估计疾病发病率的反计算方法的一些变体:法罗群岛多发性硬化症数据的应用

IF 1.2 4区 数学 International Journal of Biostatistics Pub Date : 2005-06-09 DOI:10.2202/1557-4679.1002
N. Jewell, B. Lu
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引用次数: 2

摘要

反向计算是一种最初为研究艾滋病毒发病率而开发的技术。在这里,我们介绍了一些估计技术的变体,它们允许(i)未观察到的疾病发病率计数的相关性,以及(ii)在EM算法中使用平滑步骤作为最大化步骤的一部分,以减少由于小诊断计数而导致的不稳定性。这两个问题在分析小型“流行病”时都很重要。此外,确定诊断计数之间的相关性为未观察到的发病率计数之间的相关性提供了间接证据,暗示了感染原的可能性。我们利用法罗群岛的数据,通过重建多发性硬化症发病的发病率强度函数来说明这些观点。此前,Joseph, Wolfson和Wolfson(1990)对这些数据进行了统计检验,以解决多发性硬化症的传染性问题。我们认为,发病率函数不能直接阐明第二次世界大战期间法罗群岛多发性硬化症的神秘起源,特别是不能区分感染或环境因素的假设。
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Some Variants of the Backcalculation Method for Estimation of Disease Incidence: An Application to Multiple Sclerosis Data from the Faroe Islands
Backcalculation is a technique that was originally developed for the study of HIV incidence. Here we introduce some variants of the estimation technique that allow for (i) correlation of the unobserved disease incidence counts, and (ii) the use of a smoothing step as part of the maximizing step in the EM algorithm to reduce instability due to small diagnosis counts. Both of these issues can be important in the analysis of small "epidemics." In addition, identification of correlation between diagnosis counts provides indirect evidence of correlation among unobserved incidence counts, hinting at the possibility of an infectious agent. We illustrate the ideas by reconstructing an incidence intensity function for the onset of multiple sclerosis, using data from the Faroe Islands. Previously, this data had been examined statistically, by Joseph, Wolfson & Wolfson (1990), to address the issue of infectiousness of multiple sclerosis. We argue that the incidence function cannot directly shed light on the enigmatic origin of multiple sclerosis in the Faroe Islands during World War II, and, in particular, cannot discriminate between hypotheses of an infectious or environmental agent.
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来源期刊
International Journal of Biostatistics
International Journal of Biostatistics Mathematics-Statistics and Probability
CiteScore
2.30
自引率
8.30%
发文量
28
期刊介绍: The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.
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