Panel current status data arise frequently in biomedical studies when the occurrence of a particular clinical condition is only examined at several prescheduled visit times. Existing methods for analyzing current status data have largely focused on regression modeling based on commonly used survival models such as the proportional hazards model and the accelerated failure time model. However, these procedures have the limitations of being difficult to implement and performing sub-optimally in relatively small sample sizes. The performance of these procedures is also unclear under model mis-specification. In addition, no methods currently exist to evaluate the prediction performance of estimated risk models with panel current status data. In this paper, we propose a simple estimator under a general class of non-parametric transformation (NPT) models by fitting a logistic regression working model and demonstrate that our proposed estimator is consistent for the NPT model parameter up to a scale multiplier. Furthermore, we propose non-parametric estimators for evaluating the prediction performance of the risk score derived from model fitting, which is valid regardless of the adequacy of the fitted model. Extensive simulation results suggest that our proposed estimators perform well in finite samples and the regression parameter estimators outperform existing estimators under various scenarios. We illustrate the proposed procedures using data from the Framingham Offspring Study. This article is protected by copyright. All rights reserved.

中文翻译：

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使用面板当前状态数据开发和评估风险预测模型


当仅在几个预先安排的访问时间检查特定临床状况的发生时，小组当前状态数据经常出现在生物医学研究中。现有的分析当前状态数据的方法主要集中于基于常用生存模型（例如比例风险模型和加速失效时间模型）的回归建模。然而，这些程序具有难以实施并且在相对较小的样本量中表现不佳的局限性。在模型错误指定的情况下，这些程序的性能也不清楚。此外，目前还没有方法可以利用面板当前状态数据来评估估计风险模型的预测性能。在本文中，我们通过拟合逻辑回归工作模型，在一般类别的非参数变换（NPT）模型下提出了一个简单的估计器，并证明我们提出的估计器对于 NPT 模型参数在尺度乘数范围内是一致的。此外，我们提出了非参数估计器，用于评估模型拟合得出的风险评分的预测性能，无论拟合模型是否充分，该估计器都是有效的。大量的模拟结果表明，我们提出的估计器在有限样本中表现良好，并且回归参数估计器在各种情况下都优于现有的估计器。我们使用弗雷明汉后代研究的数据来说明拟议的程序。本文受版权保护。版权所有。