缺少数据
反概率
逆概率加权
统计
审查(临床试验)
估计员
数学
事件(粒子物理)
计量经济学
蒙特卡罗方法
计算机科学
后验概率
贝叶斯概率
物理
量子力学
作者
Jun Park,Giorgos Bakoyannis,Ying Zhang,Constantin T. Yiannoutsos
出处
期刊:Biostatistics
[Oxford University Press]
日期:2021-01-07
卷期号:23 (3): 738-753
被引量:1
标识
DOI:10.1093/biostatistics/kxaa052
摘要
Competing risk data are frequently interval-censored, that is, the exact event time is not observed but only known to lie between two examination time points such as clinic visits. In addition to interval censoring, another common complication is that the event type is missing for some study participants. In this article, we propose an augmented inverse probability weighted sieve maximum likelihood estimator for the analysis of interval-censored competing risk data in the presence of missing event types. The estimator imposes weaker than usual missing at random assumptions by allowing for the inclusion of auxiliary variables that are potentially associated with the probability of missingness. The proposed estimator is shown to be doubly robust, in the sense that it is consistent even if either the model for the probability of missingness or the model for the probability of the event type is misspecified. Extensive Monte Carlo simulation studies show good performance of the proposed method even under a large amount of missing event types. The method is illustrated using data from an HIV cohort study in sub-Saharan Africa, where a significant portion of events types is missing. The proposed method can be readily implemented using the new function ciregic_aipw in the R package intccr.
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