A pairwise likelihood augmented Cox estimator for left-truncated data.
A pairwise likelihood augmented Cox estimator for left-truncated data.
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作者：
Wu F;Kim S;Qin J;Saran R;Li Y
从流行队列中收集的生存数据受到左截断的影响，分析具有挑战性。左截断数据的条件方法可能效率低下，因为它们忽略了截断时间的边际似然中的信息。长度偏置采样方法可以提高估计效率，但前提是底层截断时间均匀；否则，它们可能产生有偏差的估计。提出了一种Cox模型下左截断数据的半参数方法，该方法对截断时间没有参数分布假设。我们的方法是基于条件似然和两两似然进行推理，这种方法消除了截断分布，但保留了边际似然中回归系数和基线风险函数的信息。提出了一种同时求解回归系数和基线危险函数的迭代算法。通过经验过程和u -过程理论，证明了该估计量是一致的，渐近正态的，并具有一个闭形式的一致方差估计量。仿真研究表明，与条件逼近估计器相比，我们的估计器在回归系数和累积基线危险函数方面都具有显著的效率增益。当一致截断假设成立时，我们的估计器与完全最大似然估计器相比具有较小的偏差和效率。在一项慢性肾脏疾病队列研究分析中的应用说明了该方法的实用性。
Survival data collected from a prevalent cohort are subject to left truncation and the analysis is challenging. Conditional approaches for left-truncated data could be inefficient as they ignore the information in the marginal likelihood of the truncation times. Length-biased sampling methods may improve the estimation efficiency but only when the underlying truncation time is uniform; otherwise, they may generate biased estimates. We propose a semiparametric method for left-truncated data under the Cox model with no parametric distributional assumption about the truncation times. Our approach is to make inference based on the conditional likelihood augmented with a pairwise likelihood, which eliminates the truncation distribution, yet retains the information about the regression coefficients and the baseline hazard function in the marginal likelihood. An iterative algorithm is provided to solve for the regression coefficients and the baseline hazard function simultaneously. By empirical process and U-process theories, it has been shown that the proposed estimator is consistent and asymptotically normal with a closed-form consistent variance estimator. Simulation studies show substantial efficiency gain of our estimator in both the regression coefficients and the cumulative baseline hazard function over the conditional approach estimator. When the uniform truncation assumption holds, our estimator enjoys smaller biases and efficiency comparable to that of the full maximum likelihood estimator. An application to the analysis of a chronic kidney disease cohort study illustrates the utility of the method.
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