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首页> 外文期刊>American Medical Journal >Level of Non-Matching Primary Transplant Cases According to Repeat Transplants Cases and a New Bivariate Poisson Distribution
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Level of Non-Matching Primary Transplant Cases According to Repeat Transplants Cases and a New Bivariate Poisson Distribution

机译：根据重复移植病例和新的双变量泊松分布的非匹配原发移植病例的水平

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摘要

In their practice, healthcare administrators and professionals often wonder about the non-matching level organs in transplants for the sake of future forecasting. Currently, there is no appropriate methodology to analyze the pertinent transplant data and describe the patterns. The lack of a suitable methodology in the literature originates from an incorrect impression that the primary transplant cases and the repeat transplant cases are two separate and independent Poisson probability processes. In fact, the actual data on the primary and repeat transplant cases in USA during the year 2014 indicate otherwise with a high degree of correlation between them. One wonders about the missing link and it hides in their model as this article articulates. The aims of this article are set to find an appropriate underlying model for the data and then construct an analytic methodology. In this research process, a novel and useful bivariate probability distribution is discovered and it is named here "seemingly independent bivariate Poisson distribution" for a lack of better title. Its statistical properties are derived, explained and illustrated. This new bivariate distribution helps not only to estimate the non-matching level of organs in the transplant cases but also to project the number of repeat transplant cases based on knowing the number of primary transplant cases and vice versa.
机译：在实践中，为了将来进行预测，医疗保健管理人员和专业人员经常会对移植物中不匹配的器官感到疑惑。当前，没有合适的方法来分析相关的移植数据并描述模式。文献中缺乏合适的方法论是由于错误的印象，即一次移植病例和重复移植病例是两个独立的独立泊松概率过程。实际上，2014年美国原发和再移植病例的实际数据表明，它们之间存在高度相关性。有人对缺失的链接感到好奇，它在本文阐述时隐藏在他们的模型中。本文的目的是为数据找到合适的基础模型，然后构建一种分析方法。在此研究过程中，发现了一种新颖且有用的双变量概率分布，由于缺乏更好的称谓，因此将其命名为“看似独立的双变量泊松分布”。推导，解释和说明其统计属性。这种新的双变量分布不仅有助于估计移植病例中器官的不匹配水平，而且可以基于已知的初次移植病例的数量来预测重复移植病例的数量，反之亦然。

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