Likelihood-based methods for missing covariates in the Cox proportional hazards model
Likelihood-based methods for missing covariates in the Cox proportional hazards model
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DOI：
10.1198/016214501750332866
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发表时间：
2001-03-01
影响因子：
3.7
通讯作者：
Ibrahim, JG
中科院分区：
文献类型：
--
作者：
Herring, AH;Ibrahim, JG
与缺失协变量数据相关的问题是众所周知的，但往往被忽视。当缺失数据为随机缺失且删失数据为无信息时，本文提出了一种估计考克斯比例风险模型参数的方法。由于这种方法的计算负担，我们引入了一个近似，使我们能够使用加权期望最大化（EM）算法更容易地估计参数。当缺失的协变量是连续的，而不是分类的，我们实现了Monte Carlo版本的Ehl算法沿着的吉布斯采样器，以获得参数估计。我们也给出了这些估计的渐近分布。这种方法相对于完整病例分析的主要优点是，它可以产生更有效的参数估计值，并在MAR设置中校正偏倚。为了激励的方法，我们提出了一个由东部肿瘤协作组进行的III期黑色素瘤临床试验的分析。
Problems associated with missing covariate data are well known but often ignored. We present a method for estimating the parameters in the Cox proportional hazards model when the missing data are missing at random (MAR) and censoring is noninformative. Due to the computational burden of this method, we introduce an approximation that allows us to use a weighted expectation-maximization (EM) algorithm to estimate the parameters more easily. When the missing covariates are continuous rather than categorical, we implement a Monte Carlo version of the Ehl algorithm along with the Gibbs sampler to obtain parameter estimates. We also give the asymptotic distribution of these estimates. The primary advantage of this method over complete case analysis is that it produces more efficient parameter estimates and corrects for bias in the MAR setting. To motivate the methodology, we present an analysis of a phase III melanoma clinical trial conducted by the Eastern Cooperative Oncology Group.