Analysing bivariate survival data with interval sampling and application to cancer epidemiology
区间抽样下二元生存数据分析及癌症流行病学应用AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究疾病登记的二元生存与抽样偏差，未明确涉及因果效应或给定的其他细分。
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区间抽样下二元生存数据分析及癌症流行病学应用
- 研究问题
- 当样本仅纳入首次事件发生于指定日历区间的个体，且第二次事件可能右删失时，如何分析有序二元生存数据？
- 方法与思路
- 作者描述区间抽样造成的选择偏差，在半平稳和平稳条件下提出半参数分析方法，并以模拟和卵巢癌登记数据检验表现。
- 材料陈述的贡献
- 摘要称针对常被忽视的区间抽样偏差发展统计方法，数值研究显示其在中等样本量下表现良好，并提供登记数据应用。
- 阅读关注点
- 应核对区间抽样与右删失的联合机制、半平稳和平稳的定义，以及登记数据如何满足条件和处理抽样偏差。
- 阅读边界
- 仅据版本未注明的索引摘要；目标参数、平稳性条件及模拟指标未说明，不能推断完全消除偏差或刊本变化。
原文摘要
In biomedical studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as outcomes to identify the progression of a disease. In cancer studies, interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme, termed interval sampling, in which the first failure event is identified within a calendar time interval, the time of the initiating event can be retrospectively confirmed and the occurrence of the second failure event is observed subject to right censoring. In a cancer data application, the initiating, first and second events could correspond to birth, cancer onset and death. The fact that the data are collected conditional on the first failure event occurring within a time interval induces bias. Interval sampling is widely used for collection of disease registry data by governments and medical institutions, though the interval sampling bias is frequently overlooked by researchers. This paper develops statistical methods for analysing such data. Semiparametric methods are proposed under semi-stationarity and stationarity. Numerical studies demonstrate that the proposed estimation approaches perform well with moderate sample sizes. We apply the proposed methods to ovarian cancer registry data.