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Fan, Juanjuan; Hsu, Li; Prentice, Ross L.

doi:10.1023/a:1026557315306

Cited by 35 publications

(7 citation statements)

References 1 publication

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“…Related to the cross-ratio, a class of weighted average of local dependence measures have been proposed (Fan, Hsu and Prentice 2000; Fan, Prentice and Hsu 2000). Fan and Prentice (2002) later generalized these dependence measures to accommodate regression effects on the marginal hazard functions.…”

Section: Introductionmentioning

confidence: 99%

Cross-ratio estimation for bivariate failure times with left truncation

Lin

Nan

2013

Lifetime Data Anal

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The cross-ratio is an important local measure that characterizes the dependence between bivariate failure times. To estimate the cross-ratio in follow-up studies where delayed entry is present, estimation procedures need to account for left truncation. Ignoring left truncation yields biased estimates of the cross-ratio. We extend the method of Hu et al. (2011) by modifying the risk sets and relevant indicators to handle left-truncated bivariate failure times, which yields the cross-ratio estimate with desirable asymptotic properties that can be shown by the same techniques used in Hu et al. (2011). Numerical studies are conducted.

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Section: Introductionmentioning

confidence: 99%

Cross-ratio estimation for bivariate failure times with left truncation

Lin

Nan

2013

Lifetime Data Anal

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“…Although our methodology employs a parametric regression model for the CCSHR, strong modeling assumptions can be avoided by using sufficiently flexible functions of time, such as polynomial functions or regression splines. We have conducted additional simulations, in which data from Frank family (Fan et al, 2000) were considered to check the adequacy of the proposed parametric functions of time. It is shown that the estimated results by using cubic polynomial models agree well with the true time-dependent association, suggesting that cubic polynomial models can yield satisfactory estimates for complex time-dependent association.…”

Section: Discussionmentioning

confidence: 99%

“…Many nonparametric methods have been proposed (Prentice and Cai, 1992; Hsu and Prentice, 1996;Wang and Wells, 2000) over the past twenty years; however, most are subject to complicated estimation or interpretation. Recently, a number of authors (Fan et al, 2000; Bandeen-Roche and Ning, 2008; Cheng and Fine, 2008) have proposed nonparametric estimations of the local conditional hazards ratio (Oakes, 1989), an easily interpreted association measure. However, in this scenario, each association is assumed to be constant over finite time “bins”.…”

Section: Introductionmentioning

confidence: 99%

Estimation of time‐dependent association for bivariate failure times in the presence of a competing risk

Ning

Bandeen‐Roche

2013

Biometrics

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Summary This paper targets the estimation of a time-dependent association measure for bivariate failure times, the conditional cause-specific hazards ratio (CCSHR), which is a generalization of the conditional hazards ratio (CHR) to accommodate competing risks data. We model the CCSHR as a parametric regression function of time and event causes and leave all other aspects of the joint distribution of the failure times unspecified. We develop a pseudo-likelihood estimation procedure for model fitting and inference and establish the asymptotic properties of the estimators. We assess the finite-sample properties of the proposed estimators against the estimators obtained from a moment-based estimating equation approach. Data from the Cache County study on dementia are used to illustrate the proposed methodology.

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“…The proposed measure ρ sp ( a ) assesses agreement between the two survival processes within the finite range of [0 , a ]. As with other finite region dependence measures (Fan et al, 2000), the fixed time point a is chosen within the support of the observed survival times. The choice of the time point a may depend on practical interest.…”

Section: Methodsmentioning

confidence: 99%

New Agreement Measures Based on Survival Processes

Guo

Peng

et al. 2013

Biometrics

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Summary The need to assess agreement arises in many scenarios in biomedical sciences when measurements were taken by different methods on the same subjects. When the endpoints are survival outcomes, the study of agreement becomes more challenging given the special characteristics of time-to-event data. In this paper, we propose a new framework for assessing agreement based on survival processes that can be viewed as a natural representation of time-to-event outcomes. Our new agreement measure is formulated as the chance-corrected concordance between survival processes. It provides a new perspective for studying the relationship between correlated survival outcomes and offers an appealing interpretation as the agreement between survival times on the absolute distance scale. We provide a multivariate extension of the proposed agreement measure for multiple methods. Furthermore, the new framework enables a natural extension to evaluate time-dependent agreement structure. We develop nonparametric estimation of the proposed new agreement measures. Our estimators are shown to be strongly consistent and asymptotically normal. We evaluate the performance of the proposed estimators through simulation studies and then illustrate the methods using a prostate cancer data example.

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Untitled

Cited by 35 publications

References 1 publication

Cross-ratio estimation for bivariate failure times with left truncation

Cross-ratio estimation for bivariate failure times with left truncation

Estimation of time‐dependent association for bivariate failure times in the presence of a competing risk

New Agreement Measures Based on Survival Processes

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