: Preliminariesmentioning

confidence: 99%

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Methods for generating paired competing risks data

Brazauskas

Le‐Rademacher

2016

Computer Methods and Programs in Biomedicine

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Background and objectives Clustered competing risks data arise often in genetic studies, multicenter investigations, and matched-pairs studies. In the last two decades, major advances in competing risks theory had been made. Many new statistical methods need to be evaluated via simulation studies. Some mechanisms for simulating clustered competing risks data have been considered in the literature. However, most of them produce data where the strength of the dependence between individuals within a cluster is not clear. In this article, we aim to examine various techniques for generating bivariate competing risks data. Methods Theoretical framework for simulating dependent competing risks data using latent failure time approach, multistate models, and shared frailty models is described. The steps needed to implement each method are outlined. Properties of each technique are discussed and standard measures of association are provided in order to assess the degree of dependence in simulated paired competing risks data. Results and conclusions In addition to describing a variety of techniques to generate dependent competing risks data, the cross-hazard ratios from multiple scenarios for each method are computed. The cross-hazard ratios provide a means to compare the level of dependence of the generated data across methods. This acts as a guide for researchers to select an approach and the parameters needed to achieve the desired degree of dependence for their simulation studies.

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“…Methods accommodating regression of the component disease onset time distribution on covariates also have been proposed (e.g. Scheike et al 2010). However, these methods have only been used to study index chronic disease in familial setting.…”

Section: Appropriate Semicompeting Risks Methods For Aging Researchmentioning

confidence: 99%

Semicompeting risks in aging research: methods, issues and needs

2014

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A semicompeting risks problem involves two-types of events: a nonterminal and a terminal event (death). Typically, the nonterminal event is the focus of the study, but the terminal event can preclude the occurrence of the nonterminal event. Semicompeting risks are ubiquitous in studies of aging. Examples of semicompeting risk dyads include: dementia and death, frailty syndrome and death, disability and death, and nursing home placement and death. Semicompeting risk models can be divided into two broad classes: models based only on observables quantities (class scriptO) and those based on potential (latent) failure times (class scriptL). The classical illness-death model belongs to class scriptO. This model is a special case of the multistate models, which has been an active area of methodology development. During the past decade and a half, there has also been a flurry of methodological activity on semicompeting risks based on latent failure times (scriptL models). These advances notwithstanding, the semi-competing risks methodology has not penetrated biomedical research, in general, and gerontological research, in particular. Some possible reasons for this lack of uptake are: the methods are relatively new and sophisticated, conceptual problems associated with potential failure time models are difficult to overcome, paucity of expository articles aimed at educating practitioners, and non-availability of readily usable software. The main goals of this review article are: (i) to describe the major types of semicompeting risks problems arising in aging research, (ii) to provide a brief survey of the semicompeting risks methods, (iii) to suggest appropriate methods for addressing the problems in aging research, (iv) to highlight areas where more work is needed, and (v) to suggest ways to facilitate the uptake of the semicompeting risks methodology by the broader biomedical research community.

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A semiparametric random effects model for multivariate competing risks data

Cited by 32 publications

References 34 publications

Analyzing Competing Risk Data Using theRtimeregPackage

Analyzing Competing Risk Data Using theRtimeregPackage

Methods for generating paired competing risks data

Semicompeting risks in aging research: methods, issues and needs

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