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AI 根据所提供的论文材料归类:摘要研究回顾性抽样与事件时间推断,未明确涉及所列因果、面板或分布效应方法。

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关注这篇论文的作者
J. D. Kalbfleisch
J. F. Lawless

基于回顾性发现机制的推断:输血相关艾滋病数据分析

研究问题
个体因最终事件发生而被纳入研究、起始事件时间随后回溯确定时,如何估计事件发生过程和潜伏期分布,并辨明识别限制?
方法与思路
作者假定起始事件遵循非齐次泊松过程,且等待时间分布不依赖起始时点,据此构造似然和参数、非参数估计方法。
材料陈述的贡献
摘要称获得简洁似然与参数估计算法,并通过输血相关艾滋病数据揭示感染总人数和潜伏期分位数估计中的识别问题。
阅读关注点
应核对回顾性纳入机制如何进入似然、等待时间独立条件,以及哪些参数可识别、哪些参数需要附加信息才能估计。
阅读边界
仅据 OpenAlex 索引摘要,稿件版本未注明,不能推断刊本变化。识别问题的具体形式、补充条件和估计结果未说明。

原文摘要

In some epidemiologic studies, identification of individuals for study is dependent on the occurrence of some event. Once an individual is identified, the time of a previous event, termed an initiating event, is determined retrospectively. This article considers problems of estimation when initiating events occur as a nonhomogeneous Poisson process, and the time s from the initiating event to the final event has pdf f(s) independent of the time of the initiating event. A simple form for the likelihood function is obtained and methods of parametric and nonparametric estimation are developed and considered. In particular, the model is related to a Poisson process in the plane, and for the parametric case simple algorithms are developed for parameter estimation. Regression models are also considered as well as various generalizations of the basic problem. Parallel to the theoretical development, data on patients diagnosed with acquired immune deficiency syndrome (AIDS) are considered and a detailed analysis is given. The data report the dates of diagnosis with AIDS and infection with human immunodeficiency virus, for patients reported to the Centers of Disease Control in Atlanta, Georgia, and thought to be infected by blood or blood-product transfusion. The analysis of these data was considered by Medley, Anderson, Cox, and Billard (1987), Lui et al. (1986), and others. It is shown that nonparametric analysis leads to simple estimates of certain parameters and indicates clearly the nature of an identifiability problem that arises with data of this kind. Problems arise in the estimation of the total number of infectives or percentiles of the distribution of the induction period, s.

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