Assessing cumulative incidence functions under the semiparametric additive risk model

Hyun, Seunggeun; Sun, Yanqing; Sundaram, Rajeshwari

doi:10.1002/sim.3640

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…Cheng et al (1998); Fine and Gray (1999); Shen and Cheng (1999); Scheike and Zhang (2003); Sun et al (2006); Hyun et al (2009)…”

Section: Introductionmentioning

confidence: 99%

“…In the recent methodological research, this critique has been taken up and increasingly, competing risk modelling has been applied. Prediction from transition hazard models has been prominently studied, see for example Cheng et al (1998); Fine and Gray (1999); Shen and Cheng (1999); Scheike and Zhang (2003); Sun et al (2006); Hyun et al (2009). Such approaches have also been extended for dealing with high-dimensional molecular data in competing risks settings by Binder et al (2009).…”

Section: Introductionmentioning

confidence: 99%

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Quantifying the predictive accuracy of time-to-event models in the presence of competing risks

et al. 2011

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The development of prognostic time to event models is an area of active research, notably in the fields of cardiology, oncology and intensive care medicine. These models try to find a link between patient covariates and an event at a later time point, and are used for example for therapy assignment, risk stratification or interhospital quality assurance. Increasingly, interest is not only in prognostic models with one possible type of event (e.g. death), but in models that distinguish between several competing endpoints. However, research into methods for the evaluation of the prognostic potential of these models is still needed, as most proposed methods measure either discrimination or calibration of models, but do not examine both simultaneously. We adapt the prediction error proposal of Graf et al. (1999) and Gerds and Schumacher (2006) to handle models with more than one possible event type and introduce a consistent estimator. A simulation study investigating the behaviour of the estimator in small sample size situations and for different levels of censoring together with a real data application follows, highlighting the usefulness of the proposed approach for quantifying effects of model misspecification in summary models for a competing risks setting.

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“…Cheng et al (1998); Fine and Gray (1999); Shen and Cheng (1999); Scheike and Zhang (2003); Sun et al (2006); Hyun et al (2009)…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantifying the predictive accuracy of time-to-event models in the presence of competing risks

et al. 2011

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“…Note that such an approach would significantly simplify the original justifications given by Lin ( 1997 ) and generalizes his idea mainly used in the context of competing risks (Scheike and Zhang 2003 ; Hyun et al. 2009 ; Beyersmann et al. 2013 ).…”

Section: Discussion and Further Researchmentioning

confidence: 75%

The wild bootstrap for multivariate Nelson–Aalen estimators

Bluhmki¹,

Dobler

Beyersmann³

et al. 2018

Lifetime Data Anal

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We rigorously extend the widely used wild bootstrap resampling technique to the multivariate Nelson-Aalen estimator under Aalen's multiplicative intensity model. Aalen's model covers general Markovian multistate models including competing risks subject to independent left-truncation and right-censoring. This leads to various statistical applications such as asymptotically valid confidence bands or tests for equivalence and proportional hazards. This is exemplified in a data analysis examining the impact of ventilation on the duration of intensive care unit stay. The finite sample properties of the new procedures are investigated in a simulation study.

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Constrained parametric model for simultaneous inference of two cumulative incidence functions

2012

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We propose a parametric regression model for the cumulative incidence functions (CIFs) commonly used for competing risks data. The model adopts a modified logistic model as the baseline CIF and a generalized odds-rate model for covariate effects, and it explicitly takes into account the constraint that a subject with any given prognostic factors should eventually fail from one of the causes such that the asymptotes of the CIFs should add up to one. This constraint intrinsically holds in a nonparametric analysis without covariates, but is easily overlooked in a semiparametric or parametric regression setting. We hence model the CIF from the primary cause assuming the generalized odds-rate transformation and the modified logistic function as the baseline CIF. Under the additivity constraint, the covariate effects on the competing cause are modeled by a function of the asymptote of the baseline distribution and the covariate effects on the primary cause. The inference procedure is straightforward by using the standard maximum likelihood theory. We demonstrate desirable finite-sample performance of our model by simulation studies in comparison with existing methods. Its practical utility is illustrated in an analysis of a breast cancer dataset to assess the treatment effect of tamoxifen, adjusting for age and initial pathological tumor size, on breast cancer recurrence that is subject to dependent censoring by second primary cancers and deaths.

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Assessing cumulative incidence functions under the semiparametric additive risk model

Cited by 4 publications

References 13 publications

Quantifying the predictive accuracy of time-to-event models in the presence of competing risks

Quantifying the predictive accuracy of time-to-event models in the presence of competing risks

The wild bootstrap for multivariate Nelson–Aalen estimators

Constrained parametric model for simultaneous inference of two cumulative incidence functions

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