Estimating the causal effect of a time-varying treatment on time-to-event using structural nested failure time models

被引：28

作者：

Lok, J ^{[1
]}

Gill, R

van der Vaart, A

Robins, J

机构：

[1] Leiden Univ, Med Ctr, NL-2300 RA Leiden, Netherlands

[2] Vrije Univ Amsterdam, Amsterdam, Netherlands

[3] Univ Utrecht, NL-3508 TC Utrecht, Netherlands

[4] Harvard Univ, Cambridge, MA 02138 USA

来源：

STATISTICA NEERLANDICA | 2004年 / 58卷 / 03期

关键词：

survival analysis; counterfactual variables; G-computation; G-estimation;

D O I：

10.1111/j.1467-9574.2004.00123.x

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this paper we review an approach to estimating the causal effect of a time-varying treatment on time to some event of interest. This approach is designed for a situation where the treatment may have been repeatedly adapted to patient characteristics, which themselves may also be time-dependent. In this situation the effect of the treatment cannot simply be estimated by conditioning on the patient characteristics, as these may themselves be indicators of the treatment effect. This so-called time-dependent confounding is typical in observational studies. We discuss a new class of failure time models, structural nested failure time models, which can be used to estimate the causal effect of a time-varying treatment, and present methods for estimating and testing the parameters of these models.

引用

页码：271 / 295

页数：25

共 15 条

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[2]

Gill RD, 2001, ANN STAT, V29, P1785

[3]

LOK JJ, 2001, THESIS VRIJE U AMSTE

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