The pervasive use of prevalent cohort studies on disease duration increasingly calls for an appropriate methodology to account for the biases that invariably accompany samples formed by such data. It is well known, for example, that subjects with shorter lifetime are less likely to be present in such studies. Moreover, certain covariate values could be preferentially selected into the sample, being linked to the long‐term survivors. The existing methodology for estimating the propensity score using data collected on prevalent cases requires the correct conditional survival/hazard function given the treatment and covariates. This requirement can be alleviated if the disease under study has stationary incidence, the so‐called stationarity assumption. We propose a non‐parametric adjustment technique based on a weighted estimating equation for estimating the propensity score, which does not require modeling the conditional survival/hazard function when the stationarity assumption holds. The estimator's large‐sample properties are established, and its small‐sample behavior is studied via simulation. The estimated propensity score is utilized to estimate the survival curves. Copyright © 2014 John Wiley & Sons, Ltd

中文翻译：

---

存在长度偏差抽样的倾向得分估计：一种非参数调整方法。

普遍使用关于疾病持续时间的流行队列研究越来越需要一种适当的方法来解释这些数据所形成的样本总是伴随的偏差。例如，众所周知，寿命较短的受试者不太可能出现在此类研究中。此外，某些协变量值可以优先选择到样本中，与长期幸存者有关。使用对流行病例收集的数据估计倾向评分的现有方法需要正确的条件生存/危险函数给定治疗和协变量。如果所研究的疾病具有平稳的发病率，即所谓的平稳假设，则可以减轻这一要求。我们提出了一种基于加权估计方程的非参数调整技术来估计倾向得分，当平稳性假设成立时，它不需要对条件生存/风险函数进行建模。建立了估计器的大样本特性，并通过模拟研究了它的小样本行为。估计的倾向得分用于估计生存曲线。版权所有 © 2014 John Wiley & Sons, Ltd