Semiparametric Marginal Regression Analysis for Dependent Competing Risks Under an Assumed Copula
给定 Copula 下相依竞争风险的半参数边际回归AI 解读 ↓
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给定 Copula 下相依竞争风险的半参数边际回归
- 研究问题
- 竞争风险中只观察最先发生的事件及时间时，如何借助事件间依赖关系的假设识别边际分布，并进行半参数边际回归？
- 方法与思路
- 作者假定潜在事件时间的联合分布具有已知 Copula，以半参数变换模型刻画边际回归，并提出非参数最大似然分析。
- 材料陈述的贡献
- 摘要称提供得分函数和信息矩阵的显式表达，研究大样本与有限样本性质，并可用于相依删失的敏感性分析及临床数据示例。
- 阅读关注点
- 核对已知 Copula 假设承担的识别作用、依赖参数如何确定，以及外部信息和敏感性分析如何影响回归结论。
- 阅读边界
- 仅据所给 Crossref 摘要；边际识别依赖事件间关系假设，摘要未说明 Copula 误设的影响，也未将回归系数解释为因果效应。
原文摘要
Summary Competing risks problems arise in many fields of science, where two or more types of event may occur on a subject, but only the event occurring first is observed together with its occurrence time, and other events are censored. The marginal and joint distributions of event times for competing risks cannot be identified from the observed data without assuming the relationship between events. The commonly adopted independent censoring assumption may be easily violated. An alternative is to assume that the joint distribution of event times follows a known copula, which is an explicit function of the marginal distributions. On the basis of the latter assumption, we consider marginal regression analysis for dependent competing risks, with the marginal regressions performed via semiparametric transformation models, including the proportional hazards and proportional odds models. We propose a non-parametric maximum likelihood analysis, which provides explicit expressions for the score functions and information matrix, and facilitates convenient computations. Large and finite sample properties are studied. We report an illustration with data from an acquired immune deficiency syndrome clinical trial where the censoring may be dependent. The proposal can be readily used as a sensitivity analysis for assessing effects of potential dependent censoring and can incorporate external information on the association of competing risks.