Calibration Regression of Censored Lifetime Medical Cost
删失终生医疗费用的校准回归AI 解读 ↓
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删失终生医疗费用的校准回归
- 研究问题
- 在终生医疗费用分布不规则且随访不完整时，如何联合建模费用与生存时间，并为协变量关联提供可靠的统计推断？
- 方法与思路
- 作者提出半参数校准回归，允许变换后的费用与生存时间分别线性关联协变量，并将加权对数秩估计扩展至标记点过程。
- 材料陈述的贡献
- 摘要称估计量一致且渐近正态，并提出适用于非光滑估计函数的一种样本方差估计程序；模拟与肺癌试验用于展示方法。
- 阅读关注点
- 应核对随访与删失机制的条件、两个结果的变换及联合误差设定，以及一致性、方差估计和稳健性讨论的适用边界。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；摘要未说明完整观察机制假设、效率结论或模拟数值，临床示例不能证明普遍可靠。
原文摘要
Analysis of lifetime medical cost is challenged by its irregular distribution and by incomplete follow-up, particularly the latter with limited study duration as is typical in medical research. This article proposes the so-called calibration regression to address these issues by modeling not only lifetime medical cost but also survival time, in a semiparametric fashion. It is postulated that both outcomes, on possibly transformed scales, linearly relate to the covariates; however, the bivariate model error distribution is completely unspecified. Whereas this conceptually simple model is a natural multivariate generalization of the accelerated failure time model, this article proposes an inference procedure by extending the weighted log-rank estimating function to the marked point process framework. The resulting estimators are consistent and asymptotically normal. In addition, robustness and efficiency issues are discussed. Furthermore, a novel sample-based variance estimation procedure is proposed for estimators based on nonsmooth estimating functions in general. Finally, simulation studies show that this proposal is reliable for practical use. An illustration with application to a lung cancer clinical trial is provided.