Regression Analysis of Multivariate Incomplete Failure Time Data by Modeling Marginal Distributions

Wei, L. J.; Lin, Danyu; Weissfeld, Lisa A.

doi:10.1080/01621459.1989.10478873

Cited by 1,414 publications

(518 citation statements)

References 18 publications

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“…3; effect which have a simple marginal interpretation. Such models can be formulated using copula functions, but it is undesirable to base inferences on a particular parametric model and for this reason the marginal methods proposed by Wei et al (1989) are preferred. Inference regarding regression coefficients in this framework are carried out by use of a robust sandwich-type variance estimate easily computed in SAS or R/S-PLUS (Therneau & Grambsch, 2000).…”

Section: Discussionmentioning

confidence: 99%

“…In this section, we investigate the asymptotic bias caused by event-dependent censoring when marginal estimating equations are specified based on a working independence assumption, as is done for the multivariate approach of Wei et al (1989). We first present the general framework and then study the one-sample estimates in detail.…”

Section: Asymptotic Biases Of Marginal Estimatorsmentioning

confidence: 99%

“…When the analysis of the failure times adopted for multivariate failure time data (Wei et al, 1989) is often based on a working independence assumption, it is the dependence on the event times that is particularly problematic.…”

Section: A Model For Event-dependent Censoringmentioning

confidence: 99%

“…There are three common frameworks for the analysis of multivariate failure time data including frailty-based models (Therneau & Grambsch, 2000), copula models (Liang, Self, Bandeen-Roche, & Zeger, 1995;Nelsen, 2006), and marginal methods (Wei, Lin, & Weissfeld, 1989). While frailty models and copula models yield multivariate distributions, they require assumptions regarding the frailty distribution or the copula function respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Such fully specified models are appealing if interest lies in estimating the degree of association between two or more event times or for prediction, but when assessing treatment effects in clinical trials it is generally desirable to make minimal assumptions and maintain robustness. The marginal approach of Wei et al (1989) …”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Marginal Methods for Multivariate Failure Times Under Event-Dependent Censoring

Wu¹,

Cook²

2014

IJSP

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Many chronic diseases put individuals at increased risk of several different types of adverse clinical events. Typically these events are combined to define composite events which are then used as the basis of treatment evaluation. A potentially more efficient approach is to conduct separate marginal assessments of the effect of treatment on each component and then to synthesize this information across each type of event. While there is considerable potential for more powerful tests of treatment effect in this setting, it is possible that dependent censoring can cause problems. This happens when the occurrence of one type of event increases the risk of withdrawal from a study and hence alters the probability of observing events of other types. The purpose of this article is to formulate a model which reflects this type of mechanism, to evaluate the effect on the asymptotic and finite sample properties of marginal estimates, and to examine the performance of estimators obtained using flexible inverse probability weighted marginal estimating equations. Data from a motivating study are used for illustration.

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Section: Discussionmentioning

confidence: 99%

Section: Asymptotic Biases Of Marginal Estimatorsmentioning

confidence: 99%

Section: A Model For Event-dependent Censoringmentioning

confidence: 99%