Modeling Cumulative Incidences of Dementia and Dementia-Free Death Using a Novel Three-Parameter Logistic Function

IF 1.2 4区 数学
Y. Cheng
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引用次数: 18

Abstract

Parametric modeling of univariate cumulative incidence functions and logistic models have been studied extensively. However, to the best of our knowledge, there is no study using logistic models to characterize cumulative incidence functions. In this paper, we propose a novel parametric model which is an extension of a widely-used four-parameter logistic function for dose-response curves. The modified model can accommodate various shapes of cumulative incidence functions and be easily implemented using standard statistical software. The simulation studies demonstrate that the proposed model is as efficient as or more efficient than its nonparametric counterpart when it is correctly specified, and outperforms the existing Gompertz model when the underlying cumulative incidence function is sigmoidal. The practical utility of the modified three-parameter logistic model is illustrated using the data from the Cache County Study of dementia.
用一种新颖的三参数逻辑函数建模痴呆和无痴呆死亡的累积发病率
单变量累积关联函数的参数化建模和logistic模型得到了广泛的研究。然而,据我们所知,还没有研究使用逻辑模型来表征累积关联函数。在本文中,我们提出了一种新的参数模型,它是一种广泛使用的剂量-反应曲线的四参数逻辑函数的扩展。修正后的模型可以适应各种形状的累积关联函数,并且易于使用标准统计软件实现。仿真研究表明,当正确指定该模型时,该模型与非参数模型一样有效或更有效,并且当潜在累积关联函数为s型时,该模型优于现有的Gompertz模型。修改后的三参数逻辑模型的实际效用是说明使用数据从Cache县研究痴呆症。
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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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