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通过使用随机效应模型纵向协变量提高删失生存数据的效率。

Increasing efficiency from censored survival data by using random effects to model longitudinal covariates.

作者信息

Hogan J W, Laird N M

机构信息

Center for Statistical Sciences, Brown University, Providence, RI 02912, USA.

出版信息

Stat Methods Med Res. 1998 Mar;7(1):28-48. doi: 10.1177/096228029800700104.

DOI:10.1177/096228029800700104
PMID:9533260
Abstract

When estimating a survival time distribution, the loss of information due to right censoring results in a loss of efficiency in the estimator. In many circumstances, however, repeated measurements on a longitudinal process which is associated with survival time are made throughout the observation time, and these measurements may be used to recover information lost to censoring. For example, patients in an AIDS clinical trial may be measured at regular intervals on CD4 count and viral load. We describe a model for the joint distribution of a survival time and a repeated measures process. The joint distribution is specified by linking the survival time to subject-specific random effects characterizing the repeated measures, and is similar in form to the pattern mixture model for multivariate data with nonignorable nonresponse. We also describe an estimator of survival derived from this model. We apply the methods to a long-term AIDS clinical trial, and study properties of the survival estimator. Monte Carlo simulation is used to estimate gains in efficiency when the survival time is related to the location and scale of the random effects distribution. Under relatively light censoring (20%), the methods yield a modest gain in efficiency for estimating three-year survival in the AIDS clinical trial. Our simulation study, which mimics characteristics of the clinical trial, indicates that much larger gains in efficiency can be realized under heavier censoring or with studies designed for long term follow up on survival.

摘要

在估计生存时间分布时,由于右删失导致的信息损失会使估计量的效率降低。然而,在许多情况下,在整个观察期内会对与生存时间相关的纵向过程进行重复测量,这些测量可用于找回因删失而丢失的信息。例如,在一项艾滋病临床试验中,可能会定期测量患者的CD4细胞计数和病毒载量。我们描述了一种生存时间与重复测量过程的联合分布模型。通过将生存时间与表征重复测量的个体特定随机效应相联系来指定联合分布,其形式类似于具有不可忽略的无应答的多变量数据的模式混合模型。我们还描述了从该模型导出的生存估计量。我们将这些方法应用于一项长期艾滋病临床试验,并研究生存估计量的性质。当生存时间与随机效应分布的位置和尺度相关时,使用蒙特卡罗模拟来估计效率的提高。在相对轻度删失(20%)的情况下,这些方法在艾滋病临床试验中估计三年生存率时效率有适度提高。我们模拟临床试验特征的研究表明,在更严重的删失情况下或在设计用于长期生存随访的研究中,可以实现更大的效率提高。

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